Next Page: 10000

          Lecturer - Data Science - University of Wisconsin- Green Bay - Green Bay, WI      Cache   Translate Page      
System analysis and design; (Currently employed by the University of Wisconsin System). (NOT currently employed by the University of Wisconsin System)....
From University of Wisconsin- Green Bay - Tue, 18 Dec 2018 17:39:58 GMT - View all Green Bay, WI jobs
          Senior Quality Control Auditor, Oncology (Contract, Home-Based) - RSS (R1066660)      Cache   Translate Page      
Position description Join us on our exciting journey IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find better solutions for their patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a wide spectrum of solutions that harness advances in healthcare details, technology, analytics and human ingenuity to drive healthcare forward. ESSENTIAL JOB FUNCTIONS, DUTIES AND RESPONSIBILITIES: Primarily responsible for quality control processes relating to customer deliverables. Provide QC support for the Biostatistics, Clinical Programming, Data Management, Info Technology, SAS programming groups Provide final QC review of all study documents and other client documents (e.g., database QC, Inform QC, data management specifications, SAS output) before they are delivered. Assist with the evaluation of quality control processes. Assure process compliance with all regulatory and Novella SOPs.
          Senior Quality Control Auditor, Oncology (Contract, Home-Based) - RSS (R1066660)      Cache   Translate Page      
Job overview Join us on our exciting journey IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find better solutions for their patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a broad spectrum of solutions that harness advances in healthcare information, technology, analytics and human ingenuity to drive healthcare forward. ESSENTIAL JOB FUNCTIONS, DUTIES AND RESPONSIBILITIES: Primarily responsible for quality control processes relating to customer deliverables. Provide QC support for the Biostatistics, Clinical Programming, Data Management, Info Technology, SAS programming groups Provide final QC review of all study documents and other client documents (e.g., database QC, Inform QC, data management specifications, SAS output) before they are delivered. ist with the evaluation of quality control processes. ure process compliance with all regulatory and Novella SOPs.
          Clinical Data Manager (Office-Based) - RSS (R1066233)      Cache   Translate Page      
Job summary Join us on our exciting journey IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find better solutions for their patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a wide spectrum of solutions that harness advances in healthcare details, technology, analytics and human ingenuity to drive healthcare forward. Client seeking a Clinical Data Manager in the San Francisco, CA area. Position is Office-based Required responsibilities: Outstanding expertise to collect, maintain, validate and manage clinical data Manages data management timelines to coordinate and synchronize deliverables with the overall study timelines Adept at Electronic Data Capture (EDC) system management Strong understanding of regulatory procedures and guidelines Extensive background with Protocol review; SOPs, DOPs, Training Guidelines, Data Management Highly skilled in Electronic Data Capture (EDC) data management
          Clinical Project Manager (Contract, Home-Based) - RSS (R1056737)      Cache   Translate Page      
Position description Join us on our exciting journey IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find better solutions for their patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a broad spectrum of solutions that harness advances in healthcare details, technology, analytics and human ingenuity to drive healthcare forward. Job overview Accountable for all aspects of the management and clinical execution of early phase clinical trials within Translational Medicine (TM). This role leads the planning and implementation of all operational aspects of TM clinical trials from study concept to reporting according to timelines, budget, operational and quality standards (ICH/GCP/SOPs and procedures). Major Accountabilities Clinical Scientist for Phase I/II including multi-country / multi-center trials. The main focus will be on high complexity studies leading to clinical Proof-of-Concept or NDA registration.
          Clinical Study Coordinator, Oncology/Device (Utrecht - Contract) - RSS      Cache   Translate Page      
Position details Join us on our exciting journey IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find better solutions for their patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a wide range of solutions that harness advances in healthcare details, technology, analytics and human ingenuity to drive healthcare forward. Novella Clinical Resourcing, a Quintiles company, is a full service, Global recruitment agency with European headquarters in Stevenage, UK. We are recruiting for a Medical Device client who is looking to support 1 of their important trials in UMC, Utrecht. It is a global registry, Oncology device study and there is a need for a part-time Study Coordinator to support the site. This is an excellent opportunity to gain further Medical Device Clinical trial background and work in a freelance capacity. This opportunity will be a adjustable contract position to work 6-8 hours per week at the UMC Hospital.
          Offer - PRACTICAL SSRS 2017 Online Training @ SQL School - AUSTRIA      Cache   Translate Page      
SQL School is one of the best training institutes for Microsoft SQL Server Developer Training, SQL DBA Training, MSBI Training, Power BI Training, Azure Training, Data Science Training, Python Training, Hadoop Training, Tableau Training, Machine Learning Training, Oracle PL SQL Training. We have been providing Classroom Training, Live-Online Training, On Demand Video Training and Corporate trainings. All our training sessions are COMPLETELY PRACTICAL. SQL Server Reporting Services : Features of our Training: • Completely Practical • Completely Real time • Highly Interactive • Real time Case Studies • Interview Guidance • Certification Guidance • Mock Interviews • Job Support All Sessions are Completely Practical and Realtime. For free SSRS Online Demo, please visit : http://sqlschool.com/SSRS-Online-Training.html Schedules for PRACTICAL SQL 2016 & 2017 SSRS Online TRAINING : http://sqlschool.com/Register.html Contact us today (24 x 7) for SSRS Practical Online Training SQL School Training Institute ISO 9001:2008 Certified Organization for Training Authorized Microsoft Partner (ID# 5108842) India: Mobile: +91 (0) 9666 44 0801 Mobile: +91 (0) 9666 64 0801 USA: Office: +1 (510) 400-4845 Office 1: #101, UMA Residency, Opp: Sindhu Travels, Beside Metro Station Gate #D, SR Nagar, Hyderabad - 38, India. Office 2: #202, Sai Anu Avenue, Street #3, Patrika Nagar, HITECH City, Hyderabad -81, India. Website: http://sqlschool.com/ Follow us: https://www.facebook.com/sequelschool https://www.linkedin.com/company/sql-school https://twitter.com/sequelschool
          Data Scientist - WVU Medicine - Morgantown, WV      Cache   Translate Page      
Skilled at using programing languages such as Java, Python, Oracle, HTML, DHTML, CSS, JSON, HL7. Improves business processes and supports critical business...
From WVU Medicine - Fri, 22 Feb 2019 23:45:36 GMT - View all Morgantown, WV jobs
          Principal Security Data Scientist - Sierra Nevada Corporation - Sparks, NV      Cache   Translate Page      
DATA SCIENCE / MACHINE LEARNING SKILLS:. Sierra Nevada Corporation is an Equal Opportunity Employer. Required to act as a trusted adviser for business leaders...
From Sierra Nevada Corporation - Fri, 15 Feb 2019 23:07:32 GMT - View all Sparks, NV jobs
          Data Scientist - Telenor - Islamabad      Cache   Translate Page      
Why should you join Telenor. At Telenor Pakistan, we give you the opportunity to become a skilled professional in your chosen field of interest....
From Telenor - Thu, 07 Mar 2019 14:48:53 GMT - View all Islamabad jobs
          Manager Applied Analytics & Data Science - Telenor - Islamabad      Cache   Translate Page      
Why should you join Telenor. Apart from this, you will be exposed to how technologies like Big Data are opening new revenue streams for Telenor Pakistan....
From Telenor - Wed, 06 Mar 2019 21:05:45 GMT - View all Islamabad jobs
          Director, Modern Life and Gaming Data Platform - Microsoft - Redmond, WA      Cache   Translate Page      
Cosmos/ADL, Azure-SQL DW). The Engage team (part of Consumer Data and Analytics – CDnA) provides data, technology, operations, data science and analytics...
From Microsoft - Tue, 12 Feb 2019 00:42:24 GMT - View all Redmond, WA jobs
          Solution Architect & Data Scientist, AI/ML with strong front end UI/data visualization experience - Pivotal Consulting - Redmond, WA      Cache   Translate Page      
SQL Server, Azure SQL DB, Azure DW, Azure ML, Analytics Platform Server, HD Insight, Stream Insight, PowerBI, PowerPivot, PowerView, Excel....
From Pivotal Consulting - Tue, 25 Dec 2018 03:35:16 GMT - View all Redmond, WA jobs
          Junior Project Manager - Data & Insights Group - CBS Interactive - Fort Lauderdale, FL      Cache   Translate Page      
The candidate will be a key member of the Data and Insights Group made up of data scientists, business analysts, data engineers and researchers....
From CBS Sports Network - Sat, 02 Mar 2019 02:40:12 GMT - View all Fort Lauderdale, FL jobs
          Statistician Technician - WEST Inc - Cheyenne, WY      Cache   Translate Page      
Experience with other data science toolkits (Python, C#, JavaScript, etc.). Western EcoSystems Technology, Inc.... $18 - $22 an hour
From WEST Inc - Tue, 29 Jan 2019 11:41:19 GMT - View all Cheyenne, WY jobs
          machineByte: Reporter - Machine Learning, machineByte - NY / IL      Cache   Translate Page      
Competitive: machineByte: machineByte, an online news site dedicated to machine learning and data science in finance, is looking for a reporter to cover the Americas region. New York City or Chicago
          IQVIA: *Assistent/in klinische Forschung/ Clinical Trials Assistant / CTA (f/m) - office-based in Rotkreuz      Cache   Translate Page      
IQVIA: Join us on our exciting journey! IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find b Rotkreuz
          Director of Data Science External Alliances - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Janssen Research & Development, LLC. is a division of Johnson & Johnson and is currently recruiting for a Director of Data Science External Alliances. The...
From Johnson & Johnson Family of Companies - Fri, 22 Feb 2019 02:06:45 GMT - View all Spring House, PA jobs
          Senior Director of R&D Data Science Strategy and Innovation - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to other locations (e.g., Raritan, NJ, La Jolla / San Francisco, CA, Boston, MA, and Beerse, Belgium)....
From Johnson & Johnson Family of Companies - Thu, 17 Jan 2019 08:07:48 GMT - View all Spring House, PA jobs
          Senior Director of Data Science Analytics and Insights - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Consideration will be given to other locations (e.g., Raritan, NJ, La Jolla / San Francisco, CA, Boston, MA, and Beerse, Belgium)....
From Johnson & Johnson Family of Companies - Tue, 18 Dec 2018 02:06:37 GMT - View all Spring House, PA jobs
          Lead Data Engineer - Neiman Marcus - Dallas, TX      Cache   Translate Page      
Work with business partners and data science teams to understand business context and craft best-in-class solutions to their toughest problems....
From Neiman Marcus - Fri, 15 Feb 2019 01:35:31 GMT - View all Dallas, TX jobs
          Telecommute Data Science Instructor      Cache   Translate Page      
A career services company has a current position open for a Telecommute Data Science Instructor. Core Responsibilities Include: Teaching two 3-hour classes per week for 11 weeks Meeting the needs and learning styles of your students Guiding students through development of a stellar final project Must meet the following requirements for consideration: Eager to work with the next generation of data scientists At least 2 years of industry experience with data science Fluency in most of the company-required technical topics
          Data Science Developer      Cache   Translate Page      
NJ-Jersey City, Primary Skills: Python, R, Big Data, VLDB, Machine Learning & Statistical Analysis Description: Machine Learning & Data Science best practices Experience on VLDB, multi-structured, big data Statistical Analysis Required: Typically requires a minimum of 7-10+ years as a practitioner of machine learning in Financial Markets. Hands on Machine Learning developer in the Analytics Team for the digital t
          How Data Science Can Boost Customer Experience For Rapid Business Growth      Cache   Translate Page      

While content marketing may still remain the number one means to reach out to the customers on the internet, but in 2019 making use of data science is also the number one way to keep your customers around once you have caught their attention on the web.
Data science which is also synonymous to big data or machine learning helps businesses that are already using easy to use CRM software platforms to develop a kind of customer experience or CX that leave consumers of your offerings satisfied and looking for more.


          Lecturer - Data Science - University of Wisconsin- Green Bay - Green Bay, WI      Cache   Translate Page      
The Austin E. Cofrin School of Business at the University of Wisconsin-Green Bay seeks applicants for a Lecturer position in Data Science and Business...
From University of Wisconsin- Green Bay - Tue, 18 Dec 2018 17:39:58 GMT - View all Green Bay, WI jobs
          Assistant Professor - Data Science & Business Analytics - University of Wisconsin- Green Bay - Green Bay, WI      Cache   Translate Page      
The Austin E. Cofrin School of Business at the University of Wisconsin – Green Bay seeks applicants for a tenure-track position in Data Science and Business...
From University of Wisconsin- Green Bay - Mon, 26 Nov 2018 17:39:30 GMT - View all Green Bay, WI jobs
          Principal Data Scientist - Clockwork Solutions - Austin, TX      Cache   Translate Page      
Support Clockwork’s Business Development efforts. Evaluates simulation analysis output to reveal key insights about unstructured, chaotic, real-world systems....
From Clockwork Solutions - Wed, 26 Dec 2018 10:03:15 GMT - View all Austin, TX jobs
          Lead Data Scientist - Clockwork Solutions - Austin, TX      Cache   Translate Page      
Support Clockwork’s Business Development efforts. Evaluates simulation analysis output to reveal key insights about unstructured, chaotic, real-world systems....
From Clockwork Solutions - Wed, 26 Dec 2018 10:03:14 GMT - View all Austin, TX jobs
          Data Scientist: Medical VoC and Text Analytics Manager - GlaxoSmithKline - Research Triangle Park, NC      Cache   Translate Page      
Strong business acumen; 2+ years of unstructured data analysis/text analytics/natural language processing and/or machine learning application for critical...
From GlaxoSmithKline - Fri, 19 Oct 2018 23:19:12 GMT - View all Research Triangle Park, NC jobs
          Vice President, Data Science - Service Management Group, Inc. - Kansas City, MO      Cache   Translate Page      
Experience building application programming interface (API’s) platforms for business to business integration....
From Service Management Group, Inc. - Thu, 20 Dec 2018 22:45:46 GMT - View all Kansas City, MO jobs
          Data Scientist (Big Data Platform Development) - FedEx Services - Brookfield, WI      Cache   Translate Page      
Women’s Business Enterprise National Council “America’s Top Corporations for Women’s Business Enterprises” - 2016....
From FedEx - Fri, 25 Jan 2019 03:32:44 GMT - View all Brookfield, WI jobs
          Senior Data Scientist - BOEING - Bellevue, WA      Cache   Translate Page      
“US Person” includes US Citizen, lawful permanent resident, refugee, or asylee. Leads development of data analysis goals to meet business objectives and...
From Boeing - Wed, 27 Feb 2019 00:16:26 GMT - View all Bellevue, WA jobs
          Data Scientist - WVU Medicine - Morgantown, WV      Cache   Translate Page      
Certification in Oracle or SQL Development. Skilled at using programing languages such as Java, Python, Oracle, HTML, DHTML, CSS, JSON, HL7. JOB TITLE & CODE:....
From WVU Medicine - Fri, 22 Feb 2019 23:45:36 GMT - View all Morgantown, WV jobs
          (USA-TX-COPPELL) App Programmer - Technical Solution Specialist (Graduate Co-op)      Cache   Translate Page      
**Job Description** This is a supplemental co-op position for an application programmer within the IBM Global Solution Center in Coppell, Texas. The Global Solution Center is a world-class client center where teams of Industry Solution Architects and Specialists bring IBM together to accelerate solutions to help clients innovate and address their most challenging business problems. This person will work with team members and clients to create high quality proofs-of-concept and generate technical sales assets for the progression or close of solution sales opportunities. Key Responsibilities: + Actualize complex systems of engagement with in-depth product-, technology-, or industry-related specialized skills + Leverage IBM Cloud Platform as a Service developer platform services for composition and aggregation + Develop mobile applications on iOS and Android platforms + Leverage and exploit IBM’s extensive data analytics capabilities, including data science and data management aspects Key Skills: + Demonstrated application design skills + Demonstrated application programming skills + Demonstrated knowledge of data analytics discipline, including data science and data management aspects + Knowledge of contemporary programming environment / skills [e.g., REST APIs, Node-JS, GitHub, Slack, Mongo, Express, Angular, Node (MEAN stack), HTML/HTML5, Java/Javascript, Python] + Knowledge of IBM Cloud platform or other cloud environment + IDE familiarity and usage + Knowledge of IBM Cloud platform or other cloud environment + Code repository usage, build system (i.e. webpack), deployment (IBM Cloud/heroku, docker) + Client application frameworks, such as IBM MobileFirst + Knowledge of current industry trends + Familiar with Agile techniques + Strong communication skills, works well in teams, and quickly adapts to new technologies Other Requirements: + This co-op role will work in the Coppell, TX IBM office. You should reside in the Dallas-Fort Worth metro area. + You must be actively pursuing a graduate degree, preferably in Computer Science or related area. + This is a full-time role from mid-May until late-August 2019. There is potential to continue employment afterward, part-time (min. 20hr/wk) during school and full-time during school breaks. + IBM will not be providing visa sponsorship for these positions, now or in the future. Therefore, in order to be considered, you must have the ability to work without a need for current or future employment-based visa sponsorship. **Required Technical and Professional Expertise** + At least 6 months' experience in designing / creating IT solutions + At least 6 months' experience in analyzing client needs, requirements and expectations + At least 1 year's experience in written, verbal and visual communications skills **Preferred Tech and Prof Experience** + At least 6 months' experience with contemporary programming skills [e.g., REST APIs, Node-JS, GitHub, Slack, Mongo, Express, Angular, Node (MEAN stack), HTML/HTML5, Java/Javascript, Python] + At least 6 months' experience with IBM Bluemix or other cloud platform **EO Statement** IBM is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.
          Senior Data Scientist - Cray - Seattle, WA      Cache   Translate Page      
Who is Cray? Our business is supercomputing. Working closely with other internal teams to integrate machine learning into existing product offerings using...
From Cray - Wed, 23 Jan 2019 08:18:39 GMT - View all Seattle, WA jobs
          Manager, Data Science - Micron - Boise, ID      Cache   Translate Page      
Create server based visualization applications that use machine learning and predictive analytic to bring new insights and solution to the business....
From Micron - Fri, 30 Nov 2018 00:47:34 GMT - View all Boise, ID jobs
          Senior Data Scientist - Predictive Enterprise Group - Neudesic LLC - Philadelphia, PA      Cache   Translate Page      
Machine Learning Solutions:. The explosion of big data, machine learning and cloud computing power creates an opportunity to make a quantum leap forward in...
From Neudesic LLC - Sat, 15 Dec 2018 21:58:12 GMT - View all Philadelphia, PA jobs
          All-Cloud vs. On-Premises vs. Hybrid: How 3 Businesses Chose the Right Infrastructure      Cache   Translate Page      
All-Cloud vs. On-Premises vs. Hybrid: How 3 Businesses Chose the Right Infrastructure juliet.vanwage… Mon, 03/11/2019 - 10:21

Carvana is not a run-of-the-mill ­ used-car dealership: Its unique method of selling and distributing vehicles nationwide is disrupting the industry. So it should come as no surprise that the company deploys cutting-edge technology and IT infrastructure.

Shoppers can peruse cars online, see a 360-degree view of vehicles through special camera technology, arrange financing and finalize the sale — all within a few minutes and without a salesperson’s help. Buyers can receive their cars at home or visit one of Carvana’s signature, fully automated car vending machines, where they plop in giant coins and watch the machine dispense their new ride.

To provide the best customer service possible and ensure 24/7 uptime, Imran Kazi, Carvana’s senior director of technology services, has taken a hybrid approach. He’s deployed a small footprint of Nutanix hyperconverged infrastructure at the company’s headquarters in Tempe, Ariz., for testing and ­development and at car inspection ­centers for local compute and storage needs. But the company’s website, ­applications and data are all hosted in the public cloud.

“We don’t want to reinvent the wheel by building a big data center infrastructure, then worrying about depreciating and failing hardware,” Kazi says. “We can focus on implementing and supporting the right software solutions and tools for our employees, which helps create better experiences for our customers.”

Carvana

Carvana's waiting room at their Tempe, Ariz., facility. Photo: Jim David.

More businesses and nonprofits are moving their infrastructure to the cloud, now that it’s proven to be secure, reliable and cost-effective. But while cloud service providers can improve efficiencies and provide benefits such as redundancy and the ability to scale up and down quickly, the cloud is not always the best choice or necessarily the least expensive.

In some cases, it’s less costly to retain applications and data in-house, particularly if organizations have legacy or custom applications that are not easily migrated to the cloud. And companies concerned about latency or that have to comply with data regulations may need to keep workloads on-premises.

Some organizations have gone all-in on the public cloud. Some are sticking with traditional on-premises infrastructure or a private cloud, while others are using a mix. Analysts say companies and nonprofits have to decide what’s right for their unique requirements.

“Some see hybrid as a step on the way to the cloud,” says Frank Della Rosa, IDC’s research director for SaaS and Cloud. “Workloads sitting on-premises can eventually be moved, but some ­businesses still deal with latency and bandwidth concerns or data sovereignty and privacy issues. Those factors ­determine whether a workload is suitable for the public cloud, on-premises or private cloud.”

SEE MORE: Get help sorting through the dizzying array of cloud and on-premises computing options.

1. Hybrid Infrastructure Delivers Flexibility for Carvana

When Kazi joined Carvana in 2016, he had the opportunity to build the company’s IT infrastructure from scratch. At the time, the online auto retailer had spun off from its former parent company, but its applications and data still resided in the former parent’s data centers.

Kazi migrated customer-facing and business applications, such as customer relationship management and enterprise resource planning software, across several cloud providers, including Microsoft Azure. He also adopted Software as a Service offerings: RingCentral for unified communications and Google G Suite for email and collaboration.

Carvana

Cars stacked in Carvana's vending machine at the Tempe, Ariz., facility. Photo: Jim David.

He chose the commercial cloud for three primary benefits: It saves money. It provides the company the agility it needs for growth. And instead of having to manage infrastructure in the production environment, the cloud frees up IT staff for more strategic needs.

Kazi also built a private cloud across two data centers using Nutanix’s HCI appliances, which combine servers, storage, networking and virtualization into a small-footprint appliance.

These unified systems, which run on Nutanix’s license-free AHV hypervisor, are more energy efficient, easier to ­manage and easier to scale than traditional hardware. If the environment reaches capacity, Kazi can purchase new appliances and quickly configure them with Nutanix’s management software.

“We can easily expand as we grow and have more projects,” he says.

Since Kazi’s arrival, Carvana has grown rapidly nationwide, expanding from 11 cities to 96. It runs about 350 virtual machines across 25 clusters of Nutanix appliances. Data scientists, ­analysts and developers use them as a test and staging area for new applications and algorithms. The company uses analytics to understand customer preferences, discover new markets to enter and drive logistical efficiencies.

Through the private cloud, employees can fully test out applications and algorithms before deploying them in production in the public cloud. Having full control of in-house infrastructure aids that effort.

“We can understand every nuance of the software and what resources it takes, and then we figure out the best cloud platform to put it into production,” Kazi says.

The company also installs a Nutanix appliance in each of its car inspection centers, where 360-degree photo ­technology takes data-intensive pictures of the cars. “We need the infrastructure locally to upload the photos to the cloud,” Kazi says.

Carvana car vending machine coin slot

At Carvana’s unique vending machines, buyers deposit special coins to retrieve the cars they purchase. Photo: Jim David.

2. The Cloud Saves BARBRI Time and Money

The BARBRI Group, a Dallas-based legal education company that provides online prep courses for bar exams, has saved hundreds of thousands of ­dollars annually by shutting down its data centers and moving fully to Microsoft Azure.

About three years ago, the company had two redundant data centers in colocation facilities, but the IT infrastructure was reaching its end of life. Faced with a massive IT investment, the company went with the cloud because it was more cost-effective and efficient, says IT Director Mark Kaplan.

One reason is the scalability that the cloud offers to a seasonal business like BARBRI, which has its busy seasons in summer and winter. Instead of internal data centers running full-throttle year-round, Azure allows the company to spin up more resources during the demanding months and scale down during slow periods, Kaplan says.

It’s a huge financial savings,” he says. “We didn’t have to do a huge capital outlay, and then we ended up finding out that it’s just easier to manage.”

Cloud

Source: RightScale, “2018 State of the Cloud Report,” February 2018

BARBRI migrated to the cloud gradually. In 2016, Kaplan shut down one data center and operated half on-prem and half in the cloud. The company migrated to Azure’s Database as a Service offering, then it moved 250 applications.

“It allowed us to get used to working in the cloud, and if it failed, we could cut back to on-premises,” he explains.

The cloud effort was a success, however. So, in January 2017, Kaplan shut down the remaining data center and went all-cloud. The company uses Azure Backup for its virtual machines and changed from an expensive multiprotocol label-switching network to a ­software-defined WAN using Cisco Meraki switches. Microsoft manages the infrastructure and databases, while the BARBRI IT staff manages its own ­software and handles OS upgrades. The freed-up time allows the IT staff to pursue new projects, such as using machine learning to build online courses that adapt to students’ needs in real time.

Modern-Workforce_the-office.jpg

3. Detroit Symphony Orchestra Sees the Advantages of HCI

At the Detroit Symphony Orchestra, Jody Harper knew the organization’s three aging servers were near the end of life, but as a nonprofit with a tight budget, it hoped to prolong their life span as much as possible. In late 2017, however, the servers crashed, knocking out the symphony’s website and preventing its 75 full-time employees from working for a full day.

Harper got the 8-year-old servers running again, but it was just a temporary fix. He needed a long-term solution — and fast.

“If no one can buy tickets from our website, that’s money lost,” says Harper, senior director of technology and infrastructure.

Fortunately, he had done his homework and researched his options. Moving to the cloud was not realistic because it would require too much time and money to redesign the custom integration that exists between the symphony’s two most critical applications — its website and CRM software — for the cloud.

Instead, Harper wanted hyperconverged equipment. In the ensuing four weeks, he purchased and deployed three Scale Computing hyperconverged appliances because of their affordability and ease of use.

“For a nonprofit, cost is, hands-down, a major factor,” he says. “It’s also simple and easy. I can spin up a new server in 10 clicks.”

Today, 25 virtual servers run on the Scale equipment, including financial and event management software, databases and file and print servers.

A hybrid cloud approach is still a future option, however. To improve disaster recovery, Harper is considering using the public cloud as a backup site. In the meantime, he recently purchased a fourth Scale appliance to replicate data in a separate location.

“It’s a reliable product. It’s made our lives easier, and now we have a fourth one for disaster purposes,” he says.


          MARKETING MANAGER (M/W/D) MIT FOKUS AUF BRAND & COMMUNICATIONS // StackFuel GmbH      Cache   Translate Page      

MARKETING MANAGER (M/W/D) MIT FOKUS AUF BRAND & COMMUNICATIONS Über uns In den letzten drei Jahren hat sich StackFuel zu einem der führenden innovativen Anbieter für praxisnahe Online-Trainings im Bereich Data Analytics und Data Science in Deutschland entwickelt. Im Zuge der digitalen Transformation unterstützen wir Konzerne, mittelständische Unternehmen und Start-ups dabei effektives, datengetriebenes Arbeiten in...

Check out all open positions at http://BerlinStartupJobs.com


          Showcasing Diversity in Tech: Women in Data Science      Cache   Translate Page      
Last week, more than 150 technology leaders convened at the SAP campus in Palo Alto, California, to talk about diversity, technology, and business development at...
          Senior Data Scientist (m/w/d)      Cache   Translate Page      
Anbieter: nicht oeffentlich
Senior Data Scientist (m/w/d) Kurze Wege, flache Hierarchien, eine...
Von: 13.03.2019 04:07 · Ort: D-47051 Duisburg, Nordrhein-Westfalen
Diese Stellenanzeige Nr. 1.384.564.035
: ansehen · merken · weiterempfehlen

          Senior Account Manager      Cache   Translate Page      

This is a fantastic opportunity for a switched-on Senior Account Manager to join a boutique digitally-focused, brand and marketing agency. They offer their clients a unique blend of data science to assist with customer experience insights and deliver end-to-end customer service from planning and strategy through to implementation and ongoing optimisation. Reporting to the Account […]

The post Senior Account Manager appeared first on Mumbrella.


          Senior Account Manager      Cache   Translate Page      

This is a fantastic opportunity for a switched-on Senior Account Manager to join a boutique digitally-focused, brand and marketing agency. They offer their clients a unique blend of data science to assist with customer experience insights and deliver end-to-end customer service from planning and strategy through to implementation and ongoing optimisation. Reporting to the Account […]

The post Senior Account Manager appeared first on Mumbrella.


          Post-Doctoral Associate in Computer Science      Cache   Translate Page      

Post-Doctoral Associate in Computer Science
Research Group of Dr. Talal Rahwan
NYU Abu Dhabi

We are seeking researchers who are passionate about Data Science and Computational Social Science to join Dr. Talal Rahwan’s lab. Some of the research questions that excite us as a group include topics in: (i) network science, (ii) ethics of AI and automation; (iii) social media analysis (iv) collaboration and team work, (v) online controlled experiments.

The ideal candidate is self-motivated …


          DATA SCIENTIST - TELECOMMUNICATIONS - TransUnion - Chicago, IL      Cache   Translate Page      
You will partner with internal and external cross-functional teams to drive new business initiatives and deliver long term value-added product propositions for...
From TransUnion - Thu, 27 Sep 2018 15:13:09 GMT - View all Chicago, IL jobs
          Sr. Data Scientist - Worldwide Public Sector Team - Amazon Web Services, Inc. - Herndon, VA      Cache   Translate Page      
Travel for face-to-face customer engagements, internal conferences, and industry events. Machine learning has been strategic to Amazon from the early years....
From Amazon.com - Tue, 08 Jan 2019 09:37:16 GMT - View all Herndon, VA jobs
          Director, Modern Life and Gaming Data Platform - Microsoft - Redmond, WA      Cache   Translate Page      
Cosmos/ADL, Azure-SQL DW). The Engage team (part of Consumer Data and Analytics – CDnA) provides data, technology, operations, data science and analytics...
From Microsoft - Tue, 12 Feb 2019 00:42:24 GMT - View all Redmond, WA jobs
          Solution Architect & Data Scientist, AI/ML with strong front end UI/data visualization experience - Pivotal Consulting - Redmond, WA      Cache   Translate Page      
SQL Server, Azure SQL DB, Azure DW, Azure ML, Analytics Platform Server, HD Insight, Stream Insight, PowerBI, PowerPivot, PowerView, Excel....
From Pivotal Consulting - Tue, 25 Dec 2018 03:35:16 GMT - View all Redmond, WA jobs
          Data Scientist - Brightstar - Montréal, QC      Cache   Translate Page      
Next Wireless Group is looking for a Data Scientist to analyze large amounts of raw information to find patterns that will help improve our company. We will...
From Brightstar - Thu, 21 Feb 2019 19:19:30 GMT - View all Montréal, QC jobs
          Data Scientist - Brightstar Wireless - Montréal, QC      Cache   Translate Page      
As the first "data guru" of the company you will have to build the right tools to display all the KPIs and reports needed....
From Brightstar Wireless - Thu, 21 Feb 2019 16:21:19 GMT - View all Montréal, QC jobs
          Data Scientist - Brightstar Corp. - Montréal, QC      Cache   Translate Page      
Next Wireless Group is looking for a Data Scientist to analyze large amounts of raw information to find patterns that will help improve our company. We will...
From Brightstar Corp. - Wed, 20 Feb 2019 22:18:20 GMT - View all Montréal, QC jobs
          Lead Data Engineer - Neiman Marcus - Dallas, TX      Cache   Translate Page      
Work with business partners and data science teams to understand business context and craft best-in-class solutions to their toughest problems....
From Neiman Marcus - Fri, 15 Feb 2019 01:35:31 GMT - View all Dallas, TX jobs
          Scientifique de données -Analytique d'affaires -Data Scientist - Aviva - Montréal, QC      Cache   Translate Page      
Ce que vous serez appelé(e) à faire Joignez-vous à une équipe d’actuaires et de scientifiques et d’ingénieurs de données passionnés, chargée de mettre les...
From Aviva - Thu, 25 Oct 2018 21:02:22 GMT - View all Montréal, QC jobs
          Executive Director/Principal Data Scientist - USA-VA-Herndon      Cache   Translate Page      
This position will report to the Group CTO and be part of the technology office staff. You will apply software development skills, strong analytical and innovative thinking, AI/ML experience, quickly ...
          Lead QA Engineer-61513H      Cache   Translate Page      
GA-Alpharetta, Unlock Your Career Potential: Technology at ADP. Do you enjoy exploring, identifying and inspiring the future of the workplace and the lives of millions of people? At ADP, the world's largest B2B cloud company, our Technology team is comprised of brilliant engineers, architects, data scientists, infrastructure experts, and more. We were first in our industry to offer a SaaS solution and continue t
          Statistician Technician - WEST Inc - Cheyenne, WY      Cache   Translate Page      
Experience with other data science toolkits (Python, C#, JavaScript, etc.). Western EcoSystems Technology, Inc.... $18 - $22 an hour
From WEST Inc - Tue, 29 Jan 2019 11:41:19 GMT - View all Cheyenne, WY jobs
          Lead QA Engineer-61513H      Cache   Translate Page      
GA-Alpharetta, Unlock Your Career Potential: Technology at ADP. Do you enjoy exploring, identifying and inspiring the future of the workplace and the lives of millions of people? At ADP, the world's largest B2B cloud company, our Technology team is comprised of brilliant engineers, architects, data scientists, infrastructure experts, and more. We were first in our industry to offer a SaaS solution and continue t
          Operations Research Scientist, Data Science - Convoy - Seattle, WA      Cache   Translate Page      
Today, we use machine learning and economic analysis to figure out freight prices, shipment relevance for carriers, auction bidding strategy, and other internal...
From Convoy - Fri, 11 Jan 2019 04:12:37 GMT - View all Seattle, WA jobs
          Senior Data Engineer, Data Science - Convoy - Seattle, WA      Cache   Translate Page      
Data engineering, database engineering, business intelligence or business analytics. Today, we use machine learning to figure out freight prices, shipment...
From Convoy - Tue, 27 Nov 2018 22:12:32 GMT - View all Seattle, WA jobs
          Stitch Fix Attributes 3 Million Active Customers to Data Science Investment      Cache   Translate Page      

The online styling service has reported 25.1 percent YOY growth for Q2 FY2019, claiming its investment in category expansion, technology, marketing and above all, data science, is to thank for the growth.

The post Stitch Fix Attributes 3 Million Active Customers to Data Science Investment appeared first on Power Retail.


          Director of Data Science External Alliances - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Janssen Research & Development, LLC. is a division of Johnson & Johnson and is currently recruiting for a Director of Data Science External Alliances. The...
From Johnson & Johnson Family of Companies - Fri, 22 Feb 2019 02:06:45 GMT - View all Spring House, PA jobs
          022: Data Science Lead - Freight Brokerage - Dataspace - Green Bay, WI      Cache   Translate Page      
NoSQL databases such as MongoDB or Cassandra. Our client, one of the nation’s top transportation and logistics enterprises, has asked us to provide them with a... $150,000 a year
From Dataspace - Fri, 11 Jan 2019 18:05:23 GMT - View all Green Bay, WI jobs
          Data Scientist Lead - Schneider National - Green Bay, WI      Cache   Translate Page      
Experience with machine learning software (e.g., R, Python, SPSS, SAS), data access/manipulation (e.g., SQL, pandas, dplyr) and NoSQL databases (e.g., MongoDB,...
From Schneider National - Thu, 03 Jan 2019 06:22:26 GMT - View all Green Bay, WI jobs
          Data Scientist Lead - Schneider - Green Bay, WI      Cache   Translate Page      
Experience with machine learning software (e.g., R, Python, SPSS, SAS), data access/manipulation (e.g., SQL, pandas, dplyr) and NoSQL databases (e.g., MongoDB,...
From Schneider - Wed, 02 Jan 2019 23:36:22 GMT - View all Green Bay, WI jobs
          Data Scientist - WVU Medicine - Morgantown, WV      Cache   Translate Page      
Certification in Oracle or SQL Development. Skilled at using programing languages such as Java, Python, Oracle, HTML, DHTML, CSS, JSON, HL7. JOB TITLE & CODE:....
From WVU Medicine - Fri, 22 Feb 2019 23:45:36 GMT - View all Morgantown, WV jobs
          Data Science: Deep Learning in Python (Updated)      Cache   Translate Page      
Data Science: Deep Learning in Python (Updated)#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000
Data Science: Deep Learning in Python (Updated)
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 48000 Hz, 2ch | 1.43 GB
Duration: 9.5 hours | Genre: eLearning Video | Language: English

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow.

Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)


          Bayesian Machine Learning in Python: A/B Testing      Cache   Translate Page      
Bayesian Machine Learning in Python: A/B Testing#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000
Bayesian Machine Learning in Python: A/B Testing
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 48000 Hz, 2ch | 853 MB
Duration: 5.5 hours | Genre: eLearning Video | Language: English

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More.

Use adaptive algorithms to improve A/B testing performance

Apply Bayesian methods to A/B testing


          Scientists share plans for planetwide biodiversity census      Cache   Translate Page      
Washington (UPI) Mar 11, 2019
Biologists and data scientists have developed a plan for tracking the numbers and locations of the planet's millions of plant and animal species - a global biodiversity census of sorts. The new bio-tracking plan - published this week in the journal Nature Ecology and Evolution - features new strategies for collecting, organizing and translating massive amounts of biodiversity data fo
          Beyond Indica & Sativa: The Future of Cannabis Strains Waits for You at SXSW      Cache   Translate Page      

Today, you likely choose a strain based on its indica or sativa designation. But cannabis is more complex than that. Join Leafly's principal data scientist, Nick Jikomes, this Friday at SXSW to learn what the future holds for strain organization.

The post Beyond Indica & Sativa: The Future of Cannabis Strains Waits for You at SXSW appeared first on Leafly.


          Customer Facing Data Scientist - DataRobot - São Paulo      Cache   Translate Page      
In some cases, executing data science workflows for customers. Customer Facing Data Scientists (CFDSs) are critical to making our customers successful....
De DataRobot - Thu, 07 Mar 2019 20:49:43 GMT - Visualizar todas as empregos: São Paulo
          Senior Director of R&D Data Science Platforms - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Assess the financial implications and business case for platforms and help communicate overarching return on investment, business value drivers, success metrics...
From Johnson & Johnson Family of Companies - Thu, 22 Nov 2018 11:16:26 GMT - View all Spring House, PA jobs
          Principal Data Scientist - Johnson & Johnson Family of Companies - Raritan, NJ      Cache   Translate Page      
The primary location for this position is Spring House, PA or Raritan, NJ. Experience in developing presentations and communications to be shared with internal...
From Johnson & Johnson Family of Companies - Thu, 22 Nov 2018 11:16:26 GMT - View all Raritan, NJ jobs
          Senior Scientist, Data Science Strategy and Innovation - Johnson & Johnson Family of Companies - Titusville, NJ      Cache   Translate Page      
Consideration will be given to other locations (e.g., Raritan, NJ; Your proven social skills establish relationships, you act as a change agent, and adapt to...
From Johnson & Johnson Family of Companies - Thu, 07 Mar 2019 14:06:40 GMT - View all Titusville, NJ jobs
          Data Engineer, Product Line Owner Data Management - Johnson & Johnson Family of Companies - Raritan, NJ      Cache   Translate Page      
Pharma Industry and domain expertise, technical knowledge of data science platforms and software technology, knowledge of business process and business strategy...
From Johnson & Johnson Family of Companies - Wed, 23 Jan 2019 08:07:32 GMT - View all Raritan, NJ jobs
          Data Scientist - Oliver Wyman - New York, NY      Cache   Translate Page      
Demonstrate solid and battle-tested understanding of the standard canon of machine learning practices, including but not limited to:....
From Marsh & McLennan Companies - Sat, 05 Jan 2019 15:04:15 GMT - View all New York, NY jobs
          PL SQL Practical Live Online Training       Cache   Translate Page      
SQL School is one of the best training institutes for Microsoft SQL Server Developer Training, SQL DBA Training, MSBI Training, Power BI Training, Azure Training, Data Science Training, Python Training, Hadoop Training, Tableau Training, Machine Learning ...
          Best Project Oriented Online Training On Power BI       Cache   Translate Page      
SQL School is one of the best training institutes for Microsoft SQL Server Developer Training, SQL DBA Training, MSBI Training, Power BI Training, Azure Training, Data Science Training, Python Training, Hadoop Training, Tableau Training, Machine Learning ...
          Comment on Data Scientist Resume | Data Scientist Jobs, Salary & Skills | Data Science Training | Edureka by edureka!      Cache   Translate Page      
Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Data Science Training and Certification, Visit our Website: <a href="http://bit.ly/2r6btSL">http://bit.ly/2r6btSL</a>
          Data Scientist      Cache   Translate Page      
NJ-Weehawken, Our client, a large global Investment Bank, is looking to hire Data Scientist for contract role. Data Scientist Role: Data Scientist Location: Weehawken, New Jersey Duration: 6 months Rate: up to $100 / Hour Your New Company An established global Investment Bank with presence in Weehawken, New Jersey. You will be working with global teams, with the chance to add a prestigious, known name to your r
          Data scientist - TMC - People Drive Technology - Longueuil, QC      Cache   Translate Page      
Python (Sci-kit Learn, numpy, pandas, Tensorflow, Keras) Matlab, SQL; | JOIN tmc North america:....
From Indeed - Wed, 06 Mar 2019 20:45:02 GMT - View all Longueuil, QC jobs
          Data Science Internship / Stagiaire en science des données - Expedia - Montréal, QC      Cache   Translate Page      
Expedia Data Science- Intern Are you passionate about the technology needed to drive a multi-billion dollar business? Do you love building creative, highly...
From Expedia - Thu, 28 Feb 2019 22:41:27 GMT - View all Montréal, QC jobs
          Senior Data Scientist - Visualization and Optimization - TMC - People Drive Technology - Montréal, QC      Cache   Translate Page      
Python (Sci-kit Learn, numpy, pandas, Tensorflow, Keras), R, Matlab, SQL; | JOIN tmc North america:....
From Indeed - Wed, 20 Feb 2019 18:51:12 GMT - View all Montréal, QC jobs
          Data Scientist - Amp Me Inc. - Montréal, QC      Cache   Translate Page      
We're looking forCore SkillsData Data Analysis Google Analytics Mathematical Modeling Mathematics Microsoft Excel Panda Python SQL....
From Amp Me Inc. - Mon, 04 Feb 2019 20:22:58 GMT - View all Montréal, QC jobs
          Senior Data Scientist - Guavus - Montréal, QC      Cache   Translate Page      
Pandas, scikit-learn, Jupyter, Anaconda, spark-ml, etc. Guavus provides dynamic solutions for data-rich businesses, so that they can gain a competitive...
From Guavus - Thu, 10 Jan 2019 01:31:26 GMT - View all Montréal, QC jobs
          Data Scientist - Guavus - Montréal, QC      Cache   Translate Page      
Pandas, scikit-learn, Jupyter, Anaconda, spark-ml, etc. Guavus provides dynamic solutions for data-rich businesses, so that they can gain a competitive...
From Guavus - Thu, 10 Jan 2019 01:31:26 GMT - View all Montréal, QC jobs
          Data Scientist - Stradigi AI - Montréal, QC      Cache   Translate Page      
Comfort using Scikit learn, Numpy, Pandas; Do you want to join a leading artificial intelligence solutions provider?...
From Stradigi AI - Fri, 04 Jan 2019 20:12:19 GMT - View all Montréal, QC jobs
          Senior Data Scientist / Machine Learning Engineer - PubMatic - Montréal, QC      Cache   Translate Page      
Job Description We are looking for a strong Data Scientist or Machine Learning Engineer - a proven 'doer' to develop, implement and extend data-intensive...
From PubMatic - Tue, 04 Dec 2018 04:08:14 GMT - View all Montréal, QC jobs
          Data Scientist / Scientifique des Données - Terminal - Montréal, QC      Cache   Translate Page      
Development experience using numpy, pandas, scikit-learn, tensorflow... Expérience en développement à l’aide de numpy, pandas, scikit-learn, tensorflow......
From Terminal - Sat, 10 Nov 2018 01:01:14 GMT - View all Montréal, QC jobs
          Data Scientist - Oliver Wyman - New York, NY      Cache   Translate Page      
Demonstrate solid and battle-tested understanding of the standard canon of machine learning practices, including but not limited to:....
From Marsh & McLennan Companies - Sat, 05 Jan 2019 15:04:15 GMT - View all New York, NY jobs
          Senior Data Scientist - Mischief - San Francisco, CA      Cache   Translate Page      
Familiarity with experimental design and A/B testing. We do this through intelligent design and machine learning applications that looks to tie together an...
From Mischief - Mon, 21 Jan 2019 19:50:03 GMT - View all San Francisco, CA jobs
          AI For Everyone: What Andrew Ng wants to convey with this Non Technical Course in 30 points.      Cache   Translate Page      
Harveen Singh, Towards Data Science AI for everyone is a non technical course taking which you will have greater knowledge than most CEO’s in the world. At least this is what Andrew Ng claims. So let’s find out in short what he wants to convey. https://towardsdatascience.com/ai-for-everyone-what-andrew-ng-want-to-convey-with-this-non-technical-course-in-30-points-bedaea57c81b Share on Facebook
          Data Science - Al Manal Training Center , United Arab Emirates, Abu Dhabi,Abu Dhabi       Cache   Translate Page      
Data Science Training:

Data Science deals with leveraging the data, which is extracted for making some good decisions in a ;
Usually data is in structured and unstructured format and data analysts leverage their skills in maths, programming, 
and statistics to organize and clean the ;
So, to be proficient in it, get Data Science course in Abu Dhabi at Al Manal training institute.


Course Outline:


Data Science:
Statistics
Probability
Linear Algebra
Calculus
Python Programming
Introduction to Data Science
Data Cleaning using python
Data Visualization using python
Data Modelling using python
Machine Learning using python

Cost: 3800 AED

Duration: Upto 40 Hours


          Oil, Gas & Chemicals Data Science Manager - Deloitte - McLean, VA      Cache   Translate Page      
Storm, Spark, Flume, HDFS (Cloudera, Hortonworks, MapR, IBM Big Insights, Pivotal HD), PIG, HIVE, Scala, HCatalog, Ambari tied to mixed workload processing for...
From Deloitte - Fri, 04 Jan 2019 04:53:39 GMT - View all McLean, VA jobs
          How Random Can You Be?      Cache   Translate Page      

If you want to gather data on human nature, the easiest way is to make an online game about it. Data science Ilya Perederiy wants to know if humans can be random on purpose (spoiler: no). In the online game, all you do is press the left and right buttons on your keyboard (or you can touch icons on a touchscreen). Your goal is to fool the algorithm that is predicting your next move based on your previous moves. You can keep up with your progress with the virtual money fund on the left- you get money each time the algorithm is wrong, and you lose money when it correctly predicted your move. There is no clock, so take your time. On the graph I generated, you can see exactly where I gave up trying and started pushing buttons very fast. I kept the algorithm under 50% until about guess #275. Good luck!  -via Digg


          Data Scientist, Lead - Booz Allen Hamilton - Washington, DC      Cache   Translate Page      
Experience in supporting business development and capture activities, including white paper development, and proposals....
From Booz Allen Hamilton - Tue, 26 Feb 2019 20:29:42 GMT - View all Washington, DC jobs
          Java Developer - SkipTheDishes - Saskatoon, SK      Cache   Translate Page      
Exceptional knowledge of Java, especially Java 8. Think you have what it takes to join an elite team of software developers, engineers, and data scientists?...
From SkipTheDishes - Wed, 13 Feb 2019 17:14:45 GMT - View all Saskatoon, SK jobs
          Marketing Manager      Cache   Translate Page      
CA-San Francisco, job summary: Position Description:company Cloud Developer Marketing Project Manager - We bring Company's most advanced technologies to millions of developers, architects, data scientists, and other technical practitioners across a variety of enterprises, startups, universities, and communities. We grow developer adoption of Company Cloud Product through a variety of programs, experiences, and chan
          022: Data Science Lead - Freight Brokerage - Dataspace - Green Bay, WI      Cache   Translate Page      
NoSQL databases such as MongoDB or Cassandra. Our client, one of the nation’s top transportation and logistics enterprises, has asked us to provide them with a... $150,000 a year
From Dataspace - Fri, 11 Jan 2019 18:05:23 GMT - View all Green Bay, WI jobs
          Data Scientist Lead - Schneider National - Green Bay, WI      Cache   Translate Page      
Experience with machine learning software (e.g., R, Python, SPSS, SAS), data access/manipulation (e.g., SQL, pandas, dplyr) and NoSQL databases (e.g., MongoDB,...
From Schneider National - Thu, 03 Jan 2019 06:22:26 GMT - View all Green Bay, WI jobs
          Data Scientist Lead - Schneider - Green Bay, WI      Cache   Translate Page      
Experience with machine learning software (e.g., R, Python, SPSS, SAS), data access/manipulation (e.g., SQL, pandas, dplyr) and NoSQL databases (e.g., MongoDB,...
From Schneider - Wed, 02 Jan 2019 23:36:22 GMT - View all Green Bay, WI jobs
          cityU data science phd还有多久发      Cache   Translate Page      
问了DS的小秘说3她从研究生院了解到3院都会发。 但是研究生院告诉我我的application到4月底才审核。 现在和城大导师match的还可以,导师说放心只是行政审核麻烦。 美国offer 4月1前必须给回复,有没有小伙伴知道4月前能不能收到啊啊啊,DS的小伙伴都有很多收到了
          Beware the data science pin factory: The power of the full-stack data science generalist and the perils of division of labor through function      Cache   Translate Page      

assembly line
[This is a slightly modified version of the article I wrote for HBR originally published here].

In The Wealth of Nations, Adam Smith demonstrates how the division of labor is the chief source of productivity gains using the vivid example of a pin factory assembly line: “One [person]1 draws out the wire, another straights it, a third cuts it, a fourth points it, a fifth grinds it.” With specialization oriented around function, each worker becomes highly skilled in a narrow task leading to process efficiencies. Output per worker increases many fold; the factory becomes extremely efficient at producing pins.

This division of labor by function is so ingrained in us even today that we are quick to organize our teams accordingly. Data science is no exception. An end-to-end algorithmic business capability requires many data functions and companies usually create teams of specialists: research scientist, data engineers, machine learning engineers, causal inference scientists, and so on. Specialists’ work is coordinated by a product manager, with hand-offs between the functions in a manner resembling the pin factory: “one person sources the data, another models it, a third implements it, a fourth measures it” and on and on.

Alas, we should not be optimizing our data science teams for productivity gains; that is what you do when you know what it is you’re producing—pins or otherwise—and are merely seeking incremental efficiencies. The goal of assembly lines is execution. We know exactly what we want—pins in Smith’s example, but one can think of any product or service in which the requirements fully describe all aspects of the product and its behavior. The role of the workers is then to execute on those requirements as efficiently as possible.

But the goal of data science is not to execute. Rather, the goal is to learn and develop profound new business capabilities. Algorithmic products and services like recommendations systems, client engagement bandits, style preference classification, size matching, fashion design systems, logistics optimizers, seasonal trend detection, and more can’t be designed up-front. They need to be learned. There are no blueprints to follow; these are novel capabilities with inherent uncertainty. Coefficients, models, model types, hyper parameters, all the elements you’ll need must be learned through experimentation, trial and error, and iteration. With pins, the learning and design are done up-front, before you produce them. With data science, you learn as you go, not before you go.

In the pin factory, when learning comes first we do not expect, nor do we want, the workers to improvise on any aspect the product, except to produce it more efficiently. Organizing by function makes sense since task specialization leads to process efficiencies and production consistency (no variations in the end product).

But when the product is still evolving and the goal is to learn, specialization hinders our goals in several ways:

1. It increases coordination costs. Those are the costs that accrue in time spent communicating, discussing, justifying, and prioritizing the work to be done. These costs scale super-linearly with the number of people involved.2 When data scientists are organized by function the many specialists needed at each step, and with each change, and each handoff, and so forth, make coordination costs high. For example, a data science specialists focused on statistical modeling will have to coordinate with a data engineer any time a dataset needs to be augmented in order to experiment with new features. Similarly, any time new models are trained the research scientist will have to coordinate with a machine learning engineer to deploy them to production, etc. These coordination costs act as a tax on iteration and can hamper learning.

2. It exacerbates wait-time. Even more nefarious than coordinate costs is the time that elapses between work. While coordination costs can typically be measured in hours—the time it takes to hold meetings, discussions, design reviews—wait-times are commonly measured in days or weeks or even months! Schedules of functional specialists are difficult to align as each specialist is allocated to several initiatives. A one-hour meeting to discuss changes may take weeks to line up. And, once aligned on the changes, the actual work itself also needs to be scheduled in the context of multiple other projects vying for specialists’ time. Work like code changes or research that requires just a few hours or days to complete still may sit undone much longer before the resources are available. Until then, iteration and learning languish.

3. It narrows context. Division of labor can artificially limit learning by rewarding people for staying in their lane. For example, the research scientist who is relegated to stay within her function will focus her energy towards experimenting with different types algorithms: gradient boosting, neural nets, random forest, and so on. To be sure, good algorithm choices could lead to incremental improvements. But there is usually far more to gain from other activities like integrating new data sources. Similarly, she may develop a model that exhausts every bit of explanatory power inherent to the data. Yet, her biggest opportunity may lie in changing the objective function or relaxing certain constraints. This is hard to see or do when her job function is limited. Since the research scientist is specialized in optimizing algorithms, she’s far less likely to pursue anything else, even when it carries outsized benefits.

Telling symptoms can surface when data science teams are run like pin factories, for example in simple status updates: “waiting on ETL changes” and “waiting on ML Eng resources” are common blockers. However, I believe the more insidious impact lies in what you don’t hear, because you can’t lament what you haven’t yet learned. Perfect execution on requirements and complacency brought on by achieving process efficiencies can mask the difficult truth, that the organization is blissfully unaware of the valuable learnings they are missing out on.

The solution to this problem is, of course, to get rid of the pin factory. In order to encourage learning and iteration, data science roles need to be made more general, with broad responsibilities agnostic to technical function. That is, organize the data scientists such that they are optimized to learn. This means hiring “full stack data scientists”—generalists—that can perform diverse functions: from conception to modeling to implementation to measurement. With fewer3 people to keep in the loop, coordination costs plummet. The generalist moves fluidly between functions, extending the data pipeline to add more data, trying new features in the model, deploying new versions to production for causal measurement, and repeating the steps as quickly as new ideas come to her. Of course, the generalist performs the different functions sequentially rather than in parallel—she is just one person after all. However, doing the work typically takes just a fraction of the wait-time it would take for another specialist resource to come available. So, iteration time goes down.

Our generalist may not be as adept as a specialist in any one function. But we are not seeking functional excellence or small incremental improvements. Rather, we seek to learn and discover all-new business capabilities with step-change impact. With full context for the holistic solution she sees opportunities that a narrow specialist won’t. She has more ideas and tries more things. She fails more, too. However, the cost of failure is low and the benefits of learning are high. This asymmetry favors rapid iteration and rewards learning.

It is important to note that this amount of autonomy and diversity in skill granted to the full-stack data scientists depends greatly on the assumption of a solid data platform on which to work. A well constructed data platform abstracts the data scientists from the complexities of containerization, distributed processing, automatic failover, and other advanced computer science concepts. In addition to abstraction, a robust data platform can provide seamless hooks into an experimentation infrastructure, automate monitoring and alerting, provide auto-scaling, and enable visualization of debugging output and algorithmic results. These components are designed and built by data platform engineers, but to be clear, there is not a hand-off from the data scientist to a data platform team. It’s the data scientist that is responsible for all the code that is deployed to run on top of the platform. And, for the love of everything sacred and holy in the profession, don’t hand-off ETL for engineers to write.

I too was once lured to a function-based division of labor by the attraction of process efficiencies. But, through trial and error (is there no better way to learn?) I’ve found that more generalized roles better facilitate learning and innovating,4 and provide the right kinds of scaling: to discover and build many more business capabilities than a specialist approach. And, while there are some important considerations5 that may make this approach to organization more or less tenable in some companies (see footnote), I believe the full stack data scientist model provides a better starting place. Start with them, and then consciously (grudgingly) move toward a function-based division of labor only when clearly necessary.

Final thought.

There is further downside to functional specialization. It can lead to loss of accountability and passion from the workers. Smith himself criticizes the division of labor, suggesting that it leads to the dulling of talent—that workers become ignorant and insular as their roles are confined to a few repetitive tasks.6 While specialization may provide process efficiencies it is less likely to inspire workers.

By contrast, generalist roles provide all the things that drive job satisfaction: autonomy, mastery, and purpose.7 Autonomy in that they are not dependent on someone else for success. Mastery in that they know the business capability from end-to-end. And, purpose in that they have a direct connection to the impact on the business they’re making. If we succeed in getting people to be passionate about their work and making a big impact on the company, then the rest falls into place naturally.

Footnotes and References

[1]↩ I took the liberty of modernizing Smith’s use of pronouns.

[2]↩ As J. Richard Hackman taught us, the number of relationships (r) grows as a function number of members (n) per this equation: r = (n^2-n) / 2. And, each relationship bares some amount of coordination costs. See: Hackman, J. Richard. Leading teams: setting the stage for great performances. Boston, Mass.: Harvard Business School Press, 2002. Print.

[3]↩ It’s important to note that I am not suggesting that hiring full-stack data scientists results in fewer people overall. Rather, I am merely suggesting that when organized differently, their incentives are better aligned with learning vs. efficiency gains. Consider the following contrasting deptarment/team structures, each with 3 people. Fractional estimates and summed team sizes are illustrative only.

Specialist Model: organized for functional efficiency. Workers are not dedicated to any one business capability, rather their time is allocated to many.

Business Capability (columns)
Role (rows)
Recommendation System Algorithmic Inventory Management System Algorithmic Client Engagement System Team Size
ML Engineering 1/3 1/3 1/3 1
Modeling 1/3 1/3 1/3 1
Data Engineering 1/3 1/3 1/3 1
Total Dept Size 3

Generalists Model: Full-stack Data Scientists optimized for learning. Workers are fully dedicated to a business capability and perform all the functions.

Business Capability (columns)
Role (rows)
Recommendation System Algorithmic Inventory Management System Algorithmic Client Engagement System Total Dept Size
ML Engineering 1 1 1
Modeling
Data Engineering
Team Size 1 1 1 3

[4]↩ A more efficient way to learn about this approach to organization vs the trial and error I went through is to read the book by Amy C. Edmondson called “Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy” (Jossey-Bass, 2014).

[5]↩ This process of iteration assumes low cost of trial and error. If the cost of error is high you may want to rethink (i.e., it is not advised for medical or manufacturing). In addition, data volume and system availability requirements should also be considered. If you are dealing with petabytes or exabytes of data, specialization in data engineering may be warranted. Similarly, system availability (ie. uptime) and innovation are tradeoffs. If availability is paramount, functional excellence may trump learning. Finally, the full-stack data science model relies on the assumption of great people. They are not unicorns; they can be found as well as made. But they are in high demand and it will require certain conditions in order to attract and retain them (competitive compensation, company values, interesting work, etc.). Be sure your company culture can support this.

[6]↩ Smith, Adam. An inquiry into the nature and causes of the wealth of nations. Dublin: Printed for Messrs. Whitestone, 1776. Print. Page 464.

[7]↩ Pink, Daniel H.. Drive: the surprising truth about what motivates us. New York, NY: Riverhead Books, 2009.


          Associate Vice President/Vice President - Data Scientist - FinTech - IIT/NIT/BITS (2-6 yrs)      Cache   Translate Page      
New Delhi - Associate Vice President/Vice President - Data Scientist - FinTech - IIT/NIT/BITS (2-6 yrs) Delhi Job Code: 673699 Job Title : AVP/VP...-A, IIT, leaders of Fortune 50 companies and others....
          Associate Vice President/Vice President - Data Scientist - FinTech - IIT/NIT/BITS      Cache   Translate Page      
New Delhi - Associate Vice President/Vice President - Data Scientist - FinTech - IIT/NIT/BITS (2-6 yrs) Delhi Job Code: 673699 Job Title : AVP/VP...-A, IIT, leaders of Fortune 50 companies and others....
          Hardware Accelerated ATLAS Workloads on the WLCG      Cache   Translate Page      
In recent years the usage of machine learning techniques within data-intensive sciences in general and high-energy physics in particular has rapidly increased, in part due to the availability of large datasets on which such algorithms can be trained as well as suitable hardware, such as graphics or tensor processing units which greatly accelerate the training and execution of such algorithms. Within the HEP domain, the development of these techniques has so far relied on resources external to the primary computing infrastructure of the WLCG. In this paper we present an integration of hardware-accelerated workloads into the Grid through the declaration of dedicated queues with access to hardware accelerators and the use of linux container images holding a modern data science software stack. A frequent use-case of in the development of machine learning algorithms is the optimization of neural networks through the tuning of their hyper parameters. For this often a large range of network variations must be trained and compared, which for some optimization schemes can be performed in parallel -- a workload well suited for grid computing. An example of such a hyper-parameter scan on Grid resources for the case of Flavor Tagging within ATLAS is presented.
          Mission Innovation Data Scientist - Perspecta - Springfield, VA      Cache   Translate Page      
Experience in system activity and data modeling, information flow/transactional process analysis, internal control and risk analysis, business methods and...
From Perspecta - Mon, 17 Dec 2018 22:12:59 GMT - View all Springfield, VA jobs
          RET Design Engineer / Data Scientist - Intel - Hillsboro, OR      Cache   Translate Page      
Inside this Business Group. Intel invites you to join the team that develops and implements Resolution Enhancement Techniques that assist the successful pattern...
From Intel - Tue, 04 Dec 2018 12:11:42 GMT - View all Hillsboro, OR jobs
          **REMOTE** PHP Developer      Cache   Translate Page      
FL-Orlando, If you are a PHP Developer with Symfony experience, please read on! We are a fast-paced, startup, financial services firm with data science team to support our growing database and we need you to make it happen! What You Will Be Doing *REMOTE POSITION* - Participate in the architecture and design of new and existing products - Work with stakeholders to define constraints and develop requirements -
          Am 7. Mai ist pipes-Premiere: Warum wir mit OMR jetzt auch einen Tech-Event machen      Cache   Translate Page      

Ein Event, auf dem sich CTOs und Data Scientists an nur einem Tag über alle relevanten Entwicklungen in ihrem Bereich informieren können – das ist unser Ziel mit pipes. Unter diesem Namen werden wir am 7. Mai im Hamburger Docks erstmals parallel zum OMR Festival (am ersten Veranstaltungstag) ein Tech-Event durchführen. Pipes-Vaddi & OMR-Mitgründer Tobias […]

Der Beitrag Am 7. Mai ist pipes-Premiere: Warum wir mit OMR jetzt auch einen Tech-Event machen erschien zuerst auf Daily.


          Mozilla: Firefox Send, Task Configuration at Scale and More      Cache   Translate Page      
  • Use Firefox Send to safely share files for free

    Moving files around the web can be complicated and expensive, but with Firefox Send it doesn’t have to be. There are plenty of services that let you send files for free but you often run up against small file sharing sizes or have to deal with links that don’t expire, leaving your information online indefinitely. Many of these tools can provide extra control and privacy, but only after you pay for a subscription.

  • Task Configuration at Scale

    A talk I did for the Automationeer’s Assemble series on how Mozilla handles complexity in their CI configuration.

  • Hacks.Mozilla.Org: Iodide: an experimental tool for scientific communication and exploration on the web

    In the last 10 years, there has been an explosion of interest in “scientific computing” and “data science”: that is, the application of computation to answer questions and analyze data in the natural and social sciences. To address these needs, we’ve seen a renaissance in programming languages, tools, and techniques that help scientists and researchers explore and understand data and scientific concepts, and to communicate their findings. But to date, very few tools have focused on helping scientists gain unfiltered access to the full communication potential of modern web browsers. So today we’re excited to introduce Iodide, an experimental tool meant to help scientists write beautiful interactive documents using web technologies, all within an iterative workflow that will be familiar to many scientists.


          Guavus Plus SQLstream means Broad and Deep for IoT Data Science      Cache   Translate Page      

History

From the first time that Damian Black, founder of SQLstream, and Dr. Anukool Lakhina, founder of Guavus, first met almost a decade ago, the synergies and complementary nature of their visions was apparent to both of them. Guavus was acquired by Thales in 2017. Thales, a large, international player in aerospace and defense, with a large presence in transportation, expressed interest in SQLstream about four years ago. It was at this point that Damian and Anukool realized that the solutions Guavus and SQLstream had developed since their earlier discussions, had become even more strongly complementary, with Guavus' deep domain expertise in telecommunications, machine learning and data science, and SQLstream as a pioneer and leader in streaming analytics with an horizontal platform. In addition, Guavus is following Thales lead in broadening their domain expertise into the Industrial Internet of Things. SQLstream has had great success in the Transportation area, and in other sensor analytics ecosystems (SensAE). In addition, Guavus recognizes the need to process the vast amount of telecoms and IoT data closer to the source.

Integration

Although the merger is only a month old, the two companies are already working together to bring the strengths of each together for greater customer success. Over the next six to 12 months, the two will be integrated into a single platform with the ability to scale up to mind-numbingly large data flows, and to very finely-tuned small aggregates where and as needed throughout the ecosystem. This will allow greater operational efficiency as separating signal from noise, close to the source, allows processing the data immediately, providing value timely and cost effectively. Data rates are growing, per Damian, by 50% as edge sources increase in importance, but data storage and management costs are only decreasing by 12-14%. Only by pushing the algorithms, the machine learning models, into the streaming pipeline, and Guavus has some of the best in the industry, customers in Telecom, Transportation, and IIoT in general, will organizations be able to actually draw value from this data.

With our integrated solutions, CSPs to IIoT customers will be able to take advantage of something that’s radically different as we deliver AI-powered analytics from the network edge to the network core. With this solution, our customers can now analyze their operational, customer, and business data anywhere in the network in real time, without manual intervention, so they can make better decisions, provide smarter new services, and reduce their costs." — Guavus Press Release

This matches well with what we have seen, and what we present in SensAE, that the ebb and flow of data throughout the ecosystem must allow for appropriate aggregation and analytics at each point within the ecosystem.

Future

At MWC19, there has been a lot of interest in these specific solution, and also in building trust throughout the ecosystem, with security, and with the ability to select the desirable levels of privacy and transparency. Responding to these industry concerns is already in the Thales/Guavus/SQLstream roadmap.

The SQLstream products have the ability to analyze, filter, and aggregate data at the network edge in real-time and forward the information to the network core where the Guavus’ Reflex® platform can apply AI-powered analytics, giving customers a widely distributed and scalable architecture with better price/performance and total cost of ownership." — Guavus Press Release

The next few months are going to be exciting with SQLstream, Guavus and Thales bringing together their expertise in streaming analytics, data management, telecommunications, transportation, machine learning, data science, industrial needs and system engineering.


          Principal Security Data Scientist - Sierra Nevada Corporation - Sparks, NV      Cache   Translate Page      
DATA SCIENCE / MACHINE LEARNING SKILLS:. Sierra Nevada Corporation is an Equal Opportunity Employer. Required to act as a trusted adviser for business leaders...
From Sierra Nevada Corporation - Fri, 15 Feb 2019 23:07:32 GMT - View all Sparks, NV jobs
          Director, Modern Life and Gaming Data Platform - Microsoft - Redmond, WA      Cache   Translate Page      
Cosmos/ADL, Azure-SQL DW). The Engage team (part of Consumer Data and Analytics – CDnA) provides data, technology, operations, data science and analytics...
From Microsoft - Tue, 12 Feb 2019 00:42:24 GMT - View all Redmond, WA jobs
          Solution Architect & Data Scientist, AI/ML with strong front end UI/data visualization experience - Pivotal Consulting - Redmond, WA      Cache   Translate Page      
SQL Server, Azure SQL DB, Azure DW, Azure ML, Analytics Platform Server, HD Insight, Stream Insight, PowerBI, PowerPivot, PowerView, Excel....
From Pivotal Consulting - Tue, 25 Dec 2018 03:35:16 GMT - View all Redmond, WA jobs
          Data Scientist - MSi Corp. - Montréal, QC      Cache   Translate Page      
Our telecom client is looking for junior and intermediate level Data Scientists to utilize their analytical, statistical, and programming skills to analyze...
From Indeed - Fri, 01 Mar 2019 15:11:55 GMT - View all Montréal, QC jobs
          Data Scientist - MSi Corp. - Ottawa, ON      Cache   Translate Page      
Our telecom client is looking for junior level Data Scientists to utilize their analytical, statistical, and programming skills to analyze and interpret large...
From Indeed - Thu, 31 Jan 2019 22:22:20 GMT - View all Ottawa, ON jobs
          Minnesota State Mankato to Offer Master’s Degree in Data Science Starting in Fall 2019      Cache   Translate Page      
New program is first and only graduate program in data science in the Minnesota State system.
          Master Inteligencia Artificial ( Data scientist Junior) - Between Technology - Sant Cugat del Vallès      Cache   Translate Page      
¿Quieres llegar a ser Fullstack en PHP? Seleccionamos un Data scienstist Junior para incorporarse de forma indefinida en el equipo de desarrollo de uno de nuestros principales clientes dedicado al big data e inteligencia artificial La persona seleccionada desarrollará soluciones tecnológicas para IA del sector Retail. Se trata de una Start Up consolidada en el mercado que busca un/a perfil junior para que pueda crecer y desarrollarse en la compañia! En BETWEEN apostamos por el...
          Offer - PRACTICAL SQL 2017 Video Training @ job support - FINLAND      Cache   Translate Page      
SQL School is one of the best training institutes for Microsoft SQL Server Developer Training, SQL DBA Training, MSBI Training, Power BI Training, Azure Training, Data Science Training, Python Training, Hadoop Training, Tableau Training, Machine Learning Training, Oracle PL SQL Training. We have been providing Classroom Training, Live-Online Training, On Demand Video Training and Corporate trainings. All our training sessions are COMPLETELY PRACTICAL. SQL SERVER DBA Video Training Course Details: Real time training on SQL Server 2016 & 2017 DB Design and T-SQL. This training course is exclusively designed addressing all practical aspects of SQL Server fundamentals required for SQL DBA and Business Intelligence (MSBI) implementations. Material provided during the course. All Sessions are Completely Practical and Realtime. For free SQL Server DBA Video Demo, please visit : http://sqlschool.com/SQLDBA-Video-Training.html Schedules for PRACTICAL SQL 2016 & 2017 DBA Video TRAINING : http://sqlschool.com/Register.html Contact us today (24 x 7) for SQL DBA Practical Video Training SQL School Training Institute ISO 9001:2008 Certified Organization for Training Authorized Microsoft Partner (ID# 5108842) India: Mobile: +91 (0) 9666 44 0801 Mobile: +91 (0) 9666 64 0801 USA: Office: +1 (510) 400-4845 Office 1: #101, UMA Residency, Opp: Sindhu Travels, Beside Metro Station Gate #D, SR Nagar, Hyderabad - 38, India. . Website: http://sqlschool.com/ Follow us: https://www.facebook.com/sequelschool https://www.linkedin.com/company/sql-school https://twitter.com/sequelschool
          Lead Data Engineer - Neiman Marcus - Dallas, TX      Cache   Translate Page      
Work with business partners and data science teams to understand business context and craft best-in-class solutions to their toughest problems....
From Neiman Marcus - Fri, 15 Feb 2019 01:35:31 GMT - View all Dallas, TX jobs
          Statistician Technician - WEST Inc - Cheyenne, WY      Cache   Translate Page      
Experience with other data science toolkits (Python, C#, JavaScript, etc.). Western EcoSystems Technology, Inc.... $18 - $22 an hour
From WEST Inc - Tue, 29 Jan 2019 11:41:19 GMT - View all Cheyenne, WY jobs
          Data Scientist - Oliver Wyman - New York, NY      Cache   Translate Page      
Demonstrate solid and battle-tested understanding of the standard canon of machine learning practices, including but not limited to:....
From Marsh & McLennan Companies - Sat, 05 Jan 2019 15:04:15 GMT - View all New York, NY jobs
          Data Scientist - -      Cache   Translate Page      
Description Start ASAP ******* Pty Ltd provides IT Contracting and Professional Services to the Canberra Market. Join a talented team and work in a culture that fosters an inclusive and flexible w...
          Senior Director of R&D Data Science Platforms - Johnson & Johnson Family of Companies - Spring House, PA      Cache   Translate Page      
Assess the financial implications and business case for platforms and help communicate overarching return on investment, business value drivers, success metrics...
From Johnson & Johnson Family of Companies - Thu, 22 Nov 2018 11:16:26 GMT - View all Spring House, PA jobs
          Data Engineer, Product Line Owner Data Management - Johnson & Johnson Family of Companies - Raritan, NJ      Cache   Translate Page      
Pharma Industry and domain expertise, technical knowledge of data science platforms and software technology, knowledge of business process and business strategy...
From Johnson & Johnson Family of Companies - Wed, 23 Jan 2019 08:07:32 GMT - View all Raritan, NJ jobs
          Principal Data Scientist - Johnson & Johnson Family of Companies - Raritan, NJ      Cache   Translate Page      
The primary location for this position is Spring House, PA or Raritan, NJ. Experience in developing presentations and communications to be shared with internal...
From Johnson & Johnson Family of Companies - Thu, 22 Nov 2018 11:16:26 GMT - View all Raritan, NJ jobs
          Senior Scientist, Data Science Strategy and Innovation - Johnson & Johnson Family of Companies - Titusville, NJ      Cache   Translate Page      
Consideration will be given to other locations (e.g., Raritan, NJ; Your proven social skills establish relationships, you act as a change agent, and adapt to...
From Johnson & Johnson Family of Companies - Thu, 07 Mar 2019 14:06:40 GMT - View all Titusville, NJ jobs
          Building This Missing xAPI Layer Will Make You Rich If You Figure Out How      Cache   Translate Page      
If the mountain (of LMS Data) will not come to Mahomet, Mahomet will develop an anonymized, xAPI-compliant LMS Data generation service. (The assumption here being Mahomet would be a pretty good Open Learning Technologist.) When it comes to disrupting industries to the core, Data Science has an Achilles heel. It’s at the start of its […]
          Mozilla releases Iodide, an open source browser tool for publishing dynamic data science      Cache   Translate Page      
Mozilla's new Iodide tool, which is available in open source, aims to streamline the document creation process for data scientists.
          Senior Data Scientist - Predictive Enterprise Group - Neudesic LLC - Philadelphia, PA      Cache   Translate Page      
Machine Learning Solutions:. The explosion of big data, machine learning and cloud computing power creates an opportunity to make a quantum leap forward in...
From Neudesic LLC - Sat, 15 Dec 2018 21:58:12 GMT - View all Philadelphia, PA jobs
          AI For Everyone: What Andrew Ng wants to convey with this Non Technical Course in 30 points.      Cache   Translate Page      
Harveen Singh, Towards Data Science AI for everyone is a non technical course taking which you will have greater knowledge than most CEO’s in the world. At least this is what Andrew Ng claims. So let’s find out in short what he wants to convey. https://towardsdatascience.com/ai-for-everyone-what-andrew-ng-want-to-convey-with-this-non-technical-course-in-30-points-bedaea57c81b Share on Facebook
          Ce soir, Paris Machine Learning #5 season 6: Explainable AI, Unity Challenge, Ethical AI      Cache   Translate Page      


Tonight, we will be hosted and sponsored by CFM capital. Thank you to them. 

The schedule is as followd :
6:45 Doors open
7PM - 9PM Talks
9PM - 10PM Cocktail - Networking

As usual, there is NO waiting list or reserved seat First come first served (the room has 110 seats)

This meetup will be streamed see below:



The presentations:

introduction to CFM Capital, Eric Lebigot

Vincent-Pierre Berges, The Obstacle Tower A Generalization Challenge in Vision, Control, and Planning, https://unity3d.com

The rapid pace of research in Deep Reinforcement Learning has been driven by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games, to classic home console games, to modern strategy games. We propose a new benchmark called Obstacle Tower: a high visual fidelity, 3D, 3rd person, procedurally generated game environment. An agent in the Obstacle Tower must learn to solve both low-level control and high-level planning problems in tandem while learning from pixels and a sparse reward signal. Unlike other similar benchmarks such as the ALE, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment.

$100K AI Contest
Obstacle Tower Challenge
https://www.youtube.com/watch?v=nvdZpJkT-ls

=

Manar Toumi, Leornardo Noleto, Interpretability, https://www.bleckwen.ai

Machine learning interpretability is becoming an integral part of the data scientist workflow and can no longer be an afterthought. This talk will explore the vibrant area of machine learning interpretability and explain how to understand black-box models. Thanks to an interpretability technique based on colitional game theory: SHAP.

====

Cloderic Mars, craft.ai, Explainable AI

When it comes to actually leverage AI in production and especially in an environment where it interacts with humans, auditability and trust are not optional. That's why Explainable AI becomes a new R&D space. This talks will show why and where explainability in AI is needed, what it actually means and compare some of the techniques that falls into this category.

Arnaud de Moissac, https://dcbrain.com, Impact AI
Impact AI is a think and Do tank that aims to deal with the ethical and societal challenges of AI. We develop an ethical framework for responsible use of Artificial Intelligence respecting principles easy to understand and apply at a large scale. This talk is about the Governance part of this tool box

Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

          DATA SCIENTIST - TELECOMMUNICATIONS - TransUnion - Chicago, IL      Cache   Translate Page      
You will partner with internal and external cross-functional teams to drive new business initiatives and deliver long term value-added product propositions for...
From TransUnion - Thu, 27 Sep 2018 15:13:09 GMT - View all Chicago, IL jobs
          Data Science: предсказание бизнес-событий для улучшения сервиса      Cache   Translate Page      
Алгоритмы рекомендаций, предсказания событий либо оценки рисков – трендовое решение в банках, страховых компаниях и многих других отраслях бизнеса. Например, эти программы помогают на основе анализа данных предположить, когда клиент вернет банковский кредит, какой будет спрос в ритейле, какова вероятность наступления страхового случая или оттока клиентов в телекоме и т.д. Для бизнеса это ценная возможность оптимизировать свои расходы, повысить скорость работы и в целом улучшить сервис.

Вместе с тем, для построения подобных программ не годятся традиционные подходы – классификация и регрессия. Рассмотрим эту проблему на примере кейса, посвященного предсказанию медицинских эпизодов: проанализируем нюансы в природе данных и возможные подходы к моделированию, построим модель и проанализируем ее качество. Читать дальше →
          Elizabeth Norman International: Analytics Manager | Data & Analytics Consultancy       Cache   Translate Page      
£40000-50000 per annum: Elizabeth Norman International: We are looking for a few data scientists to join a thriving consultancy data & analytics consultancy. London
          Data Scientist - WVU Medicine - Morgantown, WV      Cache   Translate Page      
Certification in Oracle or SQL Development. Skilled at using programing languages such as Java, Python, Oracle, HTML, DHTML, CSS, JSON, HL7. JOB TITLE &amp; CODE:....
From WVU Medicine - Fri, 22 Feb 2019 23:45:36 GMT - View all Morgantown, WV jobs
          Be a Data Scientist      Cache   Translate Page      
Be a Data Scientist è il progetto di citizen science scritto a quattro mani da Frascati Scienza e Giornalisti nell’erba che vuole indagare su come e dove si informano i giovani tra gli 11 e i 19 anni. L’informazione sta vivendo un periodo di profonda crisi: le fonti che girano nel web sono spesso di dubbia provenienza, la rapidità [...]
          Data Scientist - Al-Futtaim - Dubai      Cache   Translate Page      
As part of our candidate experience promise, we also want to make ourselves available to you throughout the application process....
From Al-Futtaim - Wed, 13 Mar 2019 15:16:57 GMT - View all Dubai jobs
          Data Warehouse Developer - Paris      Cache   Translate Page      
About the role As a Data Warehouse developer you will create and innovate the Vestiaire Collective Data Platform in collaboration with our Data Engineers, BI Engineers and Data Scientists.. Who you are...
          Data Science: предсказание бизнес-событий для улучшения сервиса      Cache   Translate Page      
Алгоритмы рекомендаций, предсказания событий либо оценки рисков – трендовое решение в банках, страховых компаниях и многих других отраслях бизнеса. Например, эти программы помогают на основе анализа данных предположить, когда клиент вернет банковский кредит, какой будет спрос в ритейле, какова вероятность наступления страхового случая или оттока клиентов в телекоме и т.д. Для бизнеса это ценная возможность оптимизировать свои расходы, повысить скорость работы и в целом улучшить сервис.

Вместе с тем, для построения подобных программ не годятся традиционные подходы – классификация и регрессия. Рассмотрим эту проблему на примере кейса, посвященного предсказанию медицинских эпизодов: проанализируем нюансы в природе данных и возможные подходы к моделированию, построим модель и проанализируем ее качество. Читать дальше →
          Lecturer - Data Science - University of Wisconsin- Green Bay - Green Bay, WI      Cache   Translate Page      
System analysis and design; (Currently employed by the University of Wisconsin System). (NOT currently employed by the University of Wisconsin System)....
From University of Wisconsin- Green Bay - Tue, 18 Dec 2018 17:39:58 GMT - View all Green Bay, WI jobs
          Data Elixir - Issue 224      Cache   Translate Page      

In the News

The AI-Art Gold Rush Is Here

The gold rush started last October when Christie's sold an algorithm generated print for $432,500. More recently, an AI artist had its own show at a gallery in Chelsea. There's definitely a lot of interest here but is AI art really all that interesting? This longread in the Atlantic explores this burgeoning industry with links to artwork so you can judge for yourself.

theatlantic.com

Insight

Why Data Science Teams Need Generalists, Not Specialists

A team of specialists works well in environments where the organization knows exactly what needs to be done and execution can be managed like an assembly line. This article by Eric Colson explores why that's rarely the case in data science and how specialization can get in the way.

hbr.org

Sponsored Link

Master of Management Analytics: Your degree for the world of data

Realize the promise of data analytics and find the opportunity in the numbers. The Master of Management Analytics from Smith School of Business is essential training to unleash the potential of data and generate competitive advantage.

qns.bz

Tools and Techniques

Viewing Matrices & Probability as Graphs

Nice post that starts by showing how every matrix is a graph. From there, it's a visual tour of matrix operations and probabilities. Great read!

math3ma.com

Why Model Explainability is The Next Data Science Superpower

In this excerpt from his model explainability course, Dan Becker outlines the types of things that the very best data scientists are able to discern about their models and why that information is useful. This post also sparked a worthwhile discussion on Hacker News.

towardsdatascience.com

Exploring Neural Networks with Activation Atlases

Great interactive article on the Distil site that introduces a new technique for visualizing how decision-making happens in a neural network. It's a long read but it's compelling all the way through.

distill.pub

Set Your Jupyter Notebook up Right with this Extension

By default, Jupyter Notebooks are unnamed, have no markdown cells, and no imports. Since people are notoriously bad at changing default settings, why not encourage better practices? This simple extension gently nudges you to create better notebooks.

towardsdatascience.com

Lessons learned building natural language processing systems in health care

Building NLP systems in a complex domain like health care is hard. Not only do these systems require broad domain knowledge, every sub-specialty and form of communication is fundamentally different. In this post, David Talby outlines common issues and the lessons he's learned over 7 years of building NLP systems in health care.

oreilly.com

Find A Data Science Job Through Vettery

Vettery specializes in tech roles and is completely free for job seekers. Interested? Submit your profile, and if accepted onto the platform, you can receive interview requests directly from top companies growing their data science teams.

// sponsored

vettery.com

Resources

Awesome Machine Learning Interpretability

This curated list of machine learning interpretability resources is definitely worthy of its "awesome" moniker. Includes a blueprint of use-cases, software examples, tutorials, packages, books, papers, etc.

github.com

Data Viz

Data Visualization Society Logo: Behind the scenes

"Logo design" may not sound interesting but this post describes the logo for the newly formed Data Visualization Society. The logo changes dynamically according to member skills and it's unlike any logo you've ever seen.

medium.com

Jobs & Careers

Post on Data Elixir's Job Board to reach a wide audience of data professionals.

dataelixir.com

About

Data Elixir is curated and maintained by @lonriesberg. For additional finds from around the web, follow Data Elixir on Twitter or Facebook.


This RSS feed is published on https://dataelixir.com/. You can also subscribe via email.


          Data Science at Scale: A Conversation with Uber’s Fran Bell      Cache   Translate Page      

Fran Bell has always been a scientist; theorizing, modeling and testing how the world works. An ever-curious child, she was fascinated by the natural world, poring over biology and chemistry books, but was never satisfied with just knowing; she

The post Data Science at Scale: A Conversation with Uber’s Fran Bell appeared first on Uber Engineering Blog.


          Front End Developer      Cache   Translate Page      
NY-New York, Front-End Developer – join a team developing HR Analytics in Midtown, Manhattan - Contract role in Midtown, Manhattan Our client is seeking a Front End Developer to join their HR Analytics team. The team is a collection of over twenty analysts and data scientists. Bright, driven colleagues with varied backgrounds focused on driving insight and action around key strategic HR and people issues. The
          Microsoft Excel gets trolled and hated for Surf Excel      Cache   Translate Page      

New Delhi: Software giant Microsoft has become an unexpected victim of hate messages directed at a Surf Excel detergent commercial that has raked up a huge controversy in India over the past few days.

The latest commercial, part of Surf Excel's "Daag Achche Hai Campaign", shows a Hindu girl saving a Muslim boy from colours on Holi. However, it has offended some people, who are venting their anger on the social media.

Some uninformed folks, who joined the hate parade, confused Surf Excel with Microsoft Excel.

Soon #BoycottSurfExcel, #BoycottExcel, #MicrosoftExcel and more such hashtags involving Microsoft were trending on the social media.

"Going to request Sundar Pichai to throw you away from the Playstore. And, of course Satya Nadella as well to remove you from Microsoft Office. As a data scientist, it was tough to say this but country over profession anytime," wrote Aditya Bhandari, who claims to be a data scientist, on PlayStore.

Senior journalist Raju Narisetti posted a screenshot of Bhandari's comment on Twitter, expressing his shock at the ignorance.


          DATA INSIGHTS WIZARD      Cache   Translate Page      
A special project where we need a data scientist to comb through data from 150 survey respondents and find trends. Need to understand how woman answer vs. men, age differences, program differences, etc. (Budget: $8 - $15 USD, Jobs: Data Analytics, Data Science, Statistics)
          Principal Data Scientist - Clockwork Solutions - Austin, TX      Cache   Translate Page      
Support Clockwork’s Business Development efforts. Evaluates simulation analysis output to reveal key insights about unstructured, chaotic, real-world systems....
From Clockwork Solutions - Wed, 26 Dec 2018 10:03:15 GMT - View all Austin, TX jobs
          Lead Data Scientist - Clockwork Solutions - Austin, TX      Cache   Translate Page      
Support Clockwork’s Business Development efforts. Evaluates simulation analysis output to reveal key insights about unstructured, chaotic, real-world systems....
From Clockwork Solutions - Wed, 26 Dec 2018 10:03:14 GMT - View all Austin, TX jobs
          Data Scientist: Medical VoC and Text Analytics Manager - GlaxoSmithKline - Research Triangle Park, NC      Cache   Translate Page      
Strong business acumen; 2+ years of unstructured data analysis/text analytics/natural language processing and/or machine learning application for critical...
From GlaxoSmithKline - Fri, 19 Oct 2018 23:19:12 GMT - View all Research Triangle Park, NC jobs
          Vice President, Data Science - Service Management Group, Inc. - Kansas City, MO      Cache   Translate Page      
Experience building application programming interface (API’s) platforms for business to business integration....
From Service Management Group, Inc. - Thu, 20 Dec 2018 22:45:46 GMT - View all Kansas City, MO jobs
          European Graduate Program - Data Science (Operations & SC) - Avery Dennison - Oegstgeest      Cache   Translate Page      
Then help us fostering a new culture within Avery Dennison of problem solving through the mastering and understanding of data!...
Van Avery Dennison - Fri, 01 Feb 2019 04:20:40 GMT - Toon alle vacatures in Oegstgeest
          Master Inteligencia Artificial ( Data scientist Junior) - Between Technology - Sant Cugat del Vallès      Cache   Translate Page      
¿Quieres llegar a ser Fullstack en PHP? Seleccionamos un Data scienstist Junior para incorporarse de forma indefinida en el equipo de desarrollo de uno de nuestros principales clientes dedicado al big data e inteligencia artificial La persona seleccionada desarrollará soluciones tecnológicas para IA del sector Retail. Se trata de una Start Up consolidada en el mercado que busca un/a perfil junior para que pueda crecer y desarrollarse en la compañia! En BETWEEN apostamos por el...
          Going ATOMIC: Clustering and Associating Attacker Activity at Scale « Going ATOMIC: Clustering and Associating Attacker Activity at Scale      Cache   Translate Page      

At FireEye, we work hard to detect, track, and stop attackers. As part of this work, we learn a great deal of information about how various attackers operate, including details about commonly used malware, infrastructure, delivery mechanisms, and other tools and techniques. This knowledge is built up over hundreds of investigations and thousands of hours of analysis each year. At the time of publication, we have 50 APT or FIN groups, each of which have distinct characteristics. We have also collected thousands of uncharacterized 'clusters' of related activity about which we have not yet made any formal attribution claims. While unattributed, these clusters are still useful in the sense that they allow us to group and track associated activity over time.

However, as the information we collect grows larger and larger, we realized we needed an algorithmic method to assist in analyzing this information at scale, to discover new potential overlaps and attributions. This blog post will outline the data we used to build the model, the algorithm we developed, and some of the challenges we hope to tackle in the future.

The Data

As we detect and uncover malicious activity, we group forensically-related artifacts into 'clusters'. These clusters indicate actions, infrastructure, and malware that are all part of an intrusion, campaign, or series of activities which have direct links. These are what we call our "UNC" or "uncategorized" groups. Over time, these clusters can grow, merge with other clusters, and potentially 'graduate' into named groups, such as APT33 or FIN7. This graduation occurs only when we understand enough about their operations in each phase of the attack lifecycle and have associated the activity with a state-aligned program or criminal operation.

For every group, we can generate a summary document that contains information broken out into sections such as infrastructure, malware files, communication methods, and other aspects. Figure 1 shows a fabricated example with the various 'topics' broken out. Within each 'topic' – such as 'Malware' – we have various 'terms', which have associated counts. These numbers indicate how often we have recorded a group using that 'term'.

image
Figure 1: Example group 'documents' demonstrating how data about groups is recorded

The Problem

Our end goal is always to merge a new group either into an existing group once the link can be proven, or to graduate it to its own group if we are confident it represents a new and distinct actor set. These clustering and attribution decisions have thus far been performed manually and require rigorous analysis and justification. However, as we collect increasingly more data about attacker activities, this manual analysis becomes a bottleneck. Clusters risk going unanalyzed, and potential associations and attributions could slip through the cracks. Thus, we now incorporate a machine learning-based model into our intelligence analysis to assist with discovery, analysis, and justification for these claims.

The model we developed began with the following goals:

  1. Create a single, interpretable similarity metric between groups
  2. Evaluate past analytical decisions
  3. Discover new potential matches

image
Figure 2: Example documents highlighting observed term overlaps between two groups

The Model

This model uses a document clustering approach, familiar in the data science realm and often explained in the context of grouping books or movies. Applying the approach to our structured documents about each group, we can evaluate similarities between groups at scale.

First, we decided to model each topic individually. This decision means that each topic will result in its own measure of similarity between groups, which will ultimately be aggregated to produce a holistic similarity measure.

Here is how we apply this to our documents.

Within each topic, every distinct term is transformed into a value using a method called term frequency -inverse document frequency, or TF-IDF. This transformation is applied to every unique term for every document + topic, and the basic intuition behind it is to:

  1. Increase importance of the term if it occurs often with the document.
  2. Decrease the importance of the term if it appears commonly across all documents.

This approach rewards distinctive terms such as custom malware families – which may appear for only a handful of groups – and down-weights common things such as 'spear-phishing', which appear for the vast majority of groups.

Figure 3 shows an example of TF-IDF being applied to a fictional "UNC599" for two terms: mal.sogu and mal.threebyte. These terms indicate the usages of SOGU and THREEBYTE within the 'malware' topic and thus we calculate their value within that topic using TF-IDF. The first (TF) value is how often those terms appeared as a fraction of all malware terms for the group. The second value (IDF) is the inverse of how frequently those terms appear across all groups. Additionally, we take the natural log of the IDF value, to smooth the effects of highly common terms – as you can see in the graph, when the value is close to 1 (very common terms), the log evaluates to near-zero, thus down-weighting the final TF x IDF value. Unique values have a much higher IDF, and thus result in higher values.

image
Figure 3: Breakdown of TF-IDF metric when evaluated for a single group in regard to malware

Once each term has been given a score, each group is now reflected as a collection of distinct topics, and each topic is a vector of scores for the terms it contains. Each vector can be conceived as an arrow, detailing the 'direction' that group is 'pointing'within that topic.

Within each topic space, we can then evaluate the similarity of various groups using another method – Cosine Similarity. If, like me, you did not love trigonometry – fear not! The intuition is simple. In essence, this is a measure of how parallel two vectors are. As seen in Figure 4, to evaluate two groups' usage of malware, we plot their malware vectors and see if they are pointing in the same direction. More parallel means they are more similar.

image
Figure 4: Simplified breakdown of Cosine Similarity metric when applied to two groups in the malware 'space'

One of the nice things about this approach is that large and small vectors are treated the same – thus, a new, relatively small UNC cluster pointing in the same direction as a well-documented APT group will still reflect a high level of similarity. This is one of the primary use cases we have, discovering new clusters of activity with high similarity to already established groups.

Using TF-IDF and Cosine Similarity, we can now calculate the topic-specific similarities for every group in our corpus of documents. The final step is to combine these topic similarities into a single, aggregate metric (Figure 5). This single metric allows us to quickly query our data for 'groups similar to X' or 'similarity between X and Y'. The question then becomes: What is the best way to build this final similarity?

image
Figure 5: Overall model flow diagram showing individual topic similarities and aggregation in to final similarity matrix

The simplest approach is to take an average, and at first that’s exactly what we did. However, as analysts, this approach did not sync well with analyst intuition. As analysts, we feel that some topics matter more than others. Malware and methodologies should be more important than say, server locations or target industries...right? However, when challenged to provide custom weightings for each topic, it was impossible to find an objective weighting system, free from analyst bias. Finally, we thought: "What if we used existing, known data to tell us what the right weights are?" In order to do that, we needed a lot of known – or "labeled" – examples of both similar and dissimilar groups.

Building a Labeled Dataset

At first our concept seemed straightforward: We would find a large dataset of labeled pairs, and then fit a regression model to accurately classify them. If successful, this model should give us the weights we wanted to discover.

Figure 6 shows some graphical intuition behind this approach. First, using a set of ‘labeled’ pairs, we fit a function which best predicts the data points.

image
Figure 6: Example Linear regression plot – in reality we used a Logistic Regression, but showing a linear model to demonstrate the intuition

Then, we use that same function to predict the aggregate similarity of un-labeled pairs (Figure 7).

image
Figure 7: Example of how we used the trained model to predict final similarity from individual topic similarities.

However, our data posed a unique problem in the sense that only a tiny fraction of all potential pairings had ever been analyzed. These analyses happened manually and sporadically, often the result of sudden new information from an investigation finally linking two groups together. As a labeled dataset, these pairs were woefully insufficient for any rigorous evaluation of the approach. We needed more labeled data.

Two of our data scientists suggested a clever approach: What if we created thousands of 'fake' clusters by randomly sampling from well-established APT groups? We could therefore label any two samples that came from the same group as definitely similar, and any two from separate groups as not similar (Figure 8). This gave us the ability to synthetically generate the labeled dataset we desperately needed. Then, using a linear regression model, we were able to elegantly solve this 'weighted average' problem rather than depend on subjective guesses.

image
Figure 8: Example similarity testing with 'fake' clusters derived from known APT groups

Additionally, these synthetically created clusters gave us a dataset upon which to test various iterations of the model. What if we remove a topic? What if we change the way we capture terms? Using a large labeled dataset, we can now benchmark and evaluate performance as we update and improve the model.

To evaluate the model, we observe several metrics:

  • Recall for synthetic clusters we know come from the same original group – how many do we get right/wrong? This evaluates the accuracy of a given approach.
  • For individual topics, the 'spread' between the calculated similarity of related and unrelated clusters. This helps us identify which topics help separate the classes best.
  • The accuracy of a trained regression model, as a proxy for the 'signal' between similar and dissimilar clusters, as represented by the topics. This can help us identify overfitting issues as well.

Operational Use

In our daily operations, this model serves to augment and assist our intelligence experts. Presenting objective similarities, it can challenge biases and introduce new lines of investigation not previously considered. When dealing with thousands of clusters and new ones added every day from analysts around the globe, even the most seasoned and aware intel analyst could be excused for missing a potential lead. However, our model is able to present probable merges and similarities to analysts on demand, and thus can assist them in discovery.

Upon deploying this to our systems in December 2018, we immediately found benefits. One example is outlined in this blog post about potentially destructive attacks. Since then we have been able to inform, discover, or justify dozens of other merges.

Future Work

Like all models, this one has its weaknesses and we are already working on improvements. There is label noise in the way we manually enter information from investigations. There is sometimes 'extraneous' data about attackers that is not (yet) represented in our documents. Most of all, we have not yet fully incorporated the 'time of activity' and instead rely on 'time of recording'. This introduces a lag in our representation, which makes time-based analysis difficult. What an attacker has done lately should likely mean more than what they did five years ago.

Taking this objective approach and building the model has not only improved our intel operations, but also highlighted data requirements for future modeling efforts. As we have seen in other domains, building a machine learning model on top of forensic data can quickly highlight potential improvements to data modeling, storage, and access. Further information on this model can also be viewed in this video, from a presentation at the 2018 CAMLIS conference.

We have thus far enjoyed taking this approach to augmenting our intelligence model and are excited about the potential paths forward. Most of all, we look forward to the modeling efforts that help us profile, attribute, and stop attackers.


          Data Scientist - WVU Medicine - Morgantown, WV      Cache   Translate Page      
Skilled at using programing languages such as Java, Python, Oracle, HTML, DHTML, CSS, JSON, HL7. Improves business processes and supports critical business...
From WVU Medicine - Fri, 22 Feb 2019 23:45:36 GMT - View all Morgantown, WV jobs
          Manager, Data Science - Micron - Boise, ID      Cache   Translate Page      
Create server based visualization applications that use machine learning and predictive analytic to bring new insights and solution to the business....
From Micron - Fri, 30 Nov 2018 00:47:34 GMT - View all Boise, ID jobs
           Fantasy Baseball Rankings 2019, draft strategy: Carlos Correa, Josh Donaldson among bounce-back candidates       Cache   Translate Page      
SportsLine data scientist John Bollman has revealed 10 2019 Fantasy baseball bounce-back candidates
          Junior Project Manager - Data & Insights Group - CBS Interactive - Fort Lauderdale, FL      Cache   Translate Page      
The candidate will be a key member of the Data and Insights Group made up of data scientists, business analysts, data engineers and researchers....
From CBS Sports Network - Sat, 02 Mar 2019 02:40:12 GMT - View all Fort Lauderdale, FL jobs
          Determined AI nabs $11M Series A to democratize AI development      Cache   Translate Page      

Deep learning involves a highly iterative process where data scientists build models and test them on GPU-powered systems until they get something they can work with. It can be expensive and time-consuming, often taking weeks to fashion the right model. Determined AI, a new startup wants to change that by making the process faster, cheaper […]

The post Determined AI nabs $11M Series A to democratize AI development appeared first on RocketNews | Top News Stories From Around the Globe.


          Data Scientist - Oliver Wyman - New York, NY      Cache   Translate Page      
Demonstrate solid and battle-tested understanding of the standard canon of machine learning practices, including but not limited to:....
From Marsh & McLennan Companies - Sat, 05 Jan 2019 15:04:15 GMT - View all New York, NY jobs
          Master Inteligencia Artificial ( Data scientist Junior) - Between Technology - Sant Cugat del Vallès      Cache   Translate Page      
¿Quieres llegar a ser Fullstack en PHP? Seleccionamos un Data scienstist Junior para incorporarse de forma indefinida en el equipo de desarrollo de uno de nuestros principales clientes dedicado al big data e inteligencia artificial La persona seleccionada desarrollará soluciones tecnológicas para IA del sector Retail. Se trata de una Start Up consolidada en el mercado que busca un/a perfil junior para que pueda crecer y desarrollarse en la compañia! En BETWEEN apostamos por el...
          HDR-TRIPODS Solicitation Webinar      Cache   Translate Page      

Mar 18 2019 2:00PM to
Mar 18 2019 3:30PM
Eastern Standard Time (New York, UTC/GMT-05:00)

Transdisciplinary Research In Principles Of Data Science (TRIPODS) aims to bring together the electrical engineering, mathematics, statistics, and theoretical computer science communities to develop the theoretical foundations of data science through integrated research and training activities. Phase I will support the development of small collaborative Institutes. Phase II (to be described in an anticipated future solicitation, subject to availability of funds) will ...
More at https://www.nsf.gov/events/event_summ.jsp?cntn_id=297996&WT.mc_id=USNSF_13&WT.mc_ev=click


This is an NSF Events item.

          Data Scientist - WVU Medicine - Morgantown, WV      Cache   Translate Page      
Skilled at using programing languages such as Java, Python, Oracle, HTML, DHTML, CSS, JSON, HL7. Improves business processes and supports critical business...
From WVU Medicine - Fri, 22 Feb 2019 23:45:36 GMT - View all Morgantown, WV jobs
          Java Developer - SkipTheDishes - Saskatoon, SK      Cache   Translate Page      
Exceptional knowledge of Java, especially Java 8. Think you have what it takes to join an elite team of software developers, engineers, and data scientists?...
From SkipTheDishes - Wed, 13 Feb 2019 17:14:45 GMT - View all Saskatoon, SK jobs
          Sr. Director - Product Mgmt - Vantage - ADP - Alpharetta, GA      Cache   Translate Page      
Engineer Analyst Architect Data Scientist Application Developer Design Implementation Chief Principal Enterprise Specialist Infrastructure Research Development...
From Automatic Data Processing - Wed, 06 Mar 2019 11:32:57 GMT - View all Alpharetta, GA jobs
          022: Data Science Lead - Freight Brokerage - Dataspace - Green Bay, WI      Cache   Translate Page      
NoSQL databases such as MongoDB or Cassandra. Our client, one of the nation’s top transportation and logistics enterprises, has asked us to provide them with a... $150,000 a year
From Dataspace - Fri, 11 Jan 2019 18:05:23 GMT - View all Green Bay, WI jobs
          Data Scientist Lead - Schneider National - Green Bay, WI      Cache   Translate Page      
Experience with machine learning software (e.g., R, Python, SPSS, SAS), data access/manipulation (e.g., SQL, pandas, dplyr) and NoSQL databases (e.g., MongoDB,...
From Schneider National - Thu, 03 Jan 2019 06:22:26 GMT - View all Green Bay, WI jobs
          Data Scientist Lead - Schneider - Green Bay, WI      Cache   Translate Page      
Experience with machine learning software (e.g., R, Python, SPSS, SAS), data access/manipulation (e.g., SQL, pandas, dplyr) and NoSQL databases (e.g., MongoDB,...
From Schneider - Wed, 02 Jan 2019 23:36:22 GMT - View all Green Bay, WI jobs
          (USA-MD-Laurel) Senior Software Engineer / Computer Scientist      Cache   Translate Page      
## Position Description **Are you looking for an opportunity that will keep you engaged, challenged, and growing year after year?** **Are you searching for meaningful work that prioritizes creative problem-solving over profits?** **Do you have a strong software engineering and mathematics background?** If so, we're looking for someone like you to join our team at APL. The Tactical Intelligence Systems Group of the Asymmetric Operations Sector is seeking experienced engineers, scientists, and developers driven by curiosity, motivated to deliver solutions, and who have a real passion for learning! We are looking for developers create powerful, cutting-edge solutions for challenges in immersive user interfaces, run-time simulation, machine learning, and artificial intelligence. This may involve: * Surveying academic research to and industry tools to solve problems related to game engine rendering, graphics optimization, and custom shaders * Crafting simulations to generate datasets for machine learning algorithms * Performing full-stack architecture and API design for integrating diverse systems * Building and implementing artificial intelligence algorithms to drive characters in a variety of simulations * Developing immersive user experiences in augmented and virtual reality * Developing software frameworks to manage and analyze agent behavior * Collaborating with Laboratory, for-profit contractor, and sponsor teams to address critical sponsor needs * Effectively communicating results with non-expert audiences, and generating creative ideas to benefit the country. * Some limited travel (up to 10%) to customer sites, and occasional weekend and other after-hours work required to handle and/or complete critical project/work-related business needs. **As a Senior Software Engineer / Computer Scientist, you will....** * Primarily be responsible for applying knowledge in game design, machine learning, full-stack design, and software development to data analysis problems for our sponsors. * Work independently and on teams to engineer software solutions. * Explore promising research and maintain / gain the technical edge required for projects, and share and develop new approaches and methods. * Collaborate to document and support software analytics, and clearly present status and results to internal and external partners. ## Qualifications **You meet our minimum qualifications for the job if you...** * Possess a BS degree in Computer Science, Mathematics, or a related technical track. * Have 5+ years of programming experience and a strong math background. * Are willing and able to deliver operational solutions within business constraints. * Have demonstrated experience in at least three of the following areas: software development, development using 3D game engine, back-end development, machine learning, natural language processing and translation, knowledge representation and reasoning with evidence, synthetic data generation. * Hold an active [Secret or Top Secret] security clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship. * Are a U.S. Citizen with the ability to obtain a Department of Defense security clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship. **You'll go above and beyond our minimum requirements if you...** * Have a Master's or PhD degree in Computer Science or a related field and 10+ years of relevant experience. * Possess 2+years of experience performing development using a graphics engine such as Unity3D, Unreal Engine, Blender or Maya. * Have 2+ years of experience applying machine learning to data science or artificial intelligence. * Are experienced in developing Augmented / Virtual Reality solutions. * Are experienced with team-based development of software products and are able to lead development and research projects. * Have experience with machine learning libraries such as Tensorflow, Keras, Caffe, MXNet, CNTK, and scikit-learn, and are familiar with modern databases and parallel computation. * Possess a current DoD security clearance. **Why work at APL?** The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation’s most critical defense, security, space and science challenges. With a wide selection of challenging, impactful work and a robust education assistance program, APL promotes a culture of life-long learning. Our employees enjoy generous benefits and healthy work/life balance. APL’s campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at www.jhuapl.edu/careers. APL is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, disability status, veteran status, or any other characteristic protected by applicable law. *Primary Location:* *United States-*Maryland-*Laurel *Schedule:* Full-time *Req ID:* 18517
          Director, Data Science, Sub-Saharan Africa, Visa      Cache   Translate Page      
Visa - Johannesburg, Gauteng - Science is a lead Data Scientist role in the Sub-Saharan Africa (SSA) team. We are seeking an innovative and analytical thinker to champion...-related processes that will promote fact-based decisioning processes Help set strategic direction and the roadmap for the Data Science group in...
          Data Scientist* (Maplewood, MN) - 3M - Maplewood, MN      Cache   Translate Page      
3M is seeking a Data Scientist for the Industrial Business Group located in Maplewood, MN. Collaborate with business teams and across the analytics team to...
From 3M - Wed, 20 Feb 2019 17:09:33 GMT - View all Maplewood, MN jobs
          F. Hoffmann-La Roche AG: Data Scientist, PHC Analytics (Neuroscience focus)      Cache   Translate Page      
F. Hoffmann-La Roche AG: As a data scientist within our Personalized Health Care function you will work with partners throughout the global organisation to use meaningf Switzerland (CH)
          Junior Project Manager - Data & Insights Group - CBS Interactive - Fort Lauderdale, FL      Cache   Translate Page      
The candidate will be a key member of the Data and Insights Group made up of data scientists, business analysts, data engineers and researchers....
From CBS Sports Network - Sat, 02 Mar 2019 02:40:12 GMT - View all Fort Lauderdale, FL jobs
          Data Scientist - Intermediate      Cache   Translate Page      
MO-Saint Louis, Infomatics, Inc is a leading provider of next generation technology consulting and staff augmentation services to our client through offices in the US, India and the Middle-east. We have been rated for four years in a row by Inc-500/5000 as one of the fastest growing private companies in the US. Our clientele includes many Fortune 500 enterprises across the country. One such client has an immediat
           Курс: Data Science. Уровень1. Инструменты и технологии       Cache   Translate Page      

Preview_75bd8a64d0

8 дней
/
15 990
По мере набора

Этот курс – руководство для тех, кто хочет на практике освоить возможности Data Science и познакомиться с технологией машинного обучения. На занятиях вы на конкретном примере разберете, как происходит обработка и анализ больших данных, а затем визуализируете полученный результат.

Курс состоит из 9 модулей:

  1. Постановка задачи
  2. Классический подход
  3. DataScience
  4. Подготовка исходных данных
  5. Построение аналитической модели
  6. Оценка аналитической модели
  7. Визуализация данных
  8. Основные инструменты анализа данных
  9. Дополнительные инструменты и технологии

    После курса вы сможете:

    • использовать язык R для решения задач класса Data Science;
    • подготавливать данные для анализа;
    • визуализировать результаты анализа.

              Comment on How to Choose the Data Science Program That’s Right for You by Pranay Mehta      Cache   Translate Page      
    I have 9.5 yrs of experience comprising of backoffice opeartions but interested to get into Analytics. I have no prior experience in analytics and no knowledge of Alteryx, python or SQL. I wanted to understand if it would help me in finding right opportunities by taking business analytics course at udacity with number of years of experience I have not in analytics field. If so, what kind of industry roles can i expect?
              Columbia Team develops treatments for depression      Cache   Translate Page      
    (Data Science Institute at Columbia) Depression is a debilitating illness that affects more than 350 million people. About half of the people who take antidepressants, however, do not respond to the treatment. This team is thus trying to understand the molecular mechanisms of such treatment resistance. Ultimately, they would like to be able to predict which people will respond to antidepressant drugs before they begin treatment, and to develop treatments that can circumvent antidepressant resistance in the millions of people who do not respond to antidepressants.
              IoT Anomaly Detection 101: Data Science to Predict the Unexpected       Cache   Translate Page      
    Yes! You can predict the chance of a mechanical failure or security breach before it happens. Part one of a two-part series.
              Lecturer - Data Science - University of Wisconsin- Green Bay - Green Bay, WI      Cache   Translate Page      
    The Austin E. Cofrin School of Business at the University of Wisconsin-Green Bay seeks applicants for a Lecturer position in Data Science and Business...
    From University of Wisconsin- Green Bay - Tue, 18 Dec 2018 17:39:58 GMT - View all Green Bay, WI jobs
              Assistant Professor - Data Science & Business Analytics - University of Wisconsin- Green Bay - Green Bay, WI      Cache   Translate Page      
    The Austin E. Cofrin School of Business at the University of Wisconsin – Green Bay seeks applicants for a tenure-track position in Data Science and Business...
    From University of Wisconsin- Green Bay - Mon, 26 Nov 2018 17:39:30 GMT - View all Green Bay, WI jobs
              Data Scientist - WVU Medicine - Morgantown, WV      Cache   Translate Page      
    JOB TITLE & CODE: Data Scientist (87928) DEPARTMENT: Strategic Analytics REPORTS TO: AVP FLSA STATUS: Exempt POSITION SUMMARY: Manages, coordinates,...
    From WVU Medicine - Fri, 22 Feb 2019 23:45:36 GMT - View all Morgantown, WV jobs
              NCEAS Portrait: Julie Lowndes Wants the Force of Open Data Science to Be with You      Cache   Translate Page      

    Julie Lowndes likens “open data science” to the Force (yes, as in Star Wars), a penetrating energy that empowers scientists to wield their data more quickly and efficiently than they ever could before. In this NCEAS Portrait, she explains how the mentorship program in open data science she just launched, Openscapes, will help empower early career environmental scientists and improve their science.

    More>

     


              Want to manage your total cloud costs better? Emphasize the ‘Ops’ in DevOps, says Futurum analyst Daniel Newman      Cache   Translate Page      

    The next BriefingsDirect Voice of the Analyst interview explores new ways that businesses can gain the most control and economic payback from various cloud computing models.

    We’ll now hear from an IT industry analyst on how developers and IT operators can find newfound common ground to make hybrid cloud the best long-term economic value for their organizations.

    Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

    Here to help explore ways a managed and orchestrated cloud lifecycle culture should be sought across enterprise IT organizations is Daniel Newman, Principal Analyst and Founding Partner at Futurum Research. The interview is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

    Here are some excerpts:

    Gardner: Daniel, many tools have been delivered over the years for improving software development in the cloud. Recently, containerization and management of containers has been a big part of that.

    Now, we’re also seeing IT operators tasked with making the most of cloud, hybrid cloud, and multi-cloud around DevOps – and they need better tools, too.

    Has there been a divide or lag between what developers have been able to do in the public cloud environment and what operators must be able to do? If so, is that gap growing or shrinking now that new types of tools for automation, orchestration, and composability of infrastructure and cloud services are arriving?

    Out of the shadow, into the cloud 

    Newman: Your question lends itself to the concept of shadow IT. The users of this shadow IT find a way to get what they need to get things done. They have had a period of uncanny freedom.

    Newman
    But this has led to a couple of things. First of all, generally nobody knows what anybody else is doing within the organization. The developers have been able to creatively find tools.

    On the other hand, IT has been cast inside of a box. And they say, “Here is the toolset you get. Here are your limitations. Here is how we want you to go about things. These are the policies.”

    And in the data center world, that’s how everything gets built. This is the confined set of restrictions that makes a data center a data center.

    But in a developer’s world, it’s always been about minimum viable product. It’s been about how to develop using tools that do what they need them to do and getting the code out as quickly as possible. And when it’s all in the cloud, the end-user of the application doesn’t know which cloud it’s running on, they just know they’re getting access to the app.

    Basically we now have two worlds colliding. You have a world of strict, confined policies -- and that’s the “ops” side of DevOps. You also have the developers who have been given free rein to do what they need to do; to get what they need to get done, done.

    Get Dev and Ops to collaborate 

    Gardner: So, we need to keep that creativity and innovation going for the developers so they can satisfy their requirements. At the same time, we need to put in guard rails, to make it all sustainable.

    Otherwise we see not a minimal viable cloud – but out-of-control expenses, out-of-control governance and security, and difficulty taking advantage of both private cloud and public cloud, or a hybrid affair, when you want to make that choice.

    How do we begin to make this a case of worlds collaborating instead of worlds colliding?

    Newman: It’s a great question. We have tended to point DevOps toward “dev.” It’s really been about the development, and the “ops” side is secondary. It’s like capital D, lowercase o.

    The thing is, we’re now having a massive shift that requires more orchestration and coordination between these groups.
    How to Make
    Hybrid IT
    Simple
    You mentioned out-of-control expenses. I spoke earlier about DevOps and developers having the free rein – to do what they need to do, put it where they need to put it, containers, clouds, tools, whatever they need, and just get it out because that’s what impacts their customers.

    If you have an application where people buy things on the web and you need to get that app out, it may be a little more expensive to deploy it without the support of Ops, but you feel the pressure to get it done quickly.

    Now, Ops can come in and say, “Well, you know … what about a flex consumption-based model, what about multi-cloud, what about using containers to create more portability?”

    “What if we can keep it within the constraints of a budget and work together with you? And, by the way, we can help you understand which applications are running on which cloud and provide you the optimal [aggregate cloud use] plan.”

    Let’s be very honest, a developer doesn’t care about all of that. ... They are typically not paid or compensated in any way that leads to optimizing on cost. That’s what the Ops people do.

    Such orchestration -- just like almost all larger digital transformation efforts -- starts when you have shared goals. The problem is, they call it a DevOps group -- but Dev has one set of goals and Ops has different ones.

    What you’re seeing is the need for new composable tools for cloud services, which we saw at such events as the recent Hewlett Packard Enterprise (HPE) Discover conference. They are launching these tools, giving the Ops people more control over things, and -- by the way -- giving developers more visibility than has existed in the past.
    There is a big opportunity [for better cloud use economics] through better orchestration and collaboration, but it comes down to the age-old challenges of having the Dev and Ops people share the same goals.

    There is a big opportunity [for better cloud use economics] through better orchestration and collaboration, but it comes down to the age-old challenges inside of any IT organization -- and that is having the Dev and the Ops people share the same goals. These new tools may give them more of a reason to start working in that way.

    Gardner: The more composability the operations people have, the easier it is for them to define a path that the developers can stay inside of without encumbering the developers.

    We may be at the point in the maturity of the industry where both sides can get what they want. It’s simply a matter of putting that together -- the chocolate and peanut-butter, if you will. It becomes more of a complete DevOps.

    But there is another part of this people often don’t talk about, and that’s the data placement component. When we examine the lifecycle of a modern application, we’re not just developing it and staging it where it stays static. It has to be built upon and improved, we are doing iterations, we are doing Agile methods.

    We also have to think about the data the application is consuming and creating in the same way. That dynamic data use pattern needs to fit into a larger data management philosophy and architecture that includes multi-cloud support.

    I think it’s becoming DevDataOps-- not just DevOps these days. The operations people need to be able to put in requirements about how that data is managed within the confines of that application’s deployment, yet kept secure, and in compliance with regulations and localization requirements.

    DevDataOps emerges

    Newman: We’ve launched the DevDataOps category right now! That’s actually a really great point, because if you think about where does all that live -- meaning IT orchestration of the infrastructure choices and whether that’s in the cloud or on-premises – there has to be enough of the right kind of storage.

    Developers are usually worried about data from the sense of what can they do with that data to improve and enhance the applications. When you add in elements like machine learning (ML) and artificial intelligence (AI), that’s going to just up the compute and storage requirements. You have the edge and Internet of Things (IoT) to consider now too for data. Most applications are collecting more data in real-time. With all of these complexities, you have to ask, “Who really owns this data?”

    Well, the IT part of DevOps, the “Ops,” typically worries about capacity and resources performance for data. But are they really worried about the data in these new models? It brings in that needed third category because the Dev person doesn’t necessarily deal with the data lifecycle. The need to best use that data is a business unit imperative, a marketing-level issue, a sales-level data requirement. It can include all the data that’s created inside of a cloud instance of SAP or Salesforce.
    How to Solve Cost
    and Utilization Challenges
    of Hybrid Cloud
    Just think about how many people need to be involved in orchestration to maximize that? Culturally speaking, it goes back to shared tools, shared visibility, and shared goals. It’s also now about more orchestration required across more external groups. So your DevOps group just got bigger, because the data deluge is going to be the most valuable resource any company has. It will be, if it isn’t already today, the most influential variable in what your company becomes.

    You can’t just leave that to developers and operators of IT. It becomes core to business unit leadership, and they need to have an impact. The business leadership should be asking, “We have all this data. What are we doing with it? How are we managing it? Where does it live? How do we pour it between different clouds? What stays on-premises and what goes off? How do we govern it? How can we have governance over privacy and compliance?”

    I would say most companies really struggle to keep up with compliance because there are so many rules about what kind of data you have, where it can live, how it should be managed, and how long it should be stored.


    I think you bring up a great point, Dana. I could probably rattle on about this for a long, long time. You’ve just added a whole new element to DevOps, right here on this podcast. I don’t know that it has to do with specifically Dev or Ops, but I think it’s Dev+Ops+Data -- a new leadership element for meaningful digital transformation.

    Gardner: We talked about trying to bridge the gap between development and Ops, but I think there are other gaps, too. One is between data lifecycle management – for backup and recovery and making it the lowest cost storage environment, for example. Then there is the other group of data scientists who are warehousing that data, caching it, and grabbing more data from outside, third-party sources to do more analytics for the entire company. But these data strategies are too often still divorced.

    These data science people and what the developers and operators are doing aren’t necessarily in sync. So, we might have another category, which would be Dev+Data+DataScience+Ops.

    Add Data Analytics to the Composition 

    Newman: Now we’re going four groups. You are firstly talking about the data from the running applications. That’s managed through pure orchestration in DevOps, and that works fine through composability tools. Those tools provide IT the capability to add guard rails to the developers, so they are not doing things in the shadows, but instead do things in coordination.

    The other data category is that bigger analytical data. It includes open data, third-party data, and historical data that’s been collected and stored inside of instances of Enterprise resource planning (ERP) apps and Customer-relationship management (CRM) apps for 20 or 30 years. It’s a gold mine of information. Now we have to figure out an extract process and incorporate that data into almost every enterprise-level application that developers are building. Right now Dev and Ops don’t really have a clue what is out there and available across that category because that’s being managed somewhere else, through an analytics group of the company.

    Gardner: Or, developers will have to create an entirely different class of applications for analytics alone, as well as integrating the analytics services into all of the existing apps.

    Newman: One of the HPE partners I’ve worked with the in the past, SAS, and companies such as SAS and SAP, are going to become much closer aligned with infrastructure. Your DevOps is going to become your analytics Ops, too.
    How to Achieve
    Composability
    Across Your Data Center
    Hardware companies have built software apps to run their hardware, but they haven’t been historically building software apps to run the data that sits on the hardware. That’s been managed by the businesses running business intelligence software, such as the ones I mentioned.

    There is an opportunity for a new level of coordination to take place at the vendor level, because when you see these alliances, and you see these partnerships, this isn’t new. But, seeing it done in a way that’s about getting the maximum amount of usable data from one system into every application -- that’s futuristic, and it needs to be worked on today.

    Gardner: The bottom line is that there are many moving parts of IT that remain disjointed. But we are at the point now with composability and automation of getting an uber-view over services and processes to start making these new connections – technically, culturally, and organizationally.

    What I have seen from HPE around the HPE Composable Cloud vision moves a big step in that direction. It might be geared toward operators, but, ultimately it’s geared toward the entire enterprise, and gives the business an ability to coordinate, manage, and gain insights into all these different facets of a digital business.
    Companies right now still struggle with the resources to run multi-cloud. They tend to have maybe one public cloud and their on-premises operations. They don't know which is the best cloud approach because they are not getting the total information.

    Newman: We’ve been talking about where things can go, and it’s exciting. But let’s take a step back.

    Multi-cloud is a really great concept. Hyper-converged infrastructure, it’s all really nice, and there has been massive movement in this area in the last couple of years. Companies right now still struggle with the resources to run multi-cloud. They tend to have maybe one public cloud and their on-premise operations. They have their own expertise, and they have endless contracts and partnerships.

    They don’t know which the best-cloud approach is because they are not necessarily getting that total information. It depends on all of the relationships, the disparate resources they have across Dev and Ops, and the data can change on a week-to-week basis. One cloud may have been perfect a month ago, yet all of a sudden you change the way an application is running and consuming data, and it’s now in a different cloud.


    What HPE is doing with HPE Composable Cloud takes the cloud plus composable infrastructure and, working through HPE OneSphere and HPE OneView, brings them all into a single view. We’re in a software and user experience world.

    The tools that deliver the most usable and valuable dashboard-type of cloud use data in one spot are going to win the battle. You need that view in front of you for quick deployment, with quick builds, portability, and container management. HPE is setting itself in a good position for how we do this in one place.
    How to Remove
    Complexity From
    Multi-Cloud and Hybrid IT
    Give me one view, give me my one screen to look at, and I think your Dev and Ops -- and everybody in between – and all your new data and data science friends will all appreciate that view. HPE is on a good track, and I look forward to seeing what they do in the future.


              Senior Data Scientist - Predictive Enterprise Group - Neudesic LLC - Philadelphia, PA      Cache   Translate Page      
    Machine Learning Solutions:. The explosion of big data, machine learning and cloud computing power creates an opportunity to make a quantum leap forward in...
    From Neudesic LLC - Sat, 15 Dec 2018 21:58:12 GMT - View all Philadelphia, PA jobs
              Determined AI, which wants to make AI development easier using its software, exits stealth and announces $11M Series A led by GV (Ron Miller/TechCrunch)      Cache   Translate Page      

    Ron Miller / TechCrunch:
    Determined AI, which wants to make AI development easier using its software, exits stealth and announces $11M Series A led by GV  —  Deep learning involves a highly iterative process where data scientists build models and test them on GPU-powered systems until they get something they can work with.


              Determined AI nabs $11M Series A to democratize AI development      Cache   Translate Page      
    Deep learning involves a highly iterative process where data scientists build models and test them on GPU-powered systems until they get something they can work with. It can be expensive and time-consuming, often taking weeks to fashion the right model. Determined AI, a new startup wants to change that by making the process faster, cheaper […]
              Statistician Technician - WEST Inc - Cheyenne, WY      Cache   Translate Page      
    Experience with other data science toolkits (Python, C#, JavaScript, etc.). Western EcoSystems Technology, Inc.... $18 - $22 an hour
    From WEST Inc - Tue, 29 Jan 2019 11:41:19 GMT - View all Cheyenne, WY jobs
              At the Hospitals: Norwich Resident to Helm Northern Counties Health Care; Geisel Announces Department Chairs – Valley News      Cache   Translate Page      
    Read article - A series of news briefs which mention that Ilana Cass has been appointed the chair of the Department of Obstetrics and Gynecology, and that Michael Whitfield has been named the chair of the Department of Biomedical Data Science. (Similar coverage in New Hampshire Union Leader.)
              Data Scientist - Oliver Wyman - New York, NY      Cache   Translate Page      
    Demonstrate solid and battle-tested understanding of the standard canon of machine learning practices, including but not limited to:....
    From Marsh & McLennan Companies - Sat, 05 Jan 2019 15:04:15 GMT - View all New York, NY jobs
              Senior Quality Control Auditor, Oncology (Contract, Home-Based) - RSS (R1066660)      Cache   Translate Page      
    Position description Join us on our exciting journey IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find better solutions for their patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a wide spectrum of solutions that harness advances in healthcare details, technology, analytics and human ingenuity to drive healthcare forward. ESSENTIAL JOB FUNCTIONS, DUTIES AND RESPONSIBILITIES: Primarily responsible for quality control processes relating to customer deliverables. Provide QC support for the Biostatistics, Clinical Programming, Data Management, Info Technology, SAS programming groups Provide final QC review of all study documents and other client documents (e.g., database QC, Inform QC, data management specifications, SAS output) before they are delivered. Assist with the evaluation of quality control processes. Assure process compliance with all regulatory and Novella SOPs.
              Senior Quality Control Auditor, Oncology (Contract, Home-Based) - RSS (R1066660)      Cache   Translate Page      
    Job overview Join us on our exciting journey IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find better solutions for their patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a broad spectrum of solutions that harness advances in healthcare information, technology, analytics and human ingenuity to drive healthcare forward. ESSENTIAL JOB FUNCTIONS, DUTIES AND RESPONSIBILITIES: Primarily responsible for quality control processes relating to customer deliverables. Provide QC support for the Biostatistics, Clinical Programming, Data Management, Info Technology, SAS programming groups Provide final QC review of all study documents and other client documents (e.g., database QC, Inform QC, data management specifications, SAS output) before they are delivered. ist with the evaluation of quality control processes. ure process compliance with all regulatory and Novella SOPs.
              Clinical Data Manager (Office-Based) - RSS (R1066233)      Cache   Translate Page      
    Job summary Join us on our exciting journey IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find better solutions for their patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a wide spectrum of solutions that harness advances in healthcare details, technology, analytics and human ingenuity to drive healthcare forward. Client seeking a Clinical Data Manager in the San Francisco, CA area. Position is Office-based Required responsibilities: Outstanding expertise to collect, maintain, validate and manage clinical data Manages data management timelines to coordinate and synchronize deliverables with the overall study timelines Adept at Electronic Data Capture (EDC) system management Strong understanding of regulatory procedures and guidelines Extensive background with Protocol review; SOPs, DOPs, Training Guidelines, Data Management Highly skilled in Electronic Data Capture (EDC) data management
              Clinical Project Manager (Contract, Home-Based) - RSS (R1056737)      Cache   Translate Page      
    Position description Join us on our exciting journey IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find better solutions for their patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a broad spectrum of solutions that harness advances in healthcare details, technology, analytics and human ingenuity to drive healthcare forward. Job overview Accountable for all aspects of the management and clinical execution of early phase clinical trials within Translational Medicine (TM). This role leads the planning and implementation of all operational aspects of TM clinical trials from study concept to reporting according to timelines, budget, operational and quality standards (ICH/GCP/SOPs and procedures). Major Accountabilities Clinical Scientist for Phase I/II including multi-country / multi-center trials. The main focus will be on high complexity studies leading to clinical Proof-of-Concept or NDA registration.
              Clinical Study Coordinator, Oncology/Device (Utrecht - Contract) - RSS      Cache   Translate Page      
    Position details Join us on our exciting journey IQVIA™ is The Human Data Science Company™, focused on using data and science to help healthcare clients find better solutions for their patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a wide range of solutions that harness advances in healthcare details, technology, analytics and human ingenuity to drive healthcare forward. Novella Clinical Resourcing, a Quintiles company, is a full service, Global recruitment agency with European headquarters in Stevenage, UK. We are recruiting for a Medical Device client who is looking to support 1 of their important trials in UMC, Utrecht. It is a global registry, Oncology device study and there is a need for a part-time Study Coordinator to support the site. This is an excellent opportunity to gain further Medical Device Clinical trial background and work in a freelance capacity. This opportunity will be a adjustable contract position to work 6-8 hours per week at the UMC Hospital.
              Determined AI nabs $11M Series A to democratize AI development      Cache   Translate Page      
    Deep learning involves a highly iterative process where data scientists build models and test them on GPU-powered systems until they get something they can work with. It can be expensive and time-consuming, often taking weeks to fashion the right model. New startup Determined AI wants to change that by making the process faster, cheaper and […]
              Modern Marketing Blog Influencer Series: Improving Marketing Performance with Analytics ...      Cache   Translate Page      

    “The Modern Marketing Influencer Blog Series asked top influencers from across the marketing spectrum what’s on their minds and what topics and pressing issues in their fields they feel are begging for more insight. Here they share their thoughts on data, the ever-changing field of marketing, and how it all comes together.

    Analytics has become a common component of marketing strategy and planning. On any given day, thousands of marketing managers are asking questions like:

    • How is the social campaign performing?
    • What’s the analysis of the first-touch open rates?
    • What websites are our key demographic likely to use in the next month?

    A growing number of stand-alone software solutions—as well as new capabilities within marketing automation platforms (MAPs) and customer-relationship management systems (CRMs)—are making it easier for marketing professionals to use analytics.

    This is good news because marketers are overwhelmed by the amount of data they have, and the pace of new data is only going to accelerate. Proper analytics strategy and execution can turn that data into new insights about buyer behavior, marketing program performance, sales activity, and business impact.

    Because of these new tools and general excitement about what’s possible with analytics, it might seem as if marketers are quickly maturing in this area. But the truth is, marketing remains in its analytics infancy.

    Analytics Possibilities and Roadblocks

    I am excited about what analytics could do for marketing. Anyone who reads my blog, newsletter, or data experiments like the Cheese of the Week forecast or has seen me speak at industry events knows this.

    One example of what’s possible with analytics is intelligent time-series forecasting. With this, marketing teams would know when content about specific products and services is most likely to be found by buyers via search. The Cheese of the Week forecast is a silly but functional example of this: It uses five years of search data and proven algorithms to forecast when cheese enthusiasts will be searching for a particular type of cheese over the next 52 weeks.

    If we were building a 52-week editorial calendar for cheese-related content, this insight certainly would improve the chances of our content leading to a cheese purchase. Content development, placement, timing, amplification—everything we do—would all be more effective with this insight. Obviously, you’d use this strategy and technology with your own data; this is merely an example of what’s possible and in market now.

    Unfortunately, intelligent forecast is not common in marketing. Why not? Primarily because it requires true machine learning, and marketing professionals generally are not machine learning experts. If there is not a shared resource to tap, they don’t know where to start and can’t clearly articulate the ROI of investing in machine learning to internal stakeholders. Machine learning has enormous potential to help marketers be more successful, but what executive is going to take a risk with something that is outside of the traditional marketing-skills wheelhouse?

    This is one of the major roadblocks in analytics maturation in marketing: lack of education and training about groundbreaking advances in technology, including analytics. On average, 3.9% of a marketing team’s budget is devoted to training and education, according to the August 2018 CMO Survey, a survey of 2,895 marketing executives that is conducted twice a year. In the same survey taken in February of 2014, the average was 3.4%—so a virtually flat 4-year trend yet consider how data and technology has changed marketing during those 4 years.

    The other major roadblock is a lack of integration of separated data sets and digital workflows. While it can be beneficial to analyze data at specific points within the buyer’s journey or a marketing stack or program, the highest value of analytics comes from more sophisticated models that can be predictive and prescriptive. That is, they tell you not just when buyers will be searching for what, but also alert you to change your marketing mix in response to changes in sales. Conducting this level of analysis requires real-time integration of marketing and e-commerce data.

    The impact of these two roadblocks is evident in another statistic from the CMO survey: Marketing leaders use marketing analytics for decision making only 35.8% of the time—that’s only about 1 in 3 decisions. (It’s worth noting that this average has actually fallen from a high of 42.1% in February of 2018.)

    Going Forward and Getting Better

    Clearly, marketing is on the right path with analytics, but as my observations and the CMO Survey data demonstrate, the steps have been small, and so the relative impact is small. That doesn’t mean doing more isn’t possible. It is, but it takes commitment to invest in education and training, as well as technology planning that elevates analytics capabilities.

    Fortunately, marketing technology is evolving quickly, and that should accelerate advancement. For example, developer applications, such as Oracle Advanced Analytics 12c and Oracle R Enterprise, now make it easier to build and deploy machine learning in enterprise applications, including MAPs and CRMs. This means marketing teams with access to developers should have an easier time building predictive analytics applications.

    Vendors also are starting to build in integrations, such as the integration of DemandBase’s context-aware content recommendations in Oracle Marketing Cloud. This is a trend that is likely to continue, and as it does, it will eliminate the need to manually integrate data and build custom applications, which is the main choice available to many marketers right now.

    Another consideration is all of the untapped territory for “analytics solutions,” i.e., long-standing marketing challenges begging for a fix. Think about marketing and sales attribution. As more data sets are merged for analysis—operational, sales, audience, etc.—more insight on the impact of individual actions will be gleaned, and it will be easier to understand where to assign attribution. When this happens, it will be an enormous benefit for marketing leaders because pressure to demonstrate the top-line value of marketing is relentless. Right now, due to the complexity of the computations, true attribution analysis is the purview of only a select few data scientists, typically at larger companies.

    The big takeaway here is that although marketing is still comparatively young in analytics maturity, it’s certain that analytics will be a bigger part of marketing in the future. To be ahead of that trend and gain the most possible benefit, marketing leaders will need to commit to learning more about analytics and choosing technology that can easily evolve to match the current analytics best practices.

    See data in action as it makes a difference in how a company helps its customers with “eharmony Uses AI to Help Their Users Find Love.”

    Read the blog

     


              Oil, Gas & Chemicals Data Science Manager - Deloitte - McLean, VA      Cache   Translate Page      
    Storm, Spark, Flume, HDFS (Cloudera, Hortonworks, MapR, IBM Big Insights, Pivotal HD), PIG, HIVE, Scala, HCatalog, Ambari tied to mixed workload processing for...
    From Deloitte - Fri, 04 Jan 2019 04:53:39 GMT - View all McLean, VA jobs
              Offer - SSAS Best Online Training @ SQL School - AUSTRIA      Cache   Translate Page      
    SQL School is one of the best training institutes for Microsoft SQL Server Developer Training, SQL DBA Training, MSBI Training, Power BI Training, Azure Training, Data Science Training, Python Training, Hadoop Training, Tableau Training, Machine Learning Training, Oracle PL SQL Training. We have been providing Classroom Training, Live-Online Training, On Demand Video Training and Corporate trainings. All our training sessions are COMPLETELY PRACTICAL. SQL Server Analysis Services : Features of our Training: • Completely Practical • Completely Real time • Highly Interactive • Real time Case Studies • Interview Guidance • Certification Guidance • Mock Interviews • Job Support All Sessions are Completely Practical and Realtime. For free SSAS Online Demo, please visit : http://sqlschool.com/SSAS-Online-Training.html Schedules for PRACTICAL SQL 2016 & 2017 SSAS Online TRAINING : http://sqlschool.com/Register.html Contact us today (24 x 7) for SSAS Practical Online Training SQL School Training Institute ISO 9001:2008 Certified Organization for Training Authorized Microsoft Partner (ID# 5108842) India: Mobile: +91 (0) 9666 44 0801 Mobile: +91 (0) 9666 64 0801 USA: Office: +1 (510) 400-4845 Office 1: #101, UMA Residency, Opp: Sindhu Travels, Beside Metro Station Gate #D, SR Nagar, Hyderabad - 38, India. Website: http://sqlschool.com/ Follow us: https://www.facebook.com/sequelschool https://www.linkedin.com/company/sql-school https://twitter.com/sequelschool
              Determined AI nabs $11M Series A to democratize AI development      Cache   Translate Page      
    Deep learning involves a highly iterative process where data scientists build models and test them on GPU-powered systems until they get something they can work with. It can be expensive and time-consuming, often taking weeks to fashion the right model. New startup Determined AI wants to change that by making the process faster, cheaper and […]
              Cortex Logic CEO awarded AI Leader Of The Year      Cache   Translate Page      
    South African based Artificial Intelligence Software & Solutions veteran and founder of Cortex Logic, awarded premium accolade at Africa’s Tech Week event, underlying a life dedicated to AI and Data Science Innovation. Dr Ludik is an African based smart technology entrepreneur and Artificial Intelligence investor / AI ecosystem builder, holds a PhD in Computer Science [&hellip
              Lenovo Intelligent Computing Orchestration Simplifying HPC and AI Model Development – Intel on AI – Episode 07      Cache   Translate Page      

    In this Intel on AI podcast episode: Dr. David Ellison, the Senior Artificial Intelligence Data Scientist in the HPC Business Unit at Lenovo joins Intel on AI to discuss the Lenovo Intelligent Computing Orchestration (LiCO). Dr. Ellison explains how [See the full post…]




    Next Page: 10000

    Site Map 2018_01_14
    Site Map 2018_01_15
    Site Map 2018_01_16
    Site Map 2018_01_17
    Site Map 2018_01_18
    Site Map 2018_01_19
    Site Map 2018_01_20
    Site Map 2018_01_21
    Site Map 2018_01_22
    Site Map 2018_01_23
    Site Map 2018_01_24
    Site Map 2018_01_25
    Site Map 2018_01_26
    Site Map 2018_01_27
    Site Map 2018_01_28
    Site Map 2018_01_29
    Site Map 2018_01_30
    Site Map 2018_01_31
    Site Map 2018_02_01
    Site Map 2018_02_02
    Site Map 2018_02_03
    Site Map 2018_02_04
    Site Map 2018_02_05
    Site Map 2018_02_06
    Site Map 2018_02_07
    Site Map 2018_02_08
    Site Map 2018_02_09
    Site Map 2018_02_10
    Site Map 2018_02_11
    Site Map 2018_02_12
    Site Map 2018_02_13
    Site Map 2018_02_14
    Site Map 2018_02_15
    Site Map 2018_02_15
    Site Map 2018_02_16
    Site Map 2018_02_17
    Site Map 2018_02_18
    Site Map 2018_02_19
    Site Map 2018_02_20
    Site Map 2018_02_21
    Site Map 2018_02_22
    Site Map 2018_02_23
    Site Map 2018_02_24
    Site Map 2018_02_25
    Site Map 2018_02_26
    Site Map 2018_02_27
    Site Map 2018_02_28
    Site Map 2018_03_01
    Site Map 2018_03_02
    Site Map 2018_03_03
    Site Map 2018_03_04
    Site Map 2018_03_05
    Site Map 2018_03_06
    Site Map 2018_03_07
    Site Map 2018_03_08
    Site Map 2018_03_09
    Site Map 2018_03_10
    Site Map 2018_03_11
    Site Map 2018_03_12
    Site Map 2018_03_13
    Site Map 2018_03_14
    Site Map 2018_03_15
    Site Map 2018_03_16
    Site Map 2018_03_17
    Site Map 2018_03_18
    Site Map 2018_03_19
    Site Map 2018_03_20
    Site Map 2018_03_21
    Site Map 2018_03_22
    Site Map 2018_03_23
    Site Map 2018_03_24
    Site Map 2018_03_25
    Site Map 2018_03_26
    Site Map 2018_03_27
    Site Map 2018_03_28
    Site Map 2018_03_29
    Site Map 2018_03_30
    Site Map 2018_03_31
    Site Map 2018_04_01
    Site Map 2018_04_02
    Site Map 2018_04_03
    Site Map 2018_04_04
    Site Map 2018_04_05
    Site Map 2018_04_06
    Site Map 2018_04_07
    Site Map 2018_04_08
    Site Map 2018_04_09
    Site Map 2018_04_10
    Site Map 2018_04_11
    Site Map 2018_04_12
    Site Map 2018_04_13
    Site Map 2018_04_14
    Site Map 2018_04_15
    Site Map 2018_04_16
    Site Map 2018_04_17
    Site Map 2018_04_18
    Site Map 2018_04_19
    Site Map 2018_04_20
    Site Map 2018_04_21
    Site Map 2018_04_22
    Site Map 2018_04_23
    Site Map 2018_04_24
    Site Map 2018_04_25
    Site Map 2018_04_26
    Site Map 2018_04_27
    Site Map 2018_04_28
    Site Map 2018_04_29
    Site Map 2018_04_30
    Site Map 2018_05_01
    Site Map 2018_05_02
    Site Map 2018_05_03
    Site Map 2018_05_04
    Site Map 2018_05_05
    Site Map 2018_05_06
    Site Map 2018_05_07
    Site Map 2018_05_08
    Site Map 2018_05_09
    Site Map 2018_05_15
    Site Map 2018_05_16
    Site Map 2018_05_17
    Site Map 2018_05_18
    Site Map 2018_05_19
    Site Map 2018_05_20
    Site Map 2018_05_21
    Site Map 2018_05_22
    Site Map 2018_05_23
    Site Map 2018_05_24
    Site Map 2018_05_25
    Site Map 2018_05_26
    Site Map 2018_05_27
    Site Map 2018_05_28
    Site Map 2018_05_29
    Site Map 2018_05_30
    Site Map 2018_05_31
    Site Map 2018_06_01
    Site Map 2018_06_02
    Site Map 2018_06_03
    Site Map 2018_06_04
    Site Map 2018_06_05
    Site Map 2018_06_06
    Site Map 2018_06_07
    Site Map 2018_06_08
    Site Map 2018_06_09
    Site Map 2018_06_10
    Site Map 2018_06_11
    Site Map 2018_06_12
    Site Map 2018_06_13
    Site Map 2018_06_14
    Site Map 2018_06_15
    Site Map 2018_06_16
    Site Map 2018_06_17
    Site Map 2018_06_18
    Site Map 2018_06_19
    Site Map 2018_06_20
    Site Map 2018_06_21
    Site Map 2018_06_22
    Site Map 2018_06_23
    Site Map 2018_06_24
    Site Map 2018_06_25
    Site Map 2018_06_26
    Site Map 2018_06_27
    Site Map 2018_06_28
    Site Map 2018_06_29
    Site Map 2018_06_30
    Site Map 2018_07_01
    Site Map 2018_07_02
    Site Map 2018_07_03
    Site Map 2018_07_04
    Site Map 2018_07_05
    Site Map 2018_07_06
    Site Map 2018_07_07
    Site Map 2018_07_08
    Site Map 2018_07_09
    Site Map 2018_07_10
    Site Map 2018_07_11
    Site Map 2018_07_12
    Site Map 2018_07_13
    Site Map 2018_07_14
    Site Map 2018_07_15
    Site Map 2018_07_16
    Site Map 2018_07_17
    Site Map 2018_07_18
    Site Map 2018_07_19
    Site Map 2018_07_20
    Site Map 2018_07_21
    Site Map 2018_07_22
    Site Map 2018_07_23
    Site Map 2018_07_24
    Site Map 2018_07_25
    Site Map 2018_07_26
    Site Map 2018_07_27
    Site Map 2018_07_28
    Site Map 2018_07_29
    Site Map 2018_07_30
    Site Map 2018_07_31
    Site Map 2018_08_01
    Site Map 2018_08_02
    Site Map 2018_08_03
    Site Map 2018_08_04
    Site Map 2018_08_05
    Site Map 2018_08_06
    Site Map 2018_08_07
    Site Map 2018_08_08
    Site Map 2018_08_09
    Site Map 2018_08_10
    Site Map 2018_08_11
    Site Map 2018_08_12
    Site Map 2018_08_13
    Site Map 2018_08_15
    Site Map 2018_08_16
    Site Map 2018_08_17
    Site Map 2018_08_18
    Site Map 2018_08_19
    Site Map 2018_08_20
    Site Map 2018_08_21
    Site Map 2018_08_22
    Site Map 2018_08_23
    Site Map 2018_08_24
    Site Map 2018_08_25
    Site Map 2018_08_26
    Site Map 2018_08_27
    Site Map 2018_08_28
    Site Map 2018_08_29
    Site Map 2018_08_30
    Site Map 2018_08_31
    Site Map 2018_09_01
    Site Map 2018_09_02
    Site Map 2018_09_03
    Site Map 2018_09_04
    Site Map 2018_09_05
    Site Map 2018_09_06
    Site Map 2018_09_07
    Site Map 2018_09_08
    Site Map 2018_09_09
    Site Map 2018_09_10
    Site Map 2018_09_11
    Site Map 2018_09_12
    Site Map 2018_09_13
    Site Map 2018_09_14
    Site Map 2018_09_15
    Site Map 2018_09_16
    Site Map 2018_09_17
    Site Map 2018_09_18
    Site Map 2018_09_19
    Site Map 2018_09_20
    Site Map 2018_09_21
    Site Map 2018_09_23
    Site Map 2018_09_24
    Site Map 2018_09_25
    Site Map 2018_09_26
    Site Map 2018_09_27
    Site Map 2018_09_28
    Site Map 2018_09_29
    Site Map 2018_09_30
    Site Map 2018_10_01
    Site Map 2018_10_02
    Site Map 2018_10_03
    Site Map 2018_10_04
    Site Map 2018_10_05
    Site Map 2018_10_06
    Site Map 2018_10_07
    Site Map 2018_10_08
    Site Map 2018_10_09
    Site Map 2018_10_10
    Site Map 2018_10_11
    Site Map 2018_10_12
    Site Map 2018_10_13
    Site Map 2018_10_14
    Site Map 2018_10_15
    Site Map 2018_10_16
    Site Map 2018_10_17
    Site Map 2018_10_18
    Site Map 2018_10_19
    Site Map 2018_10_20
    Site Map 2018_10_21
    Site Map 2018_10_22
    Site Map 2018_10_23
    Site Map 2018_10_24
    Site Map 2018_10_25
    Site Map 2018_10_26
    Site Map 2018_10_27
    Site Map 2018_10_28
    Site Map 2018_10_29
    Site Map 2018_10_30
    Site Map 2018_10_31
    Site Map 2018_11_01
    Site Map 2018_11_02
    Site Map 2018_11_03
    Site Map 2018_11_04
    Site Map 2018_11_05
    Site Map 2018_11_06
    Site Map 2018_11_07
    Site Map 2018_11_08
    Site Map 2018_11_09
    Site Map 2018_11_10
    Site Map 2018_11_11
    Site Map 2018_11_12
    Site Map 2018_11_13
    Site Map 2018_11_14
    Site Map 2018_11_15
    Site Map 2018_11_16
    Site Map 2018_11_17
    Site Map 2018_11_18
    Site Map 2018_11_19
    Site Map 2018_11_20
    Site Map 2018_11_21
    Site Map 2018_11_22
    Site Map 2018_11_23
    Site Map 2018_11_24
    Site Map 2018_11_25
    Site Map 2018_11_26
    Site Map 2018_11_27
    Site Map 2018_11_28
    Site Map 2018_11_29
    Site Map 2018_11_30
    Site Map 2018_12_01
    Site Map 2018_12_02
    Site Map 2018_12_03
    Site Map 2018_12_04
    Site Map 2018_12_05
    Site Map 2018_12_06
    Site Map 2018_12_07
    Site Map 2018_12_08
    Site Map 2018_12_09
    Site Map 2018_12_10
    Site Map 2018_12_11
    Site Map 2018_12_12
    Site Map 2018_12_13
    Site Map 2018_12_14
    Site Map 2018_12_15
    Site Map 2018_12_16
    Site Map 2018_12_17
    Site Map 2018_12_18
    Site Map 2018_12_19
    Site Map 2018_12_20
    Site Map 2018_12_21
    Site Map 2018_12_22
    Site Map 2018_12_23
    Site Map 2018_12_24
    Site Map 2018_12_25
    Site Map 2018_12_26
    Site Map 2018_12_27
    Site Map 2018_12_28
    Site Map 2018_12_29
    Site Map 2018_12_30
    Site Map 2018_12_31
    Site Map 2019_01_01
    Site Map 2019_01_02
    Site Map 2019_01_03
    Site Map 2019_01_04
    Site Map 2019_01_06
    Site Map 2019_01_07
    Site Map 2019_01_08
    Site Map 2019_01_09
    Site Map 2019_01_11
    Site Map 2019_01_12
    Site Map 2019_01_13
    Site Map 2019_01_14
    Site Map 2019_01_15
    Site Map 2019_01_16
    Site Map 2019_01_17
    Site Map 2019_01_18
    Site Map 2019_01_19
    Site Map 2019_01_20
    Site Map 2019_01_21
    Site Map 2019_01_22
    Site Map 2019_01_23
    Site Map 2019_01_24
    Site Map 2019_01_25
    Site Map 2019_01_26
    Site Map 2019_01_27
    Site Map 2019_01_28
    Site Map 2019_01_29
    Site Map 2019_01_30
    Site Map 2019_01_31
    Site Map 2019_02_01
    Site Map 2019_02_02
    Site Map 2019_02_03
    Site Map 2019_02_04
    Site Map 2019_02_05
    Site Map 2019_02_06
    Site Map 2019_02_07
    Site Map 2019_02_08
    Site Map 2019_02_09
    Site Map 2019_02_10
    Site Map 2019_02_11
    Site Map 2019_02_12
    Site Map 2019_02_13
    Site Map 2019_02_14
    Site Map 2019_02_15
    Site Map 2019_02_16
    Site Map 2019_02_17
    Site Map 2019_02_18
    Site Map 2019_02_19
    Site Map 2019_02_20
    Site Map 2019_02_21
    Site Map 2019_02_22
    Site Map 2019_02_23
    Site Map 2019_02_24
    Site Map 2019_02_25
    Site Map 2019_02_26
    Site Map 2019_02_27
    Site Map 2019_02_28
    Site Map 2019_03_01
    Site Map 2019_03_02
    Site Map 2019_03_03
    Site Map 2019_03_04
    Site Map 2019_03_05
    Site Map 2019_03_06
    Site Map 2019_03_07
    Site Map 2019_03_08
    Site Map 2019_03_09
    Site Map 2019_03_10
    Site Map 2019_03_11
    Site Map 2019_03_12
    Site Map 2019_03_13