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          Data Engineer - Ritchie Bros. - Burnaby, BC      Cache   Translate Page      
Work closely with the Data Science and Analytics teams to assist with data-related technical issues and support their data and data infrastructure needs....
From Indeed - Fri, 16 Nov 2018 00:10:03 GMT - View all Burnaby, BC jobs
          Java Data Science Made Easy      Cache   Translate Page      
Java Data Science Made Easy

ISBN: 1788475658
Category: Uncategorized

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          Data Science Intern - Zillow Group - Seattle, WA      Cache   Translate Page      
Interest in working with multi-terabyte-sized data sets and is comfortable accessing that data with Hive and Presto....
From Zillow Group - Mon, 03 Dec 2018 16:50:09 GMT - View all Seattle, WA jobs
          Senior Data Scientist, Emerging Marketplaces - Zillow Group - Seattle, WA      Cache   Translate Page      
Dive into Zillow's internal and third party data (think Hive, Presto, SQL Server, Redshift, Python, Mode Analytics, Tableau, R) to make strategic...
From Zillow Group - Mon, 03 Dec 2018 16:50:09 GMT - View all Seattle, WA jobs
          Senior Data Scientist, Operations (Zillow Offers) - Zillow Group - Seattle, WA      Cache   Translate Page      
Dive into Zillow's internal and third party data (think Hive, Presto, SQL Server, Python, R, Tableau) to develop solutions that will help move the business...
From Zillow Group - Wed, 21 Nov 2018 12:24:52 GMT - View all Seattle, WA jobs
          Data Scientist - Nulogy - Toronto, ON      Cache   Translate Page      
Degree in Computer Science, Statistics, Math, Operations Research, Engineering or equivalent. Nulogy is a rare company....
From GlassDoor.com - Tue, 04 Dec 2018 23:29:40 GMT - View all Toronto, ON jobs
          Data Scientist, Consultant - KPMG LLP - Toronto, ON      Cache   Translate Page      
Generally MS or PhD-level math, statistics, operations research, engineering, computer science or econometrics. Why KPMG Lighthouse?...
From KPMG LLP - Mon, 19 Nov 2018 23:28:36 GMT - View all Toronto, ON jobs
          Product Marketing Manager, DGX Systems - NVIDIA - Santa Clara, CA      Cache   Translate Page      
You have experience with deep learning, data science, and NVIDIA GPUs. Artificial Intelligence (AI) is rapidly growing in importance and NVIDIA is at the...
From NVIDIA - Fri, 30 Nov 2018 19:54:25 GMT - View all Santa Clara, CA jobs
          Senior Data Scientist      Cache   Translate Page      
CA-San Francisco, Title: Senior Data Scientist Location: San Francisco CA Minimum 5+ years of experience required for this role Job Description What we offer you As a senior member of the data team, you'll have resources, autonomy, and the support of other A-players to drive analysis of the Hopes platform and the millions of active patients and doctors on it. Examples of data projects you could work on: Realize the
          Epic Life Quest – looking back      Cache   Translate Page      

This will be my last post for the year. In this am going to look back on goals I set for myself over past two years, how well I’ve done, and what I plan to do in the year ahead.

The idea of epic life quest was started (in community) by Steve Jones and Brent Ozar – it is about writing down goals and calling yourself to account on how you met them every year. My first post on those lines is here. I wrote this in early 2017. I did not write one in 2018, and that was because last winter was a pretty chaotic time for me and was waiting for things to look in better shape before setting more goals for myself. This year things are looking better, and below is my summary of goals I set, and how I did.

2017:

1 Complete Microsoft Data Science Program and Diploma in Healthcare Analytics from UCDavis.

got the second one done partially. I could not complete the first due to lack of time. And it wasn’t as great as I thought it would be. It was expensive, quality wasn’t up to the mark and practical gains out of it were none. I learned from this is not to commit to expensive learning programs without fully understanding if they are worth it. 


2 Stick to blogging goals – one blog post per week, one contribution to sqlservercentral.com per two weeks.

I was able to keep up with this in 2017 and part of 2018. My blogging got rather irregular half way through 2018 because of a relocation and job change, I think I can excuse myself for that. Plan to pick this back up in 2019.

3 Keep up exercise goals of 10,00 steps per day and one yoga workout per week.

I am doing fine here and this will continue as a permanent goal.


4 Speak at local user group as often as I can (my limitations with travel do not allow me to speak at too many sql saturdays or out of town events)

I did not do good on this one. Partly because my job and other personal obligations left little time to even prepare a good talk, let alone present it. Also partly because I was in a very confused place on what to talk about. Things have cleared up a bit now.


5 Submit to speak at PASS speaker idol event.

This did not happen either,for same reasons as above. Hope to make it happen in 2019.


6 Hike the Grand Canyon with my sister, we are travelling companions and love to see places together.

YES! We made and hiked several other parks too in these two years. This will be an ongoing goal.


7 See two new countries atleast – Mexico with SQL Cruise, one more towards end of the year – remains undecided for now. But two countries it is.

YES! I saw two new countries – Mexico and Jamaica. 


8 Blog on books read so that I can understand the time I devote to reading and range I read in that time.

I did not get to blogging much on what I read but managed to read quite a few books. Also contemplated on reading strategy and this will be a different type of goal (not number based) in the days to come.


9 Get home renovation work done – am undecided on if I want to keep this condo or sell it, but either way, I’d have to get work done on it. Best if it got done this year, but involves considerable financial commitment that am not sure I can meet. As of now it looks doable for this year, but may move to next year if I have to reset goals.

YES! I also sold the home after renovation. This has also changed, don’t plan to own a home anytime soon. Too much money and effort maintaining it and very limiting in so many ways.

10 Increase collection of annotated classics by year end. This is an ongoing goal to build a library for retirement. The only books I buy in print are annotated ones or those with pictures. There are not many of those and my collection is upto 30-40% of what I need already. I keep adding to it @3-4 books a year.

I am doing good on this and don’t think this should be a yearly goal, more like a long term one. I got rid of many books which I thought I did not need in paper form at all.


10 Take a course on cartooning and short story writing – both of these are my pet hobbies and never had as much time for them as I’d like – this year would like to atleast take a course on each to deepen my love and interest.
This did not happen. Too many conflicting priorities. But I did get to writing my first book and plan to pursue writing seriously in the years to come.


2018:

1  Submit to speak at PASS Summit.
This did not happen for same reasons cited above. I do not plan on this being a goal until 2020.

2 Organize SQL Saturday #10 at Louisville (not clear how different this will be yet…).
YES! SQL Saturday Louisville #10 was a phenomenal success – handed over reins of running it to new team and finding other things to do with the time I spent on it.

3 Keep up same goals for exercising.
YES! This is going on well.


4 Visit one new country with my sister – am looking at Bali/Indonesia now.

We visited three national parks as opposed to one originally planned (Zion and Quinault Rainforest). So this was  not met in theory but substituted with other valid fun things to do.

5 Visit one more new country on SQLCruise, hopefully, or on my own. Either way, I do it.
YES! I visited Mexico in 2017 and Jamaica in 2018 with SQLCruise.

6  Biggie – Pay off my mortgage. Yes, this is important and am not that far away. The only thing that keeps me from it is a bit undecided on how long I can live here with job opportunities being what they are. But I will assume those will be the same and in that case, the house will be ready to be paid off in 2018.
The house was sold instead of being paid off as I relocated to another state. I consider this goal as met although in a different way.

7 Do actual analytics work – by this time I will have a reasonable understanding of R/SAS/Microsoft Data science related skills, and expect it to take me to the next level professionally.

This was a bad goal to set, to begin with. My interviewing experience told me that very few places are doing analytics seriously to begin with, and those few places are looking for ph ds and people with a decade or more of experience to fill the role. I was able to get off pager carrying DBA role and get into doing more of data architect/coding type of work, which was what I wanted to do. Future goals will not be so specific, instead focus on what direction I want to go and where am going, instead of landing actual work which will happen on its own with time.


Goals for 2019 are as below:
1 Finish off Microsoft Big Data Certification. I am already working my way through it. I will devote every saturday evening to it and hope to get it done.
2 Continue with watching pluralsight/pass summit videos and listening to podcasts whenever time permits.
3 Complete writing second book I have committed to. 
4 Read two pages of a tech book every day evening with tea. I am intentionally keeping this goal very small and doable. This is also based on a few experiences with reading.
I do not have to read a tech book page-to-page.
There are parts that are more interesting and useful than others. 
I need to keep notes to recall/reinforce what I read. 
I will be resuming reading with this in mind. 
5 Blog @ one post every two weeks – again, scaled down from one post per week and trying to keep it modest and consistent. 
6 I am planning on 4 tech events – SQL Saturdays at Raleigh, neighboring Charlotte, Louisville and then the PASS Summit towards end of the year. 

On personal front – 
1 I plan to continue with hiking and exploring national parks. I am planning a trip in Spring/early summer with some visiting family members.
I do not think an overseas trip will materialise this year.
2 I plan to continue with healthy eating and exercising goals. 

That is my rather simple goal list. If there is one lesson i have learned with writing this down is that – it helps improve my personal commitment level, it helps keeping the list small and simple. I think with a short list, commitment and simplicity, we can all get there. Happy 2019!


          (USA-TX-Austin) Manager - DataAnalytics      Cache   Translate Page      
Job Summary The Data Analytics Manager coordinates and oversees the successful delivery of business intelligence information to the entire organization. The Data Analytics Manager is an experienced leader in BI and data science development and implementation, data architecture, data visualization and communication, ETL layers, and performance tuning. With an emphasis on effective collaboration with key stakeholders, the Data Analytics Manager owns responsibility for the assessment of business requirements, collection and identification of technical specifications, and the subsequent development of technical solutions. The Data Analytics Manager enforces a repeatable approach, cohesive framework, and industry standards with an emphasis on MicroStrategy and Pentaho Data Integration as core development tools. The Data Analytics Manager has deep theoretical and practical knowledge of the Systems Development Life Cycle (SDLC) activities specific to data integration and analytics. The Data Analytics Manager is expected to apply independent judgment and initiative in carrying out and assigning tasks and will function as a lead and mentor to developers. In addition, the Data Analytics Manager facilitates collaboration with other analysis and development teams to create standards and best practices for BI and data science solutions. Essential Job Duties • Effectively lead and mentor teams of architects and engineers. • Oversee development and application of a structured architectural approach and methodology that aligns with the key strategies of the organization to support BI and data science. • Serve as a technical leader for the organization; mentor technical staff. • Oversee development and maintenance of data integration solutions (including ETL design and architecture), semantic layer objects, presentation objects, reports, and dashboards for delivery of BI and data science solutions. • Define, implement, refine, and enforce the BI and data science solution development methodology based on industry best practices. • Develop technology specifications and ensure that any new technology solutions are optimal for meeting needs; leverage existing technologies when possible. • Apply architectural and engineering concepts to implement a solution that meets operational requirements while maintaining sustainability objectives, including: scalability, maintainability, security, reliability, extensibility, flexibility, availability, and manageability. • Lead research and development efforts (proof of concept, prototype) when introducing new technologies. • Ensure technology solutions are production ready and meet the defined specifications and that the solution can be maintained via production support methodologies and resources. • Oversee ongoing support and maintenance of deployed BI and data science solutions. • Perform other duties as assigned. Education and Experience Requirements • Bachelor's degree (Master's preferred) in Computer Science, Data Science, Engineering, Information Systems, Mathematics, Statistics, or related field. Equivalent experience will be considered in lieu of a degree. • 3+ years as a technical lead and/or architect. • 10+ years of related technical experience. • 8+ years of experience in a technical role supporting BI and data science efforts. This should include application of knowledge in statistics, data wrangling, and data visualization & communication. • 8+ years of experience in database development and tools. Ideally this includes: ETL, data modeling, complex queries, performance tuning, and stored procedures/functions. • 5+ years of designing BI and data science solutions, preferably in the healthcare industry. • 5+ years of experience in reporting and MicroStrategy (or a similar tool). • 5+ years of experience Kettle/Pentaho Data Integration (or a similar ETL tool). • 5+ years of application presentation layer experience, including data visualization and communication. • Expert data skills, including complex queries, performance tuning, expertise in a variety of approaches (e.g., relational, dimensional, unstructured). • Proven track record of successfully delivering large data-centric projects. • Strong relationship management skills; able to interface effectively with all organizational levels: users, team members, and management. • Flexible and willing to undertake a wide variety of challenging tasks. • The ability to apply architectural principles to business solutions. • A broad, enterprise-wide view of the business, with understanding of the roles of strategy, processes and capabilities, enabling technologies, and governance. • Extensive experience planning and deploying business-driven technical initiatives. • Experience using a high level language/framework (e.g. J2EE, .NET, etc.) to develop solutions. Experience with a statistical language (e.g., R) is beneficial. • Strong skills in design and implementation of logical and physical approaches to managing and analyzing large volumes of data, with knowledge of best practices. • Excellent development and testing skills (including test planning and execution). • Ability to produce high quality documentation of business and system requirements, system design, data architecture, and training materials. • Exceptional communication skills and the demonstrable ability to communicate appropriately at all levels of the organization; this includes written and verbal communications as well as visualizations + Manages, perhaps through subordinate supervisors, the coordination of the activities of a section or department with responsibility for results, including costs, methods and staffing + In some instances this manager may be responsible for a functional area and not have any subordinate employees + Works on issues of diverse scope where analysis of situation or data requires evaluation of a variety of factors, including an understanding of current business trends + Follows processes and operational policies in selecting methods and techniques for obtaining solutions + Acts as advisor to subordinate(s) to meet schedules and/or resolve problems + Develops and administers schedules, performance requirements; may have budget responsibilities + Frequently interacts with subordinate employees, customers, and/or functional peer group managers, normally involving matters between functional areas, other company divisions or units, or customers and the company + Often must lead a cooperative effort among members of a project team + Receives assignments in the form of objectives and determines how to use resources to meet schedules and goals + Provides guidance to subordinates within the latitude of established company policies + Recommends changes to policies and establishes procedures that affect immediate organization(s) EEO Statement: Active military service members, their spouses, and veteran candidates often embody the core competencies MAXIMUS deems essential, and bring a resiliency and dependability that greatly enhances our workforce. We recognize your unique skills and experiences, and want to provide you with a career path that allows you to continue making a difference for our country. We’re proud of our connections to organizations dedicated to serving veterans and their families. If you are transitioning from military to civilian life, have prior service, are a retired veteran or a member of the National Guard or Reserves, or a spouse of an active military service member, we have challenging and rewarding career opportunities available for you. A committed and diverse workforce is our most important resource. MAXIMUS is an Affirmative Action/Equal Opportunity Employer. MAXIMUS provides equal employment opportunities to all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disabled status. Requisition ID: 2018-36151 External Company URL: www.maximus.com Street: 4000 S IH-35W
          Data Science Manager - Rubikloud Technologies - Toronto, ON      Cache   Translate Page      
You have a strong background in math and technology typically (but not exclusively) found in graduate research programs in Computer Science, Engineering or...
From GlassDoor.com - Wed, 05 Dec 2018 00:36:41 GMT - View all Toronto, ON jobs
          (USA-NE-Omaha) Data Science Internship      Cache   Translate Page      
If you love to problem solve for fun and think outside of the box.....KEEP READING!!! CATCH Intelligence is hiring Data Science Interns! $14.00/Hour plus free catered lunches and snacks all day! Must be a Junior or Senior with a major or minor in Computer Science or MIS or Math. Must have experience using R. Classes in Data Science, Data Visual, Operations Research would be helpful.Prefer: Strong Math skills, creative team and independent problem solving skills and/or Database knowledge, Statistics, Machine learningLearn in a relaxed and friendly environment! Sharpen your skills under wings of Senior Data Scientist, Senior Programmers, Developers, and Architects!Build a solid path for your future today! Contact Maranna Bentley for immediate consideration: mbentley@catchintelligence.com Students who make a hobby of coding in their spare time tend to thrive at our company!
          Software/Data Science Intern - QSC - Costa Mesa, CA      Cache   Translate Page      
QSC is one of the world’s leading designers and manufacturers of professional systems equipment – and we have room for a Software/Data Science Intern to help us...
From GlassDoor.com - Wed, 05 Dec 2018 00:37:50 GMT - View all Costa Mesa, CA jobs
          (USA-WA-Seattle) Manager, Data Science & Engineering      Cache   Translate Page      
**Manager, Data Science & Engineering** Department: **Engineering** Location(s): **Seattle, WA** Tableau Software is a company on a mission. We help people see and understand their data. After a highly successful IPO in 2013, Tableau has become a market-defining company in the business intelligence industry. But don’t take our word for it—read what analysts like BARC and Gartner have to say about us. (Spoiler: You’ll find terms like “Leader” and “#1.”) Tens of thousands of companies and organizations have chosen Tableau. From the executive suites of Fortune 500 companies to the jungles of Central America, from immunology research labs to high school robotics club meetings, our software can be found anywhere people have data and questions. Additionally, journalists, bloggers, and major media web sites have embraced our free product, Tableau Public, for its ability to help them share data online in the form of interactive visualizations. Check out all of our products at: www.tableau.com/product-tour at http://www.tableausoftware.com/product-tour . Tableau’s culture is casual and high-energy. We are passionate about our product and our mission and we are loyal to each other and our company. We value work/life balance, efficiency, simplicity, freakishly friendly customer service, and making a difference in the world. Tableau offers exceptional professional and financial growth potential. To learn more about Tableau’s mission, please visit: http://mission.tableau.com at http://mission.tableausoftware.com. **Description** **What you’ll be doing…** The primary focus of the Manager for new Data Science and Engineering in the Office of the CTO is to influence the long-term direction of the product line in its support for data-science use cases and workloads. This team will build up an internal practice of data science, solving problems important to the company using Data Science techniques, with goals of building up internal best practices and influencing our product line to bring in data science techniques. The ideal candidate is a practitioner , deeply familiar with techniques and tools in the industry who can guide both other data scientists and product engineers on how to successfully engage with these techniques. They are a natural motivator of diverse teams, helping them engage with new problems and collaboratively prove out and demonstrate solutions to these problems. **Some of the things you’ll be doing include…** + Build a team of engineers and scientists that can take on data science problems at the company with the ultimate goal of strongly influencing our product line. + Build up a set of best practices for using these techniques at the company, both for this team and for other teams that want to employ them. + Work closely with local product managers and engineering leaders on prototyping projects that pull these techniques into a best-in-class analysis suite. + Hire, lead and motivate a fast moving team of engineers and scientists. **Who you are…** + **Experienced.** 3+ years of leadership experience over data science teams, ideally in a business context. + **Data Rock Star.** 5+ years of hands-on experience using data science techniques. Working knowledge of several data science platforms, either open source or commercial. + **A Natural Leader.** Demonstrated leadership abilities in moving quickly and through ambiguous projects. Motivator for data science teams composed of data engineers + **You are a Recruiter!** Tableau hires company builders and, in this role, you will be asked to be on the constant lookout for the best talent to bring onboard to help us continue to build one of the best companies in the world! \#LI-EF1
          ASST PROFESSOR-MACHINE LEARNING,AI & DATA SCIENCE-GRAINGER INST. FOR ENGINEERING - University of Wisconsin–Madison - Madison, WI      Cache   Translate Page      
Position Vacancy ID: 96128-FA Employment Class: Faculty Working Title: Asst Professor-Machine Learning,AI & Data Science-Grainger Inst. for Engineering...
From University of Wisconsin–Madison - Sat, 22 Sep 2018 00:57:35 GMT - View all Madison, WI jobs
          SR DIRECTOR 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 Manager, Data Scientist - Johnson & Johnson Family of Companies - Raritan, NJ      Cache   Translate Page      
This position may be located in Titusville, NJ or Raritan, NJ. There are many ways to explore and analyze data, and this drives the excitement and passion of...
From Johnson & Johnson Family of Companies - Thu, 08 Nov 2018 23:02:52 GMT - View all Raritan, NJ jobs
          Senior Manager, Data Scientist - Johnson & Johnson Family of Companies - Titusville, NJ      Cache   Translate Page      
This position may be located in Titusville, NJ or Raritan, NJ. There are many ways to explore and analyze data, and this drives the excitement and passion of...
From Johnson & Johnson Family of Companies - Thu, 08 Nov 2018 23:02:52 GMT - View all Titusville, NJ jobs
          NLP Data Scientist - PARC, a Xerox company - Palo Alto, CA      Cache   Translate Page      
PARC, a Xerox company, is in the Business of Breakthroughs®. We create new business options, accelerate time to market, augment internal capabilities, and...
From PARC, a Xerox company - Sat, 29 Sep 2018 08:33:49 GMT - View all Palo Alto, CA jobs
          Machine Learning / Artificial Intelligence Scientist      Cache   Translate Page      
MI-Dearborn, Ford Motors' Global Data Insight and Analytics (GDI&A) organization is looking for smart, nice and curious data scientists with exceptional skills in Machine Learning (ML), Artificial Intelligence (AI) and Scalable Computing. No other industry has the diversity, difficulty and economic impact of quantitative-based solutions as those found in the automotive industry. And no other automotive company
          Senior Data Scientist - Canvass Analytics Inc. - Toronto, ON      Cache   Translate Page      
Build relationships with Google’s Gradient Ventures, the Vector Institute and Deepmind to collaborate on the Reinforcement Learning issues....
From Canvass Analytics Inc. - Fri, 12 Oct 2018 23:33:37 GMT - View all Toronto, ON jobs
          Data Science Manager // Gestionnaire de la science des données - SSENSE - Montréal, QC      Cache   Translate Page      
He/She will help us discover the information hidden in vast amounts of data, to make smarter decisions and deliver even better products....
From GlassDoor.com - Tue, 04 Dec 2018 22:51:04 GMT - View all Montréal, QC jobs
          Data Elixir - Issue 211      Cache   Translate Page      

Insight

Curiosity-Driven Data Science

Eric Colson, the Chief Algorithms Officer at Stitch Fix, offers key insights for creating a data-centered culture that will empower your data scientists to come up with things you never dreamed of.

hbr.org

Profiles

The Friendship That Made Google Huge

Jeff Dean and Sanjay Ghemawat are the highest ranking engineers at Google. Together, they've been instrumental in the creation of technologies like MapReduce and TensorFlow and their work has changed the course of the Internet. This New Yorker profile of the duo is fantastic.

newyorker.com

Sponsored Link

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.

vettery.com

Tools and Techniques

Machine Learning Basics - Gradient Boosting & XGBoost

Nice introduction to Gradient Boosting and XGBoost by Shirin Glander. Starts with an overview of how these techniques work and then then shows how to implement them using common R packages.

netlify.com

Git Your SQL Together (with a Query Library)

Here's why you should be keeping your SQL queries in a library, along with an easy and useful approach for setting one up.

caitlinhudon.com

Estimating Probabilities with Bayesian Modeling in Python

A simple application of Probabilistic Programming with PyMC3 in Python.

towardsdatascience.com

Explore Galvanize's Immersive Data Science Bootcamp!

Powered by a cutting-edge curriculum and expert instructors, our 13-week full-time immersive program provides unparalleled education in a dynamic learning environment. Our career team also helps to identify your strengths, refine your goals, and connect you with Galvanize’s 1000+ hiring partners.

// sponsored

galvanize.com

Resources

No time to read AI research? Here are the top 2018 papers summarized.

To help you stay current with the rapidly evolving advances in AI, here are summaries of 10 key AI research papers from 2018. For each paper, the authors include links to the full papers, core ideas, key achievements, potential applications, and thoughts from the community.

topbots.com

A planetary-scale platform for Earth science data & analysis

This is an amazing collection of satellite data that's free to use for research, education, and nonprofit uses. Includes more than forty years of historical imagery and scientific datasets in disciplines such as imagery, climate, weather, terrain models, land cover, etc.

google.com

Data Viz

Visualization for Machine Learning

This tutorial session from the NeurIPS Conference is a great introduction to Machine Learning Visualization. Fernanda Viegas and Martin Wattenberg are presenting and they're fantastic. The entire presentation is ~2 hours but it's well-organized and easy to skip around.

facebook.com

Career

3 common data science career transitions, and how to make them happen

Jeremie Harris helps people transition into data science careers through SharpestMinds. No single approach works for everyone but consistently, he offers similar advice to 3 different categories of job searchers: complete beginners, software engineers, and new grads. In this post, he shares his insights and offers specific suggestions for each type of job searcher.

towardsdatascience.com

Jobs & Careers

Hiring?

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, Facebook, or Google Plus.


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


          Solving Problems with Data Science      Cache   Translate Page      

A challenge that I’ve been wrestling with is the lack of a widely populated framework or systematic approach to solving data science problems. In our analytics work at Viget, we use a framework inspired by Avinash Kaushik’s Digital Marketing and Measurement Model. We use this framework on almost every project we undertake at Viget. I believe data science could use a similar framework that organizes and structures the data science process.

As a start, I want to share the questions we like to ask when solving a data science problem. Even though some of the questions are not specific to the data science domain, they help us efficiently and effectively solve problems with data science.

Business Problem

What is the problem we are trying to solve?

That’s the most logical first step to solving any question, right? We have to be able to articulate exactly what the issue is. Start by writing down the problem without going into the specifics, such as how the data is structured or which algorithm we think could effectively solve the problem.

Then try explaining the problem to your niece or nephew, who is a freshman in high school. It is easier than explaining the problem to a third-grader, but you still can’t dive into statistical uncertainty or convolutional versus recurrent neural networks. The act of explaining the problem at a high school stats and computer science level makes your problem, and the solution, accessible to everyone within your or your client’s organization, from the junior data scientists to the Chief Legal Officer.

Clearly defining our business problem showcases how data science is used to solve real-world problems. This high-level thinking provides us with a foundation for solving the problem. Here are a few other business problem definitions we should think about.

  • Who are the stakeholders for this project?
  • Have we solved similar problems before?
  • Has someone else documented solutions to similar problems?
  • Can we reframe the problem in any way?

And don’t be fooled by these deceivingly simple questions. Sometimes more generalized questions can be very difficult to answer. But, we believe answering these framing question is the first, and possibly most important, step in the process, because it makes the rest of the effort actionable.  

Example

Say we work at a video game company —  let’s call the company Rocinante. Our business is built on customers subscribing to our massive online multiplayer game. Users are billed monthly. We have data about users who have cancelled their subscription and those who have continued to renew month after month. Our management team wants us to analyze our customer data.

What is the problem we are trying to solve?

Well, as a company, the Rocinante wants to be able to predict whether or not customers will cancel their subscription. We want to be able to predict which customers will churn, in order to address the core reasons why customers unsubscribe. Additionally, we need a plan to target specific customers with more proactive retention strategies.

Churn is the turnover of customers, also referred to as customer death. In a contractual setting - such as when a user signs a contract to join a gym - a customer “dies” when they cancel their gym membership. In a non-contractual setting, customer death is not observed and is more difficult to model. For example, Amazon does not know when you have decided to never-again purchase Adidas. Your customer death as an Amazon or Adidas customer is implied.

Possible Solutions

What are the approaches we can use to solve this problem?

There are many instances when we shouldn’t be using machine learning to solve a problem. Remember, data science is one of many tools in the toolbox. There could be a simpler, and maybe cheaper, solution out there. Maybe we could answer a question by looking at descriptive statistics around web analytics data from Google Analytics. Maybe we could solve the problem with user interviews and hear what the users think in their own words. This question aims to see if spinning up EC2 instances on Amazon Web Services is worth it. If the answer to, “Is there a simple solution,” is, “No,” then we can ask, “Can we use data science to solve this problem?” This yes or no question brings about two follow-up questions:

  1. Is the data available to solve this problem?” A data scientist without data is not a very helpful individual. Many of the data science techniques that are highlighted in media today — such as deep learning with artificial neural networks — requires a massive amount of data. A hundred data points is unlikely to provide enough data to train and test a model. If the answer to this question is no, then we can consider acquiring more data and pipelining that data to warehouses, where it can be accessed at a later date.
  2. Who are the team members we need in order to solve this problem?” Your initial answer to this question will be, “The data scientist, of course!” The vast majority of the problems we face at Viget can’t or shouldn’t be solved by a lone data scientist because we are solving business problems. Our data scientists team up with UXers, designers, developers, project managers, and hardware developers to develop digital strategies and solving data science problems is one part of that strategy. Siloing your problem and siloing your data scientists isn’t helpful for anyone.

Example

We want to predict when a customer will unsubscribe from Rocinante’s flagship game. One simple approach to solving this problem would be to take the average customer life - how long a gamer remains subscribed - and predict that all customers will churn after X amount of time. Say our data showed that on average customers churned after 72 months of subscription. Then we could predict a new customer would churn after 72 months of subscription. We test out this hypothesis on new data and learn that it is wildly inaccurate. The average customer lifetime for our previous data was 72 months, but our new batch of data had an average customer lifetime of 2 months. Users in the second batch of data churned much faster than those in the first batch. Our prediction of 72 months didn’t generalize well. Let’s try a more sophisticated approach using data science.

  1. Is the data available to solve this problem? The dataset contains 12,043 rows of data and 49 features. We determine that this sample of data is large enough for our use-case. We don’t need to deploy Rocinante’s data engineering team for this project.
  2. Who are the team members we need in order to solve this problem?  Let’s talk with the Rocinante’s data engineering team to learn more about their data collection process. We could learn about biases in the data from the data collectors themselves. Let’s also chat with the customer retention and acquisitions team and hear about their tactics to reduce churn. Our job is to analyze data that will ultimately impact their work. Our project team will consist of the data scientist to lead the analysis, a project manager to keep the project team on task, and a UX designer to help facilitate research efforts we plan to conduct before and after the data analysis.

Evaluation

How do we know if we have successfully solved the problem?

At Viget, we aim to be data-informed, which means we aren’t blindly driven by our data, but we are still focused on quantifiable measures of success. Our data science problems are held to the same standard. What are the ways in which this problem could be a success? What are the ways in which this problem could be a complete and utter failure? We often have specific success metrics and Key Performance Indicators (KPIs) that help us answer these questions.

Example

Our UX coworker has interviewed some of the other stakeholders at Rocinante and some of the gamers who play our game. Our team believes if our analysis is inconclusive, and we continue the status quo, the project would be a failure. The project would be a success if we are able to predict a churn risk score for each subscriber. A churn risk score, coupled with our monthly churn rate (the rate at which customers leave the subscription service per month), will be useful information. The customer acquisition team will have a better idea of how many new users they need to acquire in order to keep the number of customers the same, and how many new users they need in order to grow the customer base. 

Data Science-ing

What do we need to learn about the data and what analysis do we need to conduct?

At the heart of solving a data science problem are hundreds of questions. I attempted to ask these and similar questions last year in a blog post, Data Science Workflow. Below are some of the most crucial — they’re not the only questions you could face when solving a data science problem, but are ones that our team at Viget thinks about on nearly every data problem.

  1. What do we need to learn about the data?
  2. What type of exploratory data analysis do we need to conduct?
  3. Where is our data coming from?
  4. What is the current state of our data?
  5. Is this a supervised or unsupervised learning problem?
  6. Is this a regression, classification, or clustering problem?
  7. What biases could our data contain?
  8. What type of data cleaning do we need to do?
  9. What type of feature engineering could be useful?
  10. What algorithms or types of models have been proven to solve similar problems well?
  11. What evaluation metric are we using for our model?
  12. What is our training and testing plan?
  13. How can we tweak the model to make it more accurate, increase the ROC/AUC, decrease log-loss, etc. ?
  14. Have we optimized the various parameters of the algorithm? Try grid search here.
  15. Is this ethical?

That last question raises the conversation about ethics in data science. Unfortunately, there is no hippocratic oath for data scientists, but that doesn’t excuse the data science industry from acting unethically. We should apply ethical considerations to our standard data science workflow. Additionally, ethics in data science as a topic deserves more than a paragraph in this article — but I wanted to highlight that we should be cognizant and practice only ethical data science.

Example

Let’s get started with the analysis. It’s  time to answer the data science questions. Because this is an example, the answer to these data science questions are entirely hypothetical.

  1. We need to learn more about the time series nature of our data, as well as the format.
  2. We should look into average customer lifetime durations and summary statistics around some of the features we believe could be important.
  3. Our data came from login data and customer data, compiled by Rocinante’s data engineering team.
  4. The data needs to be cleaned, but it is conveniently in a PostgreSQL database.
  5. This is a supervised learning problem because we know which customers have churned.
  6. This is a binary classification problem.
  7. After conducting exploratory data analysis and speaking with the data engineering team, we do not see any biases in the data.
  8. We need to reformat some of the data and use missing data imputation for features we believe are important but have some missing data points.
  9. With 49 good features, we don’t believe we need to do any feature engineering.
  10. We have used random forests, XGBoost, and standard logistic regressions to solve classification problems.
  11. We will use ROC-AUC score as our evaluation metric.
  12. We are going to use a training-test split (80% training, 20% test) to evaluate our model.
  13. Let’s remove features that are statistically insignificant from our model to improve the ROC-AUC score.
  14. Let’s optimize the parameters within our random forests model to improve the ROC-AUC score.
  15. Our team believes we are acting ethically.

This process may look deceivingly linear, but data science is often a nonlinear practice. After doing all of the work in our example above, we could still end up with a model that doesn’t generalize well. It could be bad at predicting churn in new customers. Maybe we shouldn’t have assumed this problem was a binary classification problem and instead used survival regression to solve the problem. This part of the project will be filled with experimentation, and that’s totally normal.

Communication

What is the best way to communicated and circulate our results?

Our job is typically to bring our findings to the client, explain how the process was a success or failure, and explain why. Communicating technical details and explaining to non-technical audiences is important because not all of our clients have degrees in statistics.  There are three ways in which communication of technical details can be advantageous:

  • It can be used to inspire confidence that the work is thorough and multiple options have been considered.
  • It can highlight technical considerations or caveats that stakeholders and decision-makers should be aware of.  
  • It can offer resources to learn more about specific techniques applied.
  • It can provide supplemental materials to allow the findings to be replicated where possible.

We often use blog posts and articles to circulate our work. They help spread our knowledge and the lessons we learned while working on a project to peers. I encourage every data scientist to engage with the data science community by attending and speaking at meetups and conferences, publishing their work online, and extending a helping hand to other curious data scientists and analysts.

Example

Our method of binary classification was in fact incorrect, so we ended up using survival regression to determine there are four features that impact churn: gaming platform, geographical region, days since last update, and season. Our team aggregates all of our findings into one report, detailing the specific techniques we used, caveats about the analysis, and the multiple recommendations from our team to the customer retention and acquisition team. This report is full of the nitty-gritty details that the more technical folks, such as the data engineering team, may appreciate. Our team also creates a slide deck for the less-technical audience. This deck glosses over many of the technical details of the project and focuses on recommendations for the customer retention and acquisition team.

We give a talk at a local data science meetup, going over the trials, tribulations, and triumphs of the project and sharing them with the data science community at large.

Why?

Why are we doing all of this?

I ask myself this question daily — and not in the metaphysical sense, but in the value-driven sense. Is there value in the work we have done and in the end result? I hope the answer is yes. But, let’s be honest, this is business. We don’t have three years to put together a PhD thesis-like paper. We have to move quickly and cost-effectively. Critically evaluating the value ultimately created will help you refine your approach to the next project. And, if you didn’t produce the value you’d originally hoped, then at the very least, I hope you were able to learn something and sharpen your data science skills. 

Example

Rocinante has a better idea of how long our users will remain active on the platform based on user characteristics, and can now launch preemptive strikes in order to retain those users who look like they are about to churn. Our team eventually develops a system that alerts the customer retention and acquisition team when a user may be about to churn, and they know to reach out to that user, via email, encouraging them to try out a new feature we recently launched. Rocinante is making better data-informed decisions based on this work, and that’s great!

Conclusion

I hope this article will help guide your next data science project and get the wheels turning in your own mind. Maybe you will be the creator of a data science framework the world adopts! Let me know what you think about the questions, or whether I’m missing anything, in the comments below.


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          “Statistical insights into public opinion and politics” (my talk for the Columbia Data Science Society this Wed 9pm)      Cache   Translate Page      

7pm in Fayerweather 310: Why is it more rational to vote than to answer surveys (but it used to be the other way around)? How does this explain why we should stop overreacting to swings in the polls? How does modern polling work? What are the factors that predict election outcomes? What’s good and bad […]

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Are you looking for your next big challenge? Does the idea of working on the cutting edge of security sound exciting? Office 365 is at the center of Microsoft’s cloud first, devices first strategy as it brings together cloud versions of our most trusted communications and collaboration products such as Exchange, SharePoint, Yammer, and Skype for Business with the latest version of our desktop suite and mobile apps. Our customers depend on our services to run their organizations, whether that is a Fortune 500 company, a small business, a non-profit, or an educational institution. Our customers trust us with their most critical data and we honor that trust with continuous investment and improvement in the security of our services. Our team consists of engineers with expertise in large-scale software systems, security analysis, big data, and machine learning. We delight in digging in deep to analyze the billions of events and terabytes of data generated each day by all the Office 365 services for evidence of suspicious activity. **Responsibilities** We are seeking a talented and passionate engineer who can borrow from best practices in the company and industry and apply them in nimble fashion to analyze data and solve security problems. You will be called upon to work with security experts, data scientists, and seasoned engineers to deliver working systems that run at high quality delivering low noise insights. **Qualifications** Basic Qualifications: + BS/MS in computer science or equivalent + 3+ years of software development experience Preferred Qualifications: + Demonstrated skills in C#, Java, or C++ Strong software design and problem-solving skills + Experience with modern software service engineering practices such as Testing in Production, Live Monitoring, Data Driven Engineering + Rapid prototyping and iteration skills Skills that will set you apart + Experience in security investigation, analysis, and incident response + A passion for data science and machine learning + Experience with Spark or Hadoop + Fluency with modern web UI frameworks Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: + Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form at https://careers.microsoft.com/us/en/accommodationrequest . Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.
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BUTTE, Mont. (AP) — Major changes are in the works at a Montana university. Montana Tech plans to cut and merge certain programs and cut and transfer some faculty positions.   Programs scheduled for elimination include professional and technical communication, data science and statistics, and health care informatics.  ... Continue reading…
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USCPublicDiplomacy.org


Dec 3, 2018
by
Ali Fisher

As the sources and instruments of power have adapted to a new information age, the extent to which different groups have access to influence has also shifted.

Public diplomacy in the 21st century has to navigate the complex architecture of multi-hub, multi-directional networks that exist between communities around the world. As a result, calibrating public diplomacy and strategic communications to face specific challenges requires fine-tuning the specific approach to engagement with foreign publics as well as an understanding of the tactics employed by any adversaries who wish to influence those same publics.

This complexity is not a new phenomenon, but the information age has added an additional layer of methods to communicate or share information, and additional opportunities to analyze available data. This is true whether discussing how the Media Mujahidin seek to spread their theology, Russian attempts to influence political discourse, or attempts by political cyberhackers to influence elections

Influencing the Information Environment

There are many ways nations seek to influence the information that individuals use to shape their individual perspectives. In addition to the usual public diplomacy and cultural relations infrastructure in the UK, the 77th Brigade was created to contest information wars. As Carl Miller wrote, “They are soldiers, but the 77thBrigade edit videos, record podcasts and write viral posts. Welcome to the age of information warfare.” This decision is “an unorthodox one, but in the eyes of the British Army also a necessary innovation; simply reflecting the world in which we all now live and the new kind of warfare that happens within it.”

For public diplomacy, the ability to identify opportunities for influence—and the ability identify the influence others exert—within an information environment requires both tools and the diplomats able to use them. 

For the UK military, information operations play a key part in the non-lethal approach to targeting, but they are not alone. There has been much discussion of influence operations targeting Facebook which has sparked interest in developing new insight into the impact of social media on elections and the subsequent data release by Facebook.

Similarly, Twitter has noted that “it is clear that information operations and coordinated inauthentic behavior will not cease.” As part of their effort to protect what they term “a healthy public conversation,” they have released data relating to their investigation “into foreign interference in political conversations on Twitter.” This disclosure includes details of activity “potentially connected to a propaganda effort by a Russian government-linked organization known as the Internet Research Agency.”

This is an example of how those responsible for public diplomacy, strategic communications and information operations can utilize newer communication methods to exert influence. The specific Options for Influence that they choose may be different, but they are all operating in the same spaces and on the same platforms. This data release also highlights that for public diplomacy scholars and practitioners there are greater opportunities to analyze how others are using similar spaces and platforms to exert influence.

Analyzing an Information Environment

The recent Twitter data release relating to Russia and Iran demonstrates opportunities to gain insight into how platforms are being exploited by those conducting information operations, but only if public diplomacy organizations can handle the data appropriately.

This means there is a need to extend the level of data analysis which is taught during degree level or postgraduate courses relating to public diplomacy. Equally the culture within organizations conducting public diplomacy may have to increase the values they place on the ability to use the host of programming languages freely available.

Those interested may want to try Jupyter and follow guides such as Mining the Social Web or Python for Data Analysis (Jeffrey Stanton’s Introduction to Data Science provides a similar introduction for anyone who would prefer to focus on R).

Beginning with Jupyter, users can take advantage of a single start point and expand out to experiment with a wide range of kernelsincluding Python, R, Bash, Perl, Lua, Java or MATLAB and unlock the analytical capabilities of libraries and packages such as Pandas and Networkx. No matter the specific language someone chooses, it should allow students, scholars and practitioners to develop the skills and greater flexibility in handling data to get beyond the common overreliance on Excel.    

To demonstrate the opportunity to analyze the information environment, the following analysis of information operations on Twitter was conducted using only freely available and open source resources, including Jupyter, run on a desktop PC that is between three and four years old.      

The Data

According to the statement on the Twitter blog:

These large datasets comprise 3,841 accounts affiliated with the IRA, originating in Russia, and 770 other accounts, potentially originating in Iran. They include more than 10 million Tweets and more than 2 million images, GIFs, videos, and Periscope broadcasts…

This is a large amount of data in public diplomacy terms: 297 GB and 65 GB relating to Russian and Iranian operations, respectively.

Using Python and Pandas within Jupyter, one can read the data provided by Twitter and locate all the retweets. As Excel only allows 1,048,576 rows per worksheet and the Russian data alone contains approximately 3,333,000 retweets, the need for access to alternative data handling solutions becomes clear.

The 3.3 million retweets result in a network of approximately 205,000 accounts made up of 844,000 tweet and retweet connections. Once you can create the network, it can be either analyzed within Jupyter—options include Python package Networkx, or igraph if using R. For those with a preference for a more visual form of analysis, data can be exported for analysis in Gephi.

Important note for those who analyze the network visually: ensure the layout has run for long enough. If it is still basically square, you almost certainly haven’t let it run long enough. Visual analysis based on an incomplete network layout are invariably baseless nonsense. This type of error can be seen in the ISIS Twitter Census (p.57).

The Networks

Using Gephi, we find in the “Russian” network that 99.9 percent of nodes are connected within a single “giant component” but that some parts of that network are more interconnected than others, as the giant component contains 19 statistically distinct communities. Using the same method on the “Iranian” data, there are 232,000 retweets, connecting 32,700 accounts and 64,500 tweet and retweet connections. 99.8 percent of the accounts connect to make up a single “giant component.” This time, the giant component contains 23 statistically distinct communities (again visualized using Gephi). 

This overview shows that in both Russian and Iranian cases:

There are many more accounts in the network than those accounts identified by the original data release.There are a number of different hubs of activity with little connection between them. This allows them to reach out in different directions.

 

Collectively the overview of these information environments shows that both Russian and Iranian information operations are drawing on content produced by other accounts, but for various reasons these accounts serve their purpose. With control of the data, public diplomacy organizations could find information about the accounts producing content which is subsequently exploited by the Iranian or Russian information operation using a simple call to the Twitter API.

This information would provide another perspective on the different online communities which were being specifically targeted and would help triangulate findings from other methods. For example, community targeting was also uncovered by the Atlantic Council's Digital Forensic Research Lab in their analysis of the #tags and URLs being shared. The methods applied by the Digital Forensic Research Lab to find the #tags or URLs commonly used could be applied at the level of specific communities within the network, once you know which accounts are in which cluster just that content can be analyzed.

Working Together

One benefit of analyzing content based on the specific clusters is the greater level of granularity in the analysis. For example, are accounts alleged to be part of the information operation working together, or merely tweeting the same content, URL and #tags?

Analysis of the overall network provides some insight, which can be extended if just the accounts which have a mutual degree (two accounts that both retweeted each other) are analyzed. This means both are thought to be part of the information operation. The Russian network, when filtered using mutual degree, contains 1,198 accounts. This is 0.58 percent of the total accounts in the network but includes 13 percent of the edges (lines representing retweet relationships) present in the total network.  

In the Iranian network, the number of accounts drops to 219, 0.67 percent of the original nodes and only 1.59 percent of the edges. As one would intuit from the images, the statistics indicate that the accounts which are believed to be part of the Russian information operation worked together much more closely than those in the Iranian version. The Iranian version engaged more frequently with accounts not identified as part of the information operation.

Conclusion

The tools for developing innovative strategies for public diplomacy in the big data era have been evolving for many years as commercial tools and freely available programming languages. Some influence and information activity will be conducted in the shadows, whether this is by GCHQ, who like the UK military also had an information warfare unit, with their tools having code names like “Badger,” “Gateway,” “Burlesque” and “Clean Sweep,” or other actors. But much more will happen in the open.

For public diplomacy, the ability to identify opportunities for influence—and the ability identify the influence others exert—within an information environment requires both tools and the diplomats able to use them. Some will come from “off-the-shelf” commercially available tools. However, as shown here, there are many instances where data could be analyzed and visualized by diplomats with freely available tools.

It is a question of organizational culture and training. The Economist recently reported that Python is becoming the most popular coding language, but how many diplomats could use it or any of the many other programming languages freely available to deliver insight and influence?

Public diplomacy may not use information operations methods alleged by Twitter in their data release. However, having the skills to develop an appropriate situational awareness of what others are doing in the same place or space continues to be vital to the successful planning and practice of public diplomacy.


          ASST PROFESSOR-MACHINE LEARNING,AI & DATA SCIENCE-GRAINGER INST. FOR ENGINEERING - University of Wisconsin–Madison - Madison, WI      Cache   Translate Page      
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Turing-Powered TITAN Delivers 130 Teraflops of Deep Learning Horsepower, 11 GigaRays of Ray-Tracing Performance to World’s Most Demanding Users MONTREAL—Conference on Neural Information Processing Systems—Dec. 3, 2018—NVIDIA today introduced NVIDIA® TITAN RTX™, the world’s most powerful desktop GPU, providing massive performance for AI research, data science and creative applications. Driven by the new NVIDIA Turing™ architecture, TITAN RTX — dubbed T-Rex — delivers 130 teraflops of deep learning performance and 11 GigaRays of ray-tracing performance. “Turing is NVIDIA’s biggest advance in a decade – fusing shaders, ray tracing, and deep learning to reinvent the GPU,” said Jensen Huang, founder and...

Keep on reading: (PR) NVIDIA Reveals the Titan of Turing: TITAN RTX
          Offer - ONLINE SAP SIMPLE LOGISTICS TRAINING INDIA - USA      Cache   Translate Page      
ONLINE SAP SIMPLE LOGISTICS TRAINING INDIASOFTNSOL is a Global Interactive Learning company started by proven industry experts with an aim to provide Quality Training in the latest IT Technologies. SOFTNSOL offers SAP S4 HANA LOGISTICS Our trainers are highly talented and have Excellent Teaching skills. They are well experienced trainers in their relative field. Online training is your one stop & Best solution to learn SAP S4 HANA LOGISTICS at your home with flexible Timings.We offer SAP S4 HANA LOGISTICS conducted on Normal training and fast track training classes.SAP S4 HANA LOGISTICS ONLINE TRAININGWe offer you :1. Interactive Learning at Learners convenience time2. Industry Savvy Trainers3. Learn Right from Your Place4. Advanced Course Curriculum 5. 24/7 access 6. Two Months Server Access along with the training 7. Support after Training8. Certification Guidance We have a third coming online batch on SAP S4 HANA LOGISTICS Online Training.We also provide online trainings on SAP ABAP,SAP WebDynpro ABAP,SAP ABAP ON HANA,SAP Workflow,SAP HR ABAP,SAP OO ABAP,SAP BOBI, SAP BW,SAP BODS,SAP HANA,SAP HANA Admin, SAP S4HANA, SAP BW ON HANA,SAP S4HANA, SAP S4HANA Simple Finance,SAP S4HANA Simple Logistics,SAP ABAP on S4HANA,SAP Success Factors,SAP Hybris,SAP FIORI,SAP UI5,SAP Basis,SAP BPC,SAP Security with GRC,SAP PI,SAP C4C,SAP CRM Technical,SAP FICO,SAP SD,SAP MM,SAP CRM Functional,SAP HR,SAP WM,SAP EWM,SAP EWM on HANA,SAP APO,SAP SNC,SAP TM,SAP GTS,SAP SRM,SAP Vistex,SAP MDG,SAP PP,SAP PM,SAP QM,SAP PS,SAP IS Utilities,SAP IS Oil and Gas,SAP EHS,SAP Ariba,SAP CPM,SAP IBP,SAP C4C,SAP PLM,SAP IDM,SAP PMR,SAP Hybris,SAP PPM,SAP RAR,SAP MDG,SAP Funds Management,SAP TRM,SAP MII,SAP ATTP,SAP GST,SAP TRM,SAP FSCM,Oracle,Oracle Apps SCM,Oracle DBA,Oracle RAC DBA,Oracle Exadata,Oracle HFM,Informatica,Testing Tools,MSBI,Hadoop,devops,Data Science,AWS Admin,Python, and Salesforce .Experience the Quality of our Online Training. For Free Demo Please ContactSOFTNSOL : India: +91 9573428933USA : +1 929-268-1172WhatsApp: +91 9573428933Skype id : softnsoltrainingsEmail id: info@softnsol.comWebsite : http://softnsol.com/.
          Best Data Scientist Training institute in noida-delhi      Cache   Translate Page      
Inovi Technologies is the best Data Scientist training institute in Noida. Data science Training by Expert. Data science it is a software here distributing and processing the large set of data into the cluster of computers. This Course is designed to Master yourself in the Data Science Techniques...
          Intesa Sanpaolo, la banca verso la digital transformation      Cache   Translate Page      
Intesa Sanpaolo digital transformation

Intesa Sanpaolo ha in Italia 11,9 milioni di clienti e una rete di circa 4.400 sportelli in tutto il territorio nazionale. La banca ha nell’ultimo periodo spinto sull’acceleratore anche nel processo di digital transformation.

Sono infatti 8 milioni i clienti multicanale, di cui quasi 3 milioni sulla nuova app Intesa Sanpaolo Mobile. Sono 56 milioni i login al mese solo su app, informa la banca. E 53 milioni le operazioni dispositive su app da inizio anno, di cui 1,2 milioni di prelievi cardless. Inoltre, 47,3 milioni di operazioni di post vendita carte nel 2018. A questi dati si aggiunge l’enorme mole di consultazioni accessibili in ogni momento con pochi clic dal proprio smartphone.

Sono questi i risultati resi noti da Intesa Sanpaolo nel presentare l’ecosistema digitale della banca. Ecosistema digitale che fa perno su Intesa Sanpaolo Mobile e dà accesso a servizi bancari, finanziamenti e risparmio. E che si arricchisce ora con la nuova app Intesa Sanpaolo Investo. Quest’ultima consente di investire online in autonomia e tenere sotto controllo il proprio patrimonio.

La trasformazione digitale di Intesa Sanpaolo

Il percorso di trasformazione digitale della banca è ancora in corso. La roadmap prevede il completo ridisegno del sito e delle app, con la volontà di creare un’esperienza digitale di eccellenza. Intesa Sanpaolo ha inoltre creato appositi strumenti e servizi per aiutare i clienti a distanza nell’accesso ai nuovi canali. Rimane inteso che le filiali non scompaiono. In esse è stata eliminata la carta nell’85% delle operazioni bancarie e nel 98% dei contratti. Restano comunque il punto di riferimento per la consulenza e per le esigenze più complesse.

Stefano Barrese, responsabile Banca dei Territori Intesa Sanpaolo

Il Gruppo prevede circa 2,8 miliardi di euro di investimenti nella trasformazione digitale entro i prossimi tre anni. La roadmap prevede di portare le attività digitalizzate dal 10% nel 2017 al 70% nel 2021.

Gli investimenti interesseranno processi, cyber security, innovazione, strumenti di Advanced Analytics, dialogo con le FinTech, estensione di piattaforme digitali e percorsi di acquisto multicanale per le imprese. Ad esempio, prevede più di 100 Data Scientist dedicati nel 2021 contro i 15 nel 2017. Inoltre, nei prossimi tre anni le vendite digitali dovranno crescere dal 2,5% di fine 2017 al 15%.

Il mobile è il motore dell’ecosistema

Intesa Sanpaolo Mobile, l’app per il mobile banking, dal lancio di due anni fa ha quasi triplicato gli utenti. Ora viene utilizzata da quasi tre milioni di utenti, che si connettono in media 18 volte al mese.

L’app offre semplicità di autenticazione, sfruttando anche l’impronta digitale o il riconoscimento facciale dove disponibili. E semplicità anche nello svolgimento delle attività più comuni. È ad esempio possibile pagare un bollettino o un F24 scattando una foto. Oppure gestire in piena libertà le proprie carte di credito.

Peculiarità dell’app è la funzione che consente di prelevare senza carta alle casse veloci automatiche. Oppure aiutare un parente o un amico a prelevare denaro, senza carta, in situazioni d’emergenza: il cosiddetto prelievo SOS.

Digital e mobile payment

Anche i servizi di digital payment della banca sono integrati nell’app Intesa Sanpaolo Mobile. Essi sono accessibili già in pre-login attraverso il nuovo portafoglio digitale XME Pay. Con questo è possibile pagare nei negozi con le carte o direttamente dal conto, salvare le carte fedeltà e i documenti di identità. È inoltre possibile essere avvisati in prossimità della scadenza e beneficiare di offerte dedicate.

Per i clienti Intesa Sanpaolo che usano dispositivi iOS (sono oltre un milione) è da poco disponibile Apple Pay. Il servizio è attivabile direttamente da XME Pay su tutte le carte di credito, sulle prepagate e sulle carte di debito XME Card emesse dal Gruppo.

Per i cellulari con sistema operativo Android, XME Pay permette già da tempo di collegare le proprie carte a Samsung Pay.

Inoltre, i pagamenti diventano “social” grazie a JiffyPay, che dal 1° gennaio diventerà Bancomat Pay, modalità di pagamento account to account. Usando il solo numero di telefono, JiffyPay permette anche di scambiare denaro durante una conversazione in chat. Oltre che di creare dei gruppi per dividere il conto di una cena o fare una colletta per un regalo agli amici.

A oggi, rende noto Intesa Sanpaolo, sono 650.000 i Jiffy scambiati. E più di 200.000, in soli tre mesi, i clienti che hanno attivato XME Pay su smartphone Android e Apple.

Intesa Sanpaolo Investo

Intesa Sanpaolo Investo è l’ultima nata nell’ecosistema di app della banca. Come suggerisce il nome, si tratta dell’app dedicata agli investimenti. Permette di fare trading in modo semplice, veloce e informato. E di tenere sotto controllo il proprio patrimonio, attraverso un’esperienza digitale vicina a quella in filiale con il proprio gestore.

Investo offre ai clienti un’ampia gamma di informazioni. Gli utenti possono utilizzare la ricerca avanzata per muoversi tra migliaia di titoli quotati. Così come visualizzare per ogni azienda un corredo informativo completo di dati di bilancio, indicatori finanziari, storico quotazioni, grafici avanzati interattivi e notizie correlate.

L’app consente di creare e gestire portafogli virtuali per simulare le strategie di trading. Inoltre di monitorare con la watchlist i propri titoli preferiti. È inoltre possibile attivare widget per consultare i titoli preferiti e le notizie più rilevanti, senza la necessità di accedere all’app.

Uno sguardo al futuro

Nella settimana dal 7 al 15 dicembre Intesa Sanpaolo promuoverà, in collaborazione con Mastercard, l’iniziativa Cashless District. Questa incentiverà i pagamenti attraverso il portafoglio digitale XME Pay nelle quattro aree metropolitane di Torino, Milano, Roma e Napoli.

Nel 2019 è atteso il lancio di Bancomat Pay, l’iniziativa che nasce dall’accordo tra Sia e Bancomat. Permetterà ai titolari di carte PagoBancomat di saldare operazioni attraverso il cellulare, senza dover più digitare alcun codice. Questo darà, si aspetta la banca, grande impulso ai micro-pagamenti digitali. Intesa Sanpaolo supporterà l’evoluzione digitale del Bancomat, rendendo gratuite per gli esercenti le transazioni di piccolo importo.

Sempre nel 2019, il Gruppo lancerà una nuova app per esercizi commerciali. Questa fungerà da unico punto di accettazione di tutti i pagamenti: contactless con carte o smartphone, Bancomat Pay, Alipay e così via. E anche come unico cruscotto di rendicontazione integrata multibanca, con conseguente abbattimento dei costi e maggiore engagement con i negozianti.

Non sarà più necessario avere un POS: per l’esercente sarà sufficiente avere un tablet o uno smartphone. L’app fornirà anche un supporto di marketing, grazie al nuovo servizio Infovendite. Questo sarà utile per analizzare le dinamiche di incasso e comparare le performance rispetto alle best practice di settore presenti nel territorio. Nonché individuare le aree di ottimizzazione del proprio business.

Da sinistra: Massimo Tessitore, direzione multicanalità integrata Intesa Sanpaolo, Marcella Cocco, assessore alla trasformazione digitale e servici civici, Stefano Barrese, responsabile divisione Banca dei Territori di Intesa Sanpaolo, Arrigo Giana, direttore generale di Atm e Nando Pagnoncelli, presidente Ipsos

Intesa Sanpaolo è inoltre uno dei partner tecnologici di ATM Milano per abilitare l’ingresso nella metropolitana semplicemente avvicinando ai tornelli qualsiasi carta Mastercard o VISA contactless o virtualizzata sul telefono.

Con la Pubblica Amministrazione la collaborazione punta ad ampliare le opportunità di pagamenti digitali e l’introduzione di nuovi servizi. Ad esempio tramite l’abbinamento dell’utente con il solo numero di telefono su PagoPA e l’accettazione digitale presso l’Anagrafe del Comune di Milano.

L'articolo Intesa Sanpaolo, la banca verso la digital transformation è un contenuto originale di 01net.


          Six Principles Of Data-Driven Transformation      Cache   Translate Page      
AI, the basis of the Fourth Industrial Revolution, will completely change the way business is done and companies are run in the next five to ten years, just as the Internet has done in the last ten. Nir Kaldero, author of "Data Science for Executives: Leveraging Machine Intelligence to Drive Business ROI" shares more.
          Software Development Engineer - Video Advertising (Data Science & Analytics) - Amazon.com - Seattle, WA      Cache   Translate Page      
As a founding team member, you’ll contribute to crucial choices regarding technology selection and architecture....
From Amazon.com - Wed, 03 Oct 2018 07:19:13 GMT - View all Seattle, WA jobs
          Modeler 18G01-Cyber Risk - Risk Management Solutions (RMS) - Noida, Uttar Pradesh      Cache   Translate Page      
Knowledge and experience with advanced methods of actuarial modelling. For example, applied mathematics, computer science, data science, statistics, actuarial...
From Risk Management Solutions (RMS) - Sat, 01 Dec 2018 13:58:59 GMT - View all Noida, Uttar Pradesh jobs
          Modeler 18G01-Cyber Risk - Risk Management Solutions - Noida, Uttar Pradesh      Cache   Translate Page      
Knowledge and experience with advanced methods of actuarial modelling. For example, applied mathematics, computer science, data science, statistics, actuarial...
From Risk Management Solutions - Sat, 01 Dec 2018 11:08:07 GMT - View all Noida, Uttar Pradesh jobs
          Lynda.com: R Programming in Data Science: High Variety Data      Cache   Translate Page      
In a perfect world, every dataset would be stored as XML text with context for every piece of information. Numbers would never be stored as strings. Decimal values would never be stored as scientific notation. Strings would never be longer than 500 characters. But obviously, we don't live in a perfect world of data. And big data only makes this issue, well, bigger. This is the problem of variety; data arriving in multiple formats. Data scientists spend an inordinate amount of time with this problem, using brain power that would be better spent on valuable analysis tasks. In this course, Mark Niemann-Ross introduces the problem of data variety and demonstrates how to use the unique capabilities of R to solve them. Learn how to import a wide variety of data, from Excel to ODS files.
          Computer Science - Assistant Professor (Data Science) - Bishop's University - Québec City, QC      Cache   Translate Page      
Divisional Secretary Natural Sciences. Computer Science - Assistant Professor (Data Science)....
From University Affairs - Fri, 09 Nov 2018 18:28:16 GMT - View all Québec City, QC jobs
          dvc 0.22.0      Cache   Translate Page      
Git for data scientists - manage your code and data together
          NVIDIA Unveils TITAN RTX GPU for Accelerated Ai      Cache   Translate Page      

Today NVIDIA introduced the TITAN RTX as what the company calls "the world’s most powerful desktop GPU" for AI research, data science and creative applications. "Driven by the new NVIDIA Turing architecture, TITAN RTX — dubbed T-Rex — delivers 130 teraflops of deep learning performance and 11 GigaRays of ray-tracing performance. Turing is NVIDIA’s biggest advance in a decade – fusing shaders, ray tracing, and deep learning to reinvent the GPU,” said Jensen Huang, founder and CEO of NVIDIA. “The introduction of T-Rex puts Turing within reach of millions of the most demanding PC users — developers, scientists and content creators.”

The post NVIDIA Unveils TITAN RTX GPU for Accelerated Ai appeared first on insideHPC.


          NLP Data Scientist - PARC, a Xerox company - Palo Alto, CA      Cache   Translate Page      
PARC, a Xerox company, is in the Business of Breakthroughs®. We create new business options, accelerate time to market, augment internal capabilities, and...
From PARC, a Xerox company - Sat, 29 Sep 2018 08:33:49 GMT - View all Palo Alto, CA jobs
          Data Scientists - Client Analytics - National Bank of Canada - Montréal, QC      Cache   Translate Page      
Are you a big data guru? Would you like to make the most of your talent to improve our client experience?...
From Banque Nationale du Canada - Thu, 15 Nov 2018 14:10:16 GMT - View all Montréal, QC jobs
          Data Scientist - Prodigy Game - Toronto, ON      Cache   Translate Page      
Prodigy Game will provide accommodations to job applicants with disabilities throughout the recruitment process....
From Prodigy Game - Sun, 25 Nov 2018 08:10:30 GMT - View all Toronto, ON jobs
          **REMOTE** PHP Developer      Cache   Translate Page      
NC-Charlotte, 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 -
          (Big) Data Engineer      Cache   Translate Page      
Currently I am recruiting for a (big) data engineer who will work closely with developers, product managers and data science teams (in various projects) to develop, implement, test and measure new ideas. Requirements: • Experience with Scala and Java 8 programming languages • Hands-on experience with a big-data ecosystem – Hadoop, Yarn, Spark, Kafka, Flink, Cassandra, Kafka Streams, Luigi, Airflow • Knowledge and experience with Python is a plus • Proficient in designing efficient and robust ETL workflows • Preferred experience with a cloud computing environment – Amazon EC2, Google Cloud, Azure Do you recognize yourself in this profile or do you know anyone that could be interested? Let me know and I will reach out asap to you!...
          Senior Data Scientist, Operations (Zillow Offers) - Zillow Group - Seattle, WA      Cache   Translate Page      
Figure out what's important to the business, to specific partners, and intuit core needs from people before they realize they need it. About the team....
From Zillow Group - Wed, 21 Nov 2018 12:24:52 GMT - View all Seattle, WA jobs
          Data Architect (Aws)      Cache   Translate Page      
Het project behelst: - Een migratie van een lokaal Data Center naar een AWS Cloud oplossing - Een wijziging van SQL opslag naar een data lake, in combinatie met een “just-in-case” opslag filosofie - Het verbeteren van de inhoudelijke registratie. - Voorbereid zijn om effectief te kunnen starten met Data Science projecten Dit vanuit BI gebaseerd op een bestaand, maar nog niet geheel compleet, Logisch Data Model...
          R Programming in Data Science: High Variety Data [Video]-P2P      Cache   Translate Page      
In a perfect world, every dataset would be stored as XML text with context for every piece of information. Numbers would never be stored as strings. Decimal values would never be stored as scientific notation. Strings would never be longer than 500 characters. But obviously, we don’t live in a perfect world of data. And [...]
          6 Ways Marketing Automation Is Evolving for CMOs      Cache   Translate Page      

As a CMO, your job is to be ready for every marketing development. You’re never allowed to stand still, because the moment you do, something in your industry will change, and you’ll fall behind the competition.

Unfortunately, turnover for marketing jobs is increasing. One survey revealed that the average tenure of a marketing CMO is less than four years, and burnout is the number one cause. Automation is the solution to avoiding this burnout. It’s one of those constantly-evolving trends that’s a hot topic for marketers. 

Many CMOs are hesitant to engage in automation because it can have negative effects, like being banned from accounts or poor engagement. However, the landscape for automation is changing, and CMOs don’t have to be as scared of automation as they once were. In fact, you should be using it to your advantage as it continues to improve. 

You have enough on your plate without having to worry about spending enough time on social media to engage with users and create a strong brand. Here are some automation advancements that you’ll likely appreciate as a CMO. 

1. Real Engagement Is Possible 

In the past, automation bots were always a problem. Many CMOs refused to work with them because they caused awkward situations, lost your followers, and even got your account suspended or banned. 

However, automation has come a long way. There are still bots and automation services that can pose problems, but many more offer high-quality services that allow you to connect with real followers and not fake accounts. For example, SocialCaptain gets Instagram followers with strategic automation. The platform gets you real engagement as a result of authentic strategies guided by AI expert algorithms. 

There are great tools for every social media platform, not just Instagram. You can free up a lot of your time by using these tools while maintaining the desired engagement from your target audience. 

2. More Accessible Data 

In an interview with, Forbes, Matt Gay, CMO for Accenture, said that data is one of the marketers’ biggest challenges, particularly when it comes to the ability to use it. “It’s got to be in a form and format that is easily accessible and useable going forward, so you don’t have to have an IT person sorting through and making static reports,” he says. 

Thankfully, there is automation to handle a lot of the legwork in data analysis. The right software tools can help CMOs not only gather data but turn it into insights that can be used for advertising and marketing purposes. 

“CMOs would traditionally use data scientists and their analytics,” Gay says, pointing out that this is no longer a necessity with today’s machine algorithms that can do more than data scientists ever could. 

“Even with analytics and algorithms, these processes are manual, slow, and not leveraging the full capability of data scientists,” he continues. “As well, since machines can process massive amounts of data quickly, they will uncover insights faster and more efficiently than humans…allowing the data scientists to use their brains more and continue to seed the algorithms.” 

It’s an incredible tool that every chief marketer should be leveraging for the promotion of their business. 

3. Provides More Context 

Machine learning and artificial intelligence have come leaps and bounds in the last few years. They can now gather contextual clues much better than before, limiting inaccurate and sometimes embarrassing mistakes of the past. 

“Brands can use automation for contextual marketing, offering more personalized interactions,” says Linda Turley, an attorney at Turley Law Firm, who has experienced this first hand. “For example, data shows you when your target audience is usually online. Then, you can set your content calendar to post content at these times,” she explained. 

Additionally, data can show you trending topics, high-performing posts on social media, and highly effective advertisements in your industry. This information gathered with bots and other automated tools can give consumers the customized experience they most desire. 

4. More Valuable SEO 

SEO was once all about the use of keywords and link building to make your content searchable. However, it’s turning into a more personalized experience where quality content that’s highly valuable is king. 

Automation offers us what’s known as semantic SEO, “which aims to decipher what kind of question the person typing words into a search bar is actually trying to answer,” says Matthew Walker-Jones of Marketing Tech News. 

These semantics add value to SEO everywhere, offering a more tailored approach to understanding and catering to user behavior. 

“This marketing technique goes a long way to improve the traffic of a website by employing meaningful metadata through the removal of ambiguity in search queries and further creates clusters of content, grouped semantically by topic rather than keywords, developing meaningfully-connected networks that better respond to user searches,” he says. 

5. Augmented Reality Changes Social Media 

Connecting with consumers on social media is getting much more complicated and involved now that augmented reality is a part of the automation chapter. More and more users are demanding augmented reality, especially on social media. 

Many brands are responding, including major makeup producers like Estée Lauder and Loreal that allow you to try on colors with Facebook Messenger bots before they make a purchase through the app. Brick-and-mortar companies are also using AR to give consumers a tour of their businesses. 

AR is not out of your reach, no matter the size of your business. Automation tools not only provide insights on the highest-performing AR platforms, but they also make them more readily available. Simple smartphone apps and software tools are affordable and provide many options for incorporating these themes into existing marketing campaigns. 

6. Improved Integrations 

In the past, very few automation tools worked together. You utilized separate tools that ignored the others, giving you an incomplete picture of the collected data. Non-integrating automation tools still exist, but they’re becoming the minority. 

This is good news because seamless integration of every automation tool you use is vital to delivering a holistic experience for the customer. 

“The strategic focus of marketing automation needs to shift from the campaign or component approach to the customer journey in totality to deliver on the seamless customer experience promise,” advises an article from Martech Advisor that focuses on where automation will be by 2020. 

By the time we reach 2020, the current marketing landscape will be completely different, thanks to the prevalence of automation. These changes continue for the better, helping CMOs focus on what’s most important while staying on top of their engagement and marketing campaigns. 

Having trouble convincing your CEO that Marketing Automation is the key to sales and marketing success? We've got you covered. Download our free guide.


          Deep learning in Satellite imagery      Cache   Translate Page      
In this article, I hope to inspire you to start exploring satellite imagery datasets. Recently, this technology has gained huge momentum, and we are finding that new possibilities arise when we use satellite image analysis. Satellite data changes the game because it allows us to gather new information that is not readily available to businesses. […] Artykuł Deep learning in Satellite imagery pochodzi z serwisu Appsilon Data Science | End­ to­ End Data Science Solutions.
          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
          Intern - Data Scientist (NAND) - Micron - Boise, ID      Cache   Translate Page      
Machine learning and other advanced analytical methods. To ensure our software meets Micron's internal standards....
From Micron - Wed, 29 Aug 2018 20:54:50 GMT - View all Boise, ID jobs
          Intern - Data Scientist (DRAM) - Micron - Boise, ID      Cache   Translate Page      
Machine learning and other advanced analytical methods. To ensure our software meets Micron's internal standards....
From Micron - Mon, 20 Aug 2018 20:48:37 GMT - View all Boise, ID 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
          Directeur actuariat, Équipe de science des données -Manager Data Scientist - Aviva - Montréal, QC      Cache   Translate Page      
An English version will follow Vous allez vous joindre à une équipe d’actuaires et de scientifiques et d’ingénieurs de données passionnés, chargée de mettre...
From Aviva - Tue, 16 Oct 2018 17:53:50 GMT - View all Montréal, QC jobs
          Financial Manager Role in Data Science & Machine Learning Sector      Cache   Translate Page      
To be confirmed - Cape Town, Western Cape - in the Data Science and Machine Learning Sector. The existing accounting function is currently being shared between an assistant... An interest in Data Science and eagerness to learn about it...
          Omron: AI controller      Cache   Translate Page      
Sysmac AI Controller
Omron Automation Americas announces the Sysmac AI Controller an artificial intelligence solution that collects, analyzes, and utilizes data on edge devices within a controller to prolong equipment longevity.

The Sysmac AI Controller handles several key steps in the data-driven decision process for predictive maintenance, thereby freeing up industrial professionals from tedious calculations, analyses, and infrastructure upgrades.

This solution can help manufacturers reduce the risk of bad parts or equipment damage by detecting issues early on and prompting immediate action to resolve them. Customers will be able to take advantage of Omron’s advanced technology and its team of data scientists to facilitate predictive maintenance rather than figuring everything out on their own.

The AI functionality – also known as “machine learning” – is able to identify abnormal machine behavior without being explicitly programmed to do so. Since there could be many different factors and measurements that indicate an issue when observed together, automating the feature extraction process saves a significant amount of time and resources. Leveraging the machine learning results during production is key to ensuring end user cost savings.

The process of collecting raw data from machines is completely automated by the new AI controller which operates on the “Edge” within the machine, ensuring higher data fidelity and consistency. In addition, the controller automatically creates data models from correlation analysis and monitor machine status based on that model. Without this automation, machine designers and operators would otherwise need to develop their own analytics and optimization capabilities in order to avoid cloud solution costs.

Rather than being a cloud solution, Omron’s unique approach to artificial intelligence-based control involves hardware, offline software and in-person service. No internet connectivity or IT infrastructure/service is required. The hardware is based on the Sysmac NY5 IPC and the NX7 CPU and includes Omron’s AI Application Components, a library of AI predictive maintenance function blocks. Several additional AI specific utilities are also included.

Because the data collection and analysis is performed within the same hardware as the controls program, the solution provides the utmost data processing speed, accuracy and security. For the service component, Omron experts will assist in startup and provide periodic support.


          Data Scientist      Cache   Translate Page      
Die MTU Aero Engines entwickelt, fertigt, vertreibt und betreut zivile und militärische Antriebe …
          MoEngage raises $9M funding led by Matrix Partners India & VenturEast      Cache   Translate Page      
MoEngage will use these funds to expand its global presence and further strengthen the data science capabilities
          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
          Développeur / Data Scientist - Python - Resolution -- Compliance Consulting - Montréal, QC      Cache   Translate Page      
Résolution est une jeune entreprise technologique en croissance œuvrant dans le domaine de la conformité réglementaire (RegTech). Résolution se spécialise...
From Indeed - Sun, 25 Nov 2018 16:33:06 GMT - View all Montréal, QC jobs
          Data Scientist - Terminal - Montréal, QC      Cache   Translate Page      
Development experience using numpy, pandas, scikit-learn, tensorflow... Hims is a fast-growing men’s wellness brand who believes taking care of yourself is...
From Terminal - Sat, 10 Nov 2018 01:01:14 GMT - View all Montréal, QC jobs
          Senior or Lead Pirate Data Scientist - Autodesk - Montréal, QC      Cache   Translate Page      
R, Pandas, Jupyter, scikit, TensorFlow, etc. Senior or Lead Pirate Data Scientist....
From Autodesk - Fri, 09 Nov 2018 16:53:17 GMT - View all Montréal, QC jobs
          Data Scientist / Statistical Quant Mathematician - Contract      Cache   Translate Page      
Salary: £450 - £550 per day + Free Parking. Location: . Data Scientist / Quant Mathematician - Statistical / Data Modelling | £450 - £550 Per Day High Wycombe. HP11 £450 - £550 per day + Free Parking (6 Month Contract ongoing) Experience using C++ or Python is highly regarded, although Matlab & R experience will also be considered. Company: We are a leading producer of form and data related applications for the horse racing industry. With our head office based in Sydney, Australia, we are expanding our UK presence with a view to trading the global markets. Position Summary: Working with some of the best mathematics and stats minds in the industry in a small and focused team environment, you will work on challenging and rewarding modelling projects. To be considered for this role you will have the following: + A strong background in mathematical and statistical modelling + Previous experience working as a Quant Analyst, Data Analyst, Data Scientist or Big Data Analyst - desirable + Masters or PhD in maths / statistics / machine learning / econometrics essential. + Experience using C++ or Python is highly regarded, although Matlab & R experience will also be considered. + Strong algorithmic coding skill and solid knowledge of data structures + Experience with statistical forecasting essential. + Understanding of machine / statistical learning techniques such as non-linear regression, kernel regression, support vector machines (SVM), neural networks, classification trees and similar techniques very beneficial. + Experience working within the horse racing / wagering industry highly desirable This is a great opportunity for someone passionate about maths and stats to have their ideas put into practice. You may have worked in the following capacities: Data Modeller, Statistical Modelling, Big Data Analyst, Banking, Finance, Gambling / Gaming Sector, Statistical Modelling Application notice... We take your privacy seriously. When you apply, we shall process your details and pass your application to our client for review for this vacancy only. As you might expect we may contact you by email, text or telephone. Your data is processed on the basis of our legitimate interests in fulfilling the recruitment process. Please refer to our Data Privacy Policy & Notice on our website for further details. If you have any pre-application questions please contact us first quoting the job title & ref. Good luck, Team RR.
          Machine Learning Primer for Clinicians–Part 7      Cache   Translate Page      
Alexander Scarlat, MD is a physician and data scientist, board-certified ...
          Hitachi Vantara Releases Cloud-Based Data Integration, Analytics Platform      Cache   Translate Page      
Hitachi Vantara has unveiled a software platform that works to help customers integrate and analyze data in hybrid cloud environments. Pentaho 8.2 is designed to operate with the Hitachi Content Platform to support data optimization and storage-related functions, the company said Tuesday. The integrated offering allows data scientists and analysts manage data from structured and unstructured […]
          Data Science Consultant - Accenture - Montréal, QC      Cache   Translate Page      
Accenture is looking for talented individuals who want to be part of this change by joining Accenture Applied Intelligence, the world’s largest team in applying...
From Accenture - Sat, 24 Nov 2018 02:34:32 GMT - View all Montréal, QC jobs
          Data Science Manager - Accenture - Montréal, QC      Cache   Translate Page      
Choose Accenture, and make delivering innovative work part of your extraordinary career. Accenture Analytics delivers insight driven outcomes at scale to help...
From Accenture - Sat, 24 Nov 2018 02:30:23 GMT - View all Montréal, QC jobs
          Canada Region - Data Science Lead - Accenture - Montréal, QC      Cache   Translate Page      
Ensure Accenture has comprehensive knowledge capital and intellectual property that is protected, differentiated and strategic for the Canadian market across...
From Accenture - Sat, 24 Nov 2018 02:26:15 GMT - View all Montréal, QC jobs
          Cloudera beschleunigt die KI-Industrialisierung mit Cloud nativer Machine-Learning-Plattform      Cache   Translate Page      

München, Palo Alto (Kalifornien), 5. Dezember 2018 – Cloudera, Inc. (NYSE: CLDR) hat eine Vorschau auf eine neue, Cloud-basierte Machine-Learning-Plattform der nächsten Generation auf Basis von Kubernetes veröffentlicht. Das kommende Cloudera Machine Learning erweitert das Angebot von Cloudera für Self-Service Data Science im Unternehmen. Es bietet eine schnelle Bereitstellung und automatische Skalierung sowie eine containerisierte, […]

Der Beitrag Cloudera beschleunigt die KI-Industrialisierung mit Cloud nativer Machine-Learning-Plattform erschien zuerst auf IT-I-Ko.


          Data Scientists - Client Analytics - National Bank of Canada - Montréal, QC      Cache   Translate Page      
Are you a big data guru? Would you like to make the most of your talent to improve our client experience?...
From Banque Nationale du Canada - Thu, 15 Nov 2018 14:10:16 GMT - View all Montréal, QC jobs
          Indie-Siyensya to Honor Winning Science Films on Nov. 28      Cache   Translate Page      

Wazzup Pilipinas!

The Department of Science and Technology-Science Education Institute will award the winners of the third Indie-Siyensya science filmmaking competition on November 28, Wednesday, at the Philippine International Convention Center in Pasay City.

The winners were chosen by Indie-Siyensya's distinguished board of judges from the final entries submitted in the Youth and Open categories, with the themes "What My Community Needs Now" and "What My Country Needs Now," respectively.

Three films from each category will receive trophies and cash prizes: 100,000 pesos for the Best Film, 50,000 pesos for the second prize and 25,000 pesos for the third prize. The competition's main organizer will also give a special Viewer’s Choice Award, with a cash prize of 10,000 pesos, to the film with the most number of votes from the Indie-Siyensya audience.

Judging this year's Indie-Siyensya are advocacy filmmaker and educator Seymour Sanchez from the De La Salle-College of Saint Benilde Digital Filmmaking program and Far Eastern University Department of Communication; Prof. Patrick Campos, director of the University of the Philippines Film Institute; Prof. Garry Jay Montemayor, chair of the Department of Science Communication, College of Development Communication, UP Los Baños; Dr. Mudjekeewis "Mudjie" Santos, father and founder of the Genetic Fingerprinting Laboratory under the Department of Agriculture-National Fisheries Research and Development Institute; and astrophysicist and data scientist Dr. Reinabelle "Reina" Reyes, who became known as "the Filipina who proved Einstein right" with her work on his Theory of Relativity.

The judges screened more than 300 film concept proposals for short documentaries that capture science as it works to answer the needs of communities and the country. The criteria for judging are Scientific Content and Adherence to the Theme (50% for the Youth category and 40% for the Open category), Execution of Idea (30% for both categories), and Film Techniques (20% for Youth and 30% for Open).

“We were surprised by the number of individuals and groups interested in joining this year’s Indie-Siyensya. We’re hopeful that projects like this will encourage more students to choose science courses,” DOST-SEI Director Dr. Josette Biyo shared.

Indie-Siyensya partnered with the Film Development Council of the Philippines to hold a free science communication and film workshop last July 6 at the Cinematheque Centre Manila. The workshop, which aims to help equip participants with the skills needed to create visually appealing and credible science films, was facilitated by Montemayor, 2017 Breakthrough Junior Challenge Challenge winner Hillary Diane Andales and director of photography Joshua Reyles.








Aside from organizing Indie-Siyensya, DOST-SEI primarily spearheads the country’s premier science scholarship programs and conducts teacher training and science promotion programs for the youth.


          Hydrogen International Limited: Commercial Lawyer - 3m FTC      Cache   Translate Page      
Negotiable: Hydrogen International Limited: My client, an international customer data science company who derive tailored marketing strategies for leading FMCG brands is now looking to hire a co England, London, Hammersmith and Fulham
          Collider Cup III, Dec 5      Cache   Translate Page      
The Collider Cup is SCET's all-star showcase of the best student teams from this Fall 2018 semester. Come watch teams pitch to panels of professors, investors and industry experts as they vie to win the grand prize, the Collider Cup!

New this semester, the top three teams from the event will receive automatic final round meetings with Arrow Capital for the potential to receive $15K to $50K in seed funding to pursue their startup ventures.

We will also have the following funds in attendance and ready to meet with students:

Free Ventures, UC Berkeley's nonprofit, student-run startup accelerator,
Dorm Room Fund, a student-run venture fund backed by First Round,
The Trione Student Venture Fund which provides $5,000 grants and office space to early-stage startups involving current Haas students.
The day will be divided into four parts:

The morning will showcase six of the 12 classes from this Fall with an intro from the instructor, one student team presenting their project, followed by Q&A and feedback from a panel of judges.
Lunch & Networking with SCET students and instructors. Lunch will be provided, so be sure to RSVP to reserve your spot for food!
The afternoon will showcase the remaining 6 classes from this Fall with an intro from the instructor, one student team presenting their project, followed by Q&A and feedback from a panel of judges.
We will close with a short reception awarding The Collider Cup grand prize for the all-around winning student team while enjoying drinks and small bites.
Agenda:

9:45 am Check-in
10:00 am Welcome from Ikhlaq Sidhu, Faculty Director & Founder of SCET and Ken Singer, Managing Director of SCET.

Morning Fall Showcase (10:05 am - 11:35 am)
10:05 am Berkeley Method of Entrepreneurship Bootcamp (192) with Gigi Wang
10:20 am Applied Data Science with Venture Applications (135/290) with Ikhlaq Sidhu
10:35 am Amazoogle Data-Driven Business Models (185) with Shomit Ghose
10:50 am Challenge Lab: Blockchain (185) with Luke Kowalski and Alexander Fred-Ojala
11:05 am Technology Entrepreneurship (191) with Naeem Zafar
11:20 am Challenge Lab: Global Startup (191) with Ken Singer

Lunch & Networking (11:35 am - 12:30 pm)
Certificate of Entrepreneurship & Technology with Jesse Dieker

Afternoon Fall Showcase (12:30 pm - 2:00 pm)
12:30 pm Lean Transfer (190E/290) with Naeem Zafar & Rhonda Shrader
12:45 pm Technology Entrepreneurship (191) with Leah Edwards
1:00 pm Product Design (109E) with Rachel Powers
1:15 pm Challange Lab: Sports Tech (185) with Stephen Torres & Danielle Vivo
1:30 pm The A. Richard Newton Lecture Series (95/195) with Victoria Howell
1:45 pm Startup Semester with David Law

Reception & grand prize award ceremony (2:00 pm - 3:00 pm)
Enjoy drinks and small bites with all of our guests. During this time, the SCET judges will pick the all-around winning team and award them with The Collider Cup grand prize at 2:30 pm!
We look forward to welcoming you as we showcase the top projects from this semester and introduce you to all that SCET has to offer! Be sure to RSVP if you plan to join us for lunch so that we can be sure to have enough food.

See you soon for SCET's Collider Cup 2018!
          Microsoft open sources the inference engine at the heart of its Windows machine- ...      Cache   Translate Page      

Microsoft is using its annual Connect(); developers conference to make a number of AI-related announcements, including the open sourcing of one of its key pieces of its windows Machine Learning (Windows ML) platform .


Microsoft open sources the inference engine at the heart of its Windows machine- ...
Credit: Microsoft

Microsoft is open sourcing the Open Neural Network Exchange (ONNX) runtime , officials said today, December 4. The ONNX runtime is an inference engine for machine-learning models in the ONNX format. Microsoft is making it available on GitHub so developers can customize and integrate the runtime into their existing systems and compile/build it on a variety of operating systems.


Microsoft open sources the inference engine at the heart of its Windows machine- ...
Credit: Microsoft

The ONNX engine is a key piece of Windows ML. Microsoft is building this machine-learning interface into Windows 10 to try to get developers to use trained machine learning models in their Windows apps. The Windows ML inference engine can evaluate trained models locally on Windows devices, instead of requiring developers to run them in the cloud.

Microsoft and Facebook announced the ONNX format in 2017 in the name of enabling developers to move deep-learning models between different AI frameworks, including its own Cognitive Toolkit (CNTK). More recently, Microsoft officials have started downplaying the Microsoft Cognitive Toolkit , in favor of Facebook's PyTorch and Google's TensorFlow, as reported last month by CNBC.

When I asked Microsoft about CNBC's report, a spokesperson provided the following statement:

"Microsoft believes an open ecosystem will help bring AI to everyone. We're seeing traction for ONNX and python with developers and data scientists so we are increasing our investments in those areas, while we continue to support Microsoft Cognitive Toolkit. We have nothing else to share at this time."

Microsoft also is making its Azure Machine Learning service generally available as of today, December 4. Azure ML enables developers and data scientists to build, train and deploy machine-learning models. My ZDNet colleague Andrew Brust has more details on Azure ML .

In other AI news, Microsoft is continuing to flesh out its Azure Cognitive Services application-programming interface (API) strategy. Today, Microsoft is adding container support for its Language Understanding API (in preview form). Recently, Microsoft announced it was making available several other of its Cognitive Services APIs in containers to enable developers to bring these AI capabilities offline and to edge devices.


          Data Scientists - Client Analytics - National Bank of Canada - Montréal, QC      Cache   Translate Page      
Are you a big data guru? Would you like to make the most of your talent to improve our client experience?...
From Banque Nationale du Canada - Thu, 15 Nov 2018 14:10:16 GMT - View all Montréal, QC jobs
          Acutely Hires Verizon Senior Data Scientist      Cache   Translate Page      

Acutely continues to grow their team to deliver rich insights for the food and entertainment industries

Chicago, IL -- (ReleaseWire) -- 12/05/2018 -- Acutely, a leading restaurant site selection technology, announced today that it has hired Moises Arturo Diaz Tomas as a Senior Data Scientist.

"I'm very excited to work for Acutely and deliver value using my passion for technology. Humans are never in calm, they want to grow, they are always looking for innovation and I really enjoy helping people to innovate, to make decisions using technology and grow together."

Moises joins the team with a background as one of the leading Data Scientists in South America. Having received his degree in Engineering Informatics from the Universidad Nacional Jose Faustino Sanchez Carrion; Moises embarked on a career as a leader in data science and artificial intelligence.

Bringing over 10 years of experience in analysis, design, development and implementation of IT Solutions and Services; Moises most recently worked for Verizon Wireless as a Data Scientist, Tech Leader. Moises is thrilled to bring his background and extensive experience to Acutely's clients.

"We are thrilled to add someone with the experience and talent that Moises brings to Acutely. His expertise will play a vital role in our growth and helping our clients make these important decisions." Mathew Focht, CEO.

About Acutely
Acutely combines industry experience with result-focused data science to produce real-world business answers. Your business produces lots of data about your customers, transactions, and operations. We turn that raw data into actionable insights for your business.

Learn more at acutely.com

For more information on this press release visit: http://www.releasewire.com/press-releases/acutely-hires-verizon-senior-data-scientist-1094190.htm

Media Relations Contact

Haywood Wright
Business Manager
Acutely
Email: Click to Email Haywood Wright
Web: https://acutely.com/

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          Announcing Kaggle integration with Google Data Studio      Cache   Translate Page      
Kaggle and Data Studio

As of today, Kaggle is now officially integrated with Data Studio, Google’s serverless business intelligence and data visualization platform. You’ll be able to connect to and visualize Kaggle datasets directly from Data Studio using the Kaggle Community Connector.


Kaggle is the world's largest online community of data scientists. Its two million users come to the platform to explore, create, and share data projects using Kernels, a free hosted notebook IDE. Over 10,000 public datasets can be analyzed using Kernels, accessed via the web or Kaggle's public API. This new integration, users can analyze these datasets in Kaggle; and then visualize findings and publish their data stories using Data Studio.


Here’s an example dashboard in Data Studio using a Kaggle dataset:

As a free-to-use reporting solution, Data Studio makes it easier for users to understand their data, derive key insights, and effectively communicate findings using compelling interactive dashboards. Data Studio is creating an innovative landscape where users can spend less time building their data pipeline and more time on creating data stories and sharing them with the right audience.


With this integration, users can browse to a dataset in Kaggle, pick a file, and use the one-click integration button to launch Data Studio with the selected data. From there, users can create and publish their own interactive dashboard, which can be embedded in websites and blogs. Since there is no cost to use Data Studio and the infrastructure is handled by Google, users don't have to worry about scalability, even if millions of people view the dashboard.


Here’s a quick clip showing how easy it is to use this integration to build dashboards in Data Studio:

Dashboards

See Connecting Kaggle Datasets to Data Studio to learn more.


Data Studio helps data professionals bring the power of visual analytics to their data. The hassle-free publishing process means everyone can tell engaging stories, open up dashboards for others to interact with, and make better-informed decisions. We're also releasing the connector code for this integration in the Data Studio Open Source Repository. This should help both Data Studio developers and Kaggle users to build newer and better solutions.


To get started, try out a Kaggle maintained dataset and launch the Kaggle connector for Data Studio. Let’s analyze more data and build awesome dashboards!



          Technical Product Manager - Data Science - GoDaddy - Kirkland, WA      Cache   Translate Page      
Deliver the infrastructure to provide the business insights our marketing team needs. The small business market contains over a hundred million businesses...
From GoDaddy - Wed, 10 Oct 2018 21:04:32 GMT - View all Kirkland, WA jobs
          New Leaders for Future Thinking’s GemSeek and cQuest      Cache   Translate Page      
UK-based research and data science firm Future Thinking has promoted Petko Tinchev as CEO of its GemSeek and cQuest subsidiaries, from 1st ...
          NVIDIA Announces Beastly New Titan RTX      Cache   Translate Page      
Later on in the press release, NVIDIA says that it's "built for AI researchers and deep learning developers," claims it's "perfect for data scientists," and ...
          NVIDIA Announces Beastly New Titan RTX      Cache   Translate Page      
Later on in the press release, NVIDIA says that it's "built for AI researchers and deep learning developers," claims it's "perfect for data scientists," and ...
          Global Data Science Platform Market Share, Revenue Generated By Major Players & Industry Size …      Cache   Translate Page      
Data Science Platform Market report would come in handy to understand your competitors and give you an insight about sales, volumes, revenues in ...
          CANCELED | Thinkful + Free Geek: Getting Started in Data Science      Cache   Translate Page      
Eventbrite - Thinkful Portland presents CANCELED | Thinkful + Free Geek: Getting Started in Data Science - Tuesday, December 4, 2018 at Free Geek ...
          Data Scientist - Yamaha - Cypress, CA      Cache   Translate Page      
Develop statistical models, machine learning-based tools or processes to measure and manage business performance....
From Yamaha - Wed, 22 Aug 2018 00:54:18 GMT - View all Cypress, CA jobs
          Principal Data Scientist - Deep Learning - QuantumBlack - Montréal, QC      Cache   Translate Page      
Work closely with Data Engineers, Machine Learning Engineers and Designers to build end-to-end analytics solutions for our clients that drive real impact in the...
From QuantumBlack - Thu, 25 Oct 2018 16:08:33 GMT - View all Montréal, QC jobs
          2018 Year in Review: Security and DevOps Talks from Salesforce      Cache   Translate Page      
2018 Year in Review: Security and DevOps Talks from Salesforce

Laura Lindeman

We’ve had a great year on the conference circuit, with close to 100 Salesforce employees highlighting their work externally in a talk! We’re sharing a roundup of some of the talks that were captured on tape in a series of three posts, organized by category. Feel free to bookmark the posts and come back later when you need a little break from holiday craziness to spend some time learning.

Up first: Security and DevOps.


2018 Year in Review: Security and DevOps Talks from Salesforce
Photo by Kane Reinholdtsen on Unsplash Security Fingerprinting Encrypted Channels for Detection JohnAlthouse

Talk by John Althouse at DerbyCon

Last year we open sourced JA3, a method for fingerprinting client applications over TLS, and we saw that it was good. This year we tried fingerprinting the server side of the encrypted communication, and it’s even better. Fingerprinting both ends of the channel creates a unique TLS communication fingerprint between client and server making detection of TLS C2 channels exceedingly easy. I’ll explain how in this talk. What about non-TLS encrypted channels? The same principal can be applied. I’ll talk about fingerprinting SSH clients and servers and what we’ve observed in our research. Are those SSH clients what they say they are? Maybe not.

Tweet about it:

@ DerbyCon

@ 4A4133

#Security

Compromising Online Accounts by Cracking Voicemail Systems

Talk by Martin Vigo at DEFCON 2018

Voicemail systems have been with us since the 80s. They played a big role in the earlier hacking scene and re-reading those e-zines, articles and tutorials paints an interesting picture. Not much has changed. Not in the technology nor in the attack vectors. Can we leverage the last 30 years innovations to further compromise voicemail systems? And what is the real impact today of pwning these? In this talk I will cover voicemail systems, it’s security and how we can use oldskool techniques and new ones on top of current technology to compromise them. I will discuss the broader impact of gaining unauthorized access to voicemail systems today and introduce a new tool that automates the process.

Tweet about it:

@ defcon

@ martin_vigo

#Security

Get the Right Security Tools into your Enterprise

Talk by Sam Harwin at ACoD

Security professionals often struggle with getting buy-in, influencing their organizations and helping define the value of security tools. Further, we often focus on the technical aspects to the detriment of the ‘people’ and ‘process’ resulting in solutions that don’t get implemented to support the organization’s purpose or for security.

Also onour blog!

Fuzzing Malware For Fun andProfit

Talk by Maksim Shudrak at DEFCON

Practice shows that even the most secure software written by the best engineers contain bugs. Malware is not an exception. In most cases their authors do not follow the best secure software development practices thereby introducing an interesting attack scenario which can be used to stop or slow-down malware spreading, defend against DDoS attacks and take control over C&Cs and botnets. Several previous researches have demonstrated that such bugs exist and can be exploited. To find those bugs it would be reasonable to use coverage-guided fuzzing. This talk aims to answer the following two questions: ___ we defend against malware by exploiting bugs in them? How can we use fuzzing to find those bugs automatically? The author will show how we can apply coverage-guided fuzzing to automatically find bugs in sophisticated malicious samples such as botnet Mirai which was used to conduct one of the most destructive DDoS in history and various banking trojans. A new cross-platform tool implemented on top of WinAFL will be released and a set of 0day vulnerabilities will be presented. Do you want to see how a small addition to HTTP-response can stop a large-scale DDoS attack or how a smart bitflipping can cause RCE in a sophisticated banking trojan? If the answer is yes, this is definitely your talk.

Tweet about it:

@ defcon

@ MShudrak

#Security

DevOps Distributed Tracing: From theory topractice

Stella Cotton | Distributed tracing: From theory to practice

Traditional application performance monitoring is great for debugging a single app but how do you debug a system with… slideslive.com

Traditional application performance monitoring is great for debugging a single app but how do you debug a system with multiple services? Distributed tracing can help! You’ll learn the theory behind how distributed tracing works. But we’ll also dive into other practical considerations you won’t get from a README, like choosing libraries for your polyglot systems, infrastructure considerations, and security.

Tweet about it:

@ WebExpo

@ practice_cactus

#Monitoring

Performance anomaly detection atscale

Watch the talk by Tuli Nivas at Velocity Conference(O’Reilly login required)

Automated anomaly detection in production using simple data science techniques enables you to more quickly identify an issue and reduce the time it takes to get customers out of an outage. Tuli Nivas shows how to apply simple statistics to change how performance data is viewed and how to easily and effectively identify issues in production.

Tweet about it:

@ VelocityConf

@ TuliNivas

#DevOps

Check back next week for another roundup post, featuring talks on Machine Learning, AI, and Big Data.


          Cloudera And ParallelM Partner To Accelerate The Industrialization Of AI      Cache   Translate Page      
ParallelM, the leader in MLOps, today announced a partnership with Cloudera to add options for bringing machine learning (ML) models from Cloudera ML development environments, including Cloudera Data Science Workbench (CDSW) and the upcoming cloud-native Cloudera Machine Learning platform, into production using ParallelMamp;rsquo;s MCenter.
          Data Scientist (Bi Analyst) - Kiss Products, Inc. - Headquarters, BC      Cache   Translate Page      
Bachelor’s degree in Marketing / Statistics / Computer Science / Management. Advanced degree in statistics, computer science, or machine-learning related fields...
From Kiss Products, Inc. - Wed, 21 Nov 2018 09:02:24 GMT - View all Headquarters, BC jobs
          CONSULENTE DATA SCIENTIST SENIOR/JUNIOR - Prisma S.r.l. - Junior, WV      Cache   Translate Page      
Prisma Srl opera nel settore dell’Information Technology dal 1984. Attraverso il continuo monitoraggio delle tecnologie emergenti e l’attenta valorizzazione...
From Prisma S.r.l. - Thu, 27 Sep 2018 07:51:39 GMT - View all Junior, WV jobs
          Data Scientist (Bi Analyst) - Kiss Products, Inc. - Headquarters, BC      Cache   Translate Page      
The position will make value out of data by creating and developing various machine learning-based tools or processes within the company....
From Kiss Products, Inc. - Wed, 21 Nov 2018 09:02:24 GMT - View all Headquarters, BC jobs
          Sr. Data Scientist - Microsoft - Redmond, WA      Cache   Translate Page      
We live in the Age of Data. And we LOVE Data! Data and insights from data power an increasing range of applications, transforming not just the technology...
From Microsoft - Mon, 13 Aug 2018 22:59:44 GMT - View all Redmond, WA jobs
          NVIDIA Unveils TITAN RTX GPU for Accelerated Ai      Cache   Translate Page      

Today NVIDIA introduced the TITAN RTX as what the company calls "the world’s most powerful desktop GPU" for AI research, data science and creative applications. "Driven by the new NVIDIA Turing architecture, TITAN RTX — dubbed T-Rex — delivers 130 teraflops of deep learning performance and 11 GigaRays of ray-tracing performance. Turing is NVIDIA’s biggest advance in a decade – fusing shaders, ray tracing, and deep learning to reinvent the GPU,” said Jensen Huang, founder and CEO of NVIDIA. “The introduction of T-Rex puts Turing within reach of millions of the most demanding PC users — developers, scientists and content creators.”

The post NVIDIA Unveils TITAN RTX GPU for Accelerated Ai appeared first on insideHPC.


          Collider Cup III, Dec 5      Cache   Translate Page      
The Collider Cup is SCET's all-star showcase of the best student teams from this Fall 2018 semester. Come watch teams pitch to panels of professors, investors and industry experts as they vie to win the grand prize, the Collider Cup!

New this semester, the top three teams from the event will receive automatic final round meetings with Arrow Capital for the potential to receive $15K to $50K in seed funding to pursue their startup ventures.

We will also have the following funds in attendance and ready to meet with students:

Free Ventures, UC Berkeley's nonprofit, student-run startup accelerator,
Dorm Room Fund, a student-run venture fund backed by First Round,
The Trione Student Venture Fund which provides $5,000 grants and office space to early-stage startups involving current Haas students.
The day will be divided into four parts:

The morning will showcase six of the 12 classes from this Fall with an intro from the instructor, one student team presenting their project, followed by Q&A and feedback from a panel of judges.
Lunch & Networking with SCET students and instructors. Lunch will be provided, so be sure to RSVP to reserve your spot for food!
The afternoon will showcase the remaining 6 classes from this Fall with an intro from the instructor, one student team presenting their project, followed by Q&A and feedback from a panel of judges.
We will close with a short reception awarding The Collider Cup grand prize for the all-around winning student team while enjoying drinks and small bites.
Agenda:

9:45 am Check-in
10:00 am Welcome from Ikhlaq Sidhu, Faculty Director & Founder of SCET and Ken Singer, Managing Director of SCET.

Morning Fall Showcase (10:05 am - 11:35 am)
10:05 am Berkeley Method of Entrepreneurship Bootcamp (192) with Gigi Wang
10:20 am Applied Data Science with Venture Applications (135/290) with Ikhlaq Sidhu
10:35 am Amazoogle Data-Driven Business Models (185) with Shomit Ghose
10:50 am Challenge Lab: Blockchain (185) with Luke Kowalski and Alexander Fred-Ojala
11:05 am Technology Entrepreneurship (191) with Naeem Zafar
11:20 am Challenge Lab: Global Startup (191) with Ken Singer

Lunch & Networking (11:35 am - 12:30 pm)
Certificate of Entrepreneurship & Technology with Jesse Dieker

Afternoon Fall Showcase (12:30 pm - 2:00 pm)
12:30 pm Lean Transfer (190E/290) with Naeem Zafar & Rhonda Shrader
12:45 pm Technology Entrepreneurship (191) with Leah Edwards
1:00 pm Product Design (109E) with Rachel Powers
1:15 pm Challange Lab: Sports Tech (185) with Stephen Torres & Danielle Vivo
1:30 pm The A. Richard Newton Lecture Series (95/195) with Victoria Howell
1:45 pm Startup Semester with David Law

Reception & grand prize award ceremony (2:00 pm - 3:00 pm)
Enjoy drinks and small bites with all of our guests. During this time, the SCET judges will pick the all-around winning team and award them with The Collider Cup grand prize at 2:30 pm!
We look forward to welcoming you as we showcase the top projects from this semester and introduce you to all that SCET has to offer! Be sure to RSVP if you plan to join us for lunch so that we can be sure to have enough food.

See you soon for SCET's Collider Cup 2018!
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Thanks for another fun month of content and community on Opensource.com! Last month the site brought in 1,004,107 unique visitors who generated 1,524,240 page views. We published 84 articles in November and welcomed 17 new writers:


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          Machine Learning and CrateDB, Part Two: Getting Started With Jupyter      Cache   Translate Page      

Inpart one of this miniseries, I shared my excitement about machine learning and introduced you to things like predictive maintenance and problem formalization.

In this post, I briefly explain how machine learning fits into the larger discipline of data science and then go on to show you how to get started on a toy project using three open source tools:

CrateDB

A distributed SQL database.

Jupyter Notebook

A web-based notebook for interactive computing.

Pandas

A data analysis library for python.

I'm using macOS, but the following instructions should be trivially adaptable for linux or windows.

Let's go.

Data Science

To really make the best use of machine learning, we need to understand the problems we are trying to solve. And we need to be able to contextualize, interpret, and leverage the results that machine learning produces.

That frequently necessitates the application of statistical modeling, mathematics, data analysis, information science, computer science, and so on.


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

(Adapted from Data Science Venn Diagram by Shelly Palmer.)

Data science is the interdisciplinary field that brings all of these together.

But What Does a Data Scientist Actually Do?

Data scientist and author Joel Grus says that “a data scientist is someone who extracts insights from messy data.”

A data scientist might be expected to:

Design and conduct surveys or studies Interpret that data Design data processing algorithms Produce predictive models Build prototypes and proof of concepts

And so on...

Data Science as a Process

Data science is the combination of multiple fields. But it's also important to think of data science as a process.

I particularly like this representation of the data science process:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

(Adapted from the Harvard CS 109: Data Science lecture slides by Hanspeter Pfister, Joe Blitzstein, and Verena Kaynig.)

As you can see from the diagram, the data science process is iterative and non-linear, and different skills are required for each step.

We often start by asking a question that needs data to answer.

From there, we collect and prepare the data, explore it, model it, interpret it, and communicate the results.

Crucially, at any stage, additional insight gained may require us to go back one step. Perhaps we need more data, or different data. Maybe we need to prepare it or model it differently. All of this can change as we understand more about the data.

Sometimes it happens that you realize the question itself was flawed, and that a different question is better.

The Science Bit

"Here comes the science bit. Concentrate..."

Data science is also a science.

That seems like a redundant thing to mention, but it's important to stress. Fundamentally, data scientists apply the scientific method to understand data.

Data scientists:

Make observations Formulate hypothesis Test hypothesis Evaluate the results

Lather, rinse, repeat .

Critically, the scientific method demands that we take a skeptical view of our hypotheses.

As Edward Teller, the famous Hungarian-American physicist, once said , a "fact is a simple statement that everyone believes. It is innocent, unless found guilty. A hypothesis is a novel suggestion that no one wants to believe. It is guilty, until found effective."

Let's Get Started

Okay, with that quick introduction to data science and how it relates to machine learning, let's set up a data science environment so you can get started.

I'm going to show you how to start playing around with data from CrateDB using the Pandas library and Jupyter Notebook.

Install CrateDB

If you don't already have CrateDB running locally, it's relatively effortless to get set up.

Pop open a terminal and run this command:

$ bash -c "$(curl -L https://try.crate.io/)"

This command justdownloads CrateDB and runs it from the tarball. If you'd like to actually install CrateDB a bit more permanently, or you are using Windows, check out our collection of super easy one-step install guides .

If you're using the command above, it should pop open the CrateDBadmin UI for you automatically once it has finished. Otherwise, head over to http://localhost:4200/ in your browser.

You should see something like this:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter
Get Some Data

If you’re playing around with a fresh CrateDB install, it's likely that you don't have any data. So head on over to the Help screen by selecting the question mark icon on the left-hand navigation menu.

The help screen looks like this:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

Select IMPORT TWEETS FOR TESTING and follow the instructions to authenticate your Twitter account.

Don't worry. This isn’t going to post anything on your behalf. It doesn’t even look at your tweets. All this does is import a bunch of recent public tweets on Twitter.

Once you're done, select the Tables icon from the left-hand navigation, and then choose the tweets table. You should end up here:

http://localhost:4200/#/tables/doc/tweets

Which should look like this:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

Okay, great!

We've got some tweets. Let's do something with this data.

Install Anaconda

Instead of installing Pandas and the Jupyter Notebook manually, we're going to install Anaconda, which is an open source data science platform that comes with both Pandas and Jupyter Notebook.

Head over to the Anaconda download page .

Select the Python 3.7 version.

When the download has finished, run the installer:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

Select Continue and follow the instructions.

If you get asked to install Microsoft VSCode , you can just select Continue , because we won't be needing it.

When you're done, Anaconda should be installed.

Install the CrateDB Python Client Library

We're going to be using Python to access CrateDB, so before we continue, let's install the appropriate library.

Anaconda should have modified your system $PATH so that python points to the version of Python that ships with Anaconda.

Anaconda achieves this by appending a line to your ~/.bashrc or ~/.bash_profile files. You can revert back to your original Python setup at any time.

Check this worked, like so:

$ which python
/anaconda3/bin/python

Pip (the Python package manager) should also be pointing to the version that ships with Anaconda:

$ which pip
/anaconda3/bin/pip

Then, install the CrateDB Python client library , like so:

$ pip install crate Create Your First Notebook

What do I mean by notebook ?

In the context of computer science, a notebook is a sort of cross between a word processing document and an interactive shell. Specifically, Jupyter Notebook is a web application that "allows you to create and share documents that contain live code, equations, visualizations, and narrative text."

Jupyter Notebook is the successor to the IPython Notebook. IPython itself being a more feature-rich alternative to the Python interactive shell .

Start the Anaconda Navigator :

$ anaconda-navigator

You should see something like this:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

Select Launch from the Jupyter box (top center).

This will open Jupyter Notebook in a new browser window:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

This shows a list of files in your home directory.

Typically, you would navigate to your notebook files and open them from this interface. But as we don't have any notebook files yet, let's create one.

Select New from the top right-hand navigation menu, and then Python 3 , like so:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

This should open a new tab with a blank notebook:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter
Here, you can see a box with In [ ]: (meaning "input") and then an input field.

You can type Python code into the input field:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter
Hover over the In [ ]: text and you should see a play icon appear. If you press this icon, your Python code will be run:
Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

Okay, great!

Query CrateDB From Jupyter

Now we have our first notebook set up, let's import our Twitter data into Jupyter using Pandas.

Pandas is a library that "provides high-performance, easy-to-use data structures, and data analysis tools for the Python programming language."

Firstly, we need to import the pandas module.

Select the "+" icon from the top left-hand Jupyter notebook navigation menu.

A new In [ ]: box should appear:
Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

Type the following input:

import pandas as pd

Then run the input.


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

Nothing should happen. That's good. It means that the import was successful and didn't raise any exceptions.

Next, let's define an SQL statement for querying CrateDB. We want to fetch all distinct users and their followers and friends.

limit = 100000
sql_query = """
SELECT DISTINCT account_user['id'] AS id,
account_user['followers_count'] AS followers,
account_user['friends_count'] AS friends
FROM tweets LIMIT {};
""".format(limit)

Again, this should produce no output:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

Let's execute this statement and return the results as a DataFrame .

A DataFrame is the Pandas data structure that corresponds to a table of rows. The DataFrame includes functionality that allows us to perform various arithmetic operations on the data, as well as SQL-like post-processing operations such as aggregates and joins.

Type the following:

try:
df_data = pd.read_sql(
sql_query, 'crate://localhost:4200', index_col='id')
except Exception:
print('Is CreatDB running and are the tweets imported?')

Here, we're connecting to CrateDB on localhost:4200 , executing our prepared statement, and returning the results as a DataFrame named df_data using id as the index column. If the connection or the query errors out, an exception is raised, and an error message is printed.

Again, if everything worked, there should be no output:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

Now we have our DataFrame we can display it:

display(df_data.head(10))

This displays the first 10 rows:


Machine Learning and CrateDB, Part Two: Getting Started With Jupyter

Neat!

Don't forget to give your notebook a name. Click "Untitled" at the top of the window and choose something useful.

Wrap Up

In this post, I spoke about how machine learning is one component of data science. Then I showed you how to get started with Jupyter.

From here, you might want to start poking around with Pandas to see what else you can do with the data from CrateDB.

In part three of this miniseries, I will show you how to use these tools to implement a linear regression model to predict the number of followers a Twitter user has depending on their number of friends (the people they follow).




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