How to innovate in the Age of Big Data presented by Stephen Brobst

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Executive Summer Session

Stephen Brobst will be at the European Data Innovation Hub. We asked him to share his views on the importance of open data, open source, analytics in the cloud and data science. Stephen is on the forefront of the technology and we can’t wait to hear what is happening in the Silicon Valley. Count on it that you will leave the workshop inspired and weaponed with some actionable ideas that will help us to define a profitable strategy for the data science teams.

Format of the session :

  • 15:00 – Keynote:How to innovate in the Age of Big Data
  • 15:50 – Open Discussion on “Sustainable Strategies for Data Science, tackling following topics:
  • Data Science is the Key to Business Success
  • Three Critical Technologies Necessary for Big Data Exploitation
  • How to Innovate in the Age of Big Data
  • 16:45 – Networking Session

Stephen Brobst is the Chief Technology Officer for Teradata Corporation.  Stephen performed his graduate work in Computer Science at the Massachusetts Institute of Technology where his Masters and PhD research focused on high-performance parallel processing. He also completed an MBA with joint course and thesis work at the Harvard Business School and the MIT Sloan School of Management.  Stephen is a TDWI Fellow and has been on the faculty of The Data Warehousing Institute since 1996.  During Barack Obama’s first term he was also appointed to the Presidential Council of Advisors on Science and Technology (PCAST) in the working group on Networking and Information Technology Research and Development (NITRD).  He was recently ranked by ExecRank as the #4 CTO in the United States (behind the CTOs from Amazon.com, Tesla Motors, and Intel) out of a pool of 10,000+ CTOs.

Job – Junior Data Scientist

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Are you pursuing a career in data science?

We have a great opportunity for you: an intensive training program combined with interesting job opportunities!

Interested? Check out http://di-academy.com/bootcamp/ follow the link to our datascience survey and send your cv to training@di-academy.com

Once selected, you’ll be invited for the intake event that will take place in Brussels this summer.

Hope to see you there,

Nele & Philippe

Analytics: Lessons Learned from Winston Churchill

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I had the pleasure to be invited for lunch by Prof. Baessens earlier this week and we talked about a next meetup subject that could be ‘War and Analytics’. As you might know Bart  is a WWI fanatic and he has already written a nice article on the subject called ‘Analytics: Lessons Learned from Winston Churchill’

here is the article—

Nicolas Glady’s Activities

Activities Overview‎ > ‎Online articles‎ > ‎ Analytics: Lessons Learned from Winston Churchill

Analytics has been around for quite some time now.  Even during World War II, it proved critical for the Allied victory. Some famous examples of allied analytical activities include the decoding of the enigma code, which effectively removed the danger of submarine warfare, and the 3D reconstruction of 2D images shot by gunless Spitfires, which helped Intelligence at RAF Medmenham eliminate the danger of the V1 and V2 and support operation Overlord. Many of the analytical lessons learned at that time are now more relevant than ever, in particular those provided by one of the great victors of WWII, then Prime Minister, Sir Winston Churchill.

The phrase “I only believe in statistics that I doctored myself” is often attributed to him. However, while its wit is certainly typical of the Greatest Briton, it was probably a Nazi Propaganda invention. Even so, can Churchill still teach us something about statistical analyses and Analytics?

 

A good analytical model should satisfy several requirements depending upon the application area and follow a certain process. The CRISP-DM, a leading methodology to conduct data-driven analysis, proposes a structured approach: understand the business, understand the data, prepare the data, design a model, evaluate it, and deploy the solution. The wisdom of the 1953 Nobel Prize for literature can help us better understand this process.

Have an actionable approach: aim at solving a real business issue

Any analytics project should start with a business problem, and then provide a solution. Indeed, Analytics is not a purely technical, statistical or computational exercise, since any analytical model needs to be actionable. For example, a model can allow us to predict future problems like credit card fraud or customer churn rate. Because managers are decision-makers, as are politicians, they need “the ability to foretell what is going to happen tomorrow, next week, next month, and next year… And to have the ability afterwards to explain why it didn’t happen.” In other words, even when the model fails to predict what really happened, its ability to explain the process in an intelligible way is still crucial.

In order to be relevant for businesses, the parties concerned need first to define and qualify a problem before analysis can effectively find a solution. For example, trying to predict what will happen in 10 years or more makes little sense from a practical, day-to-day business perspective: “It is a mistake to look too far ahead. Only one link in the chain of destiny can be handled at a time.”  Understandably, many analytical models in use in the industry have prediction horizons spanning no further than 2-3 years.

Understand the data you have at your disposal

There is a fairly large gap between data and comprehension. Churchill went so far as to argue that “true genius resides in the capacity for evaluation of uncertain, hazardous, and conflicting information.”  Indeed, Big Data is complex and is not a quick-fix solution for most business problems. In fact, it takes time to work through and the big picture might even seem less clear at first. It is the role of the Business Analytics expert to really understand the data and know what sources and variables to select.

Prepare the data

Once a complete overview of the available data has been drafted, the analyst will start preparing the tables for modelling by consolidating different sources, selecting the relevant variables and cleaning the data sets. This is usually a very time-consuming and tedious task, but needs to be done: “If you’re going through hell, keep going.”

Never forget to consider as much past historical information as you can. Typically, when trying to predict future events, using past transactional data is very relevant as most of the predictive power comes from this type of information. “The longer you can look back, the farther you can look forward.”

read more here

Launching the first Data Science Bootcamp in Europe

We are so happy to launch the first European  data science bootcamp

It is so nice to write this page on the launch of the first European data science bootcamp that will start this summer in Brussels. This initiative will boost the digital transformation effort of each company by allowing them to improve their data skills either by recruiting trainees and young graduates or transforming existing BI teams to become experienced business data scientists.

Intense 5+12 weeks approach to focus on practical directly applicable business cases.

The content of this bootcamp originated from the Data Science Community. Following the advice of our academic,  innovation and training partners we have decided to offer a unique hands-on 5 + 12 weeks approach.

  1. We call the first 5 weeks the Summer Camp (starts Aug 16th).  The participants work onsite or remote on e-learning MOOCs from DataCamp to demonstrate their ability to code in Python, R, SAS, SQL and to master statistical principles. During this period experts put all their energy into coaching the candidates in keeping up the pace and finishing the exercises. All the activities take place in our training centre located in the European Data Innovation Hub.
    -> If you are a young graduate you can expect to be contacted by tier one companies who will offer you a job or traineeship that will start with the participation to the datascience bootcamp.
  2. The European Data Science Bootcamp starts September 19thDuring a 12 week period – every Monday and Tuesday – participants will work on 15 different business cases presented by business experts from different industries and covering diverse business areas. Each Friday, the future data scientists will gather to work on their own business case, with coaching by our data experts to achieve an MVP (Minimum Viable Product) at the conclusion of the bootcamp.

Delivering strong experienced business data science professionals after an intense semester of hands-on business cases.

Companies are invited to reserve seats for their own existing staff or for the young graduates who have expressed interest in following the bootcamp.

 Please reserve your seat(s) now as, this bootcamp is limited to 15 participants.

Please contact Nele Coghe on training@di-academy.com or click on di-Academy to learn more information about this first European Data Science Bootcamp.

  • Here  is the powerpoint presentation explaining the Bootcamp.
  • Here is the presentation done by Nele during the Data Innovation Summit.

Hope to see you soon at the Hub,

Philippe Van Impe
pvanimpe@di-academy.com

20% #StrataHadoop discount is back – San Jose Mar 28-31

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Strata + Hadoop World is the leading event on how big data and ubiquitous, real-time computing is shaping the course of business and society. It brings together the world’s best data scientists and business leaders to share hard-won knowledge and innovations in technology and strategy.

Check out the impressive program and make plans to join Strata + Hadoop World in San Jose March 28-31, 2016.

 

 

Save 20% on most passes with discount code UGBDSC.

 

“The best opportunity to learn about the technologies that are transforming big data and data science.”

Join Us on our visit to the Silicon Valley ecosystem:

We plan to arrive on Saturday, we will rent a house for our community members in San Francisco from Saturday 26/3 to Tuesday 29 then we will move to a house in San Jose.

We will have a van to drive to the different startups and meetups.

Thank You:

We get one free for each 3 tickets booked using our 20% discount code UGBDSC. 

LINK: http://www.oreilly.com/pub/cpc/5243

Agenda of our Visit:

We have lined up a few companies that we want to meet. If you can help us and facilitate the access to an interesting company please let us know.

We have already received some nice invitations from:

We plan to attend a few meetups too, like to one from SF DataScience on Monday about  ‘Kafka and Data Science’ , …

On arrival the ‘Sons of Analytics’ will rent a bike to attend the LAUGHLIN RIVER RUN .

And of course we will travel to San-Jose to attend the Strata Hadoop event. Please always use our discount code at any O’Reilly activity: our 20% discount code UGBDSC

Costs

Our main objective is to learn and have fun, we have no plans to make any profit out of this organisation, the plan is to share the costs amongst the participants and to take 2 volunteers from the community with us that will travel free.

Our first estimation is that you should be able to do this for less than 3000.

Flight 800
Accomodation 6 300
Food and drinks 600
Car rental 120
Peto cash 200
Ticket Strata 1000
3020

Job – ULB – Academic position in Data Science for Bioinformatics and Computational Biology

ULB

Faculty of Sciences, Department of Computer Science.

Required Qualifications :

PhD Degree (with doctoral thesis) in Computer Science, Bioinformatics, Computational Biology, Bioengineering, or equivalent qualifications.

Required Skills :

  • A minimum of 4-year scientific career at the time of hiring
  • Postdoctoral experience of minimum one year and an excellent scientific record
  • A stay abroad in an academic institution other that the one in which the doctoral studies were undertaken is mandatory. This can be during or after the doctoral studies. The academic stay abroad needs to have taken place either during a full academic year or maximum 4 academic stays adding up to a 12 month period
  • For non-French speaking natives, a learning period may be granted, but candidates must be capable of teaching in French (level C1 is required) at the end of the third year following their appointment.

The Faculty of Sciences of the Université Libre de Bruxelles (ULB) announces the opening of a full-time academic position in Data Science for Bioinformatics and Computational Biology starting October 1st, 2016. The position will be in affiliation with the Department of Computer Science of the ULB and the ULB/VUB Interuniversity Institute for Bioinformatics in Brussels (IB2). The following web pages can also be consulted for further information: http://www.ulb.ac.be/di (Computer Science Dept. homepage), http://www.ibsquare.be (Interuniversity Institute for Bioinformatics in Brussels), mlg.ulb.ac.be (ULB Machine Learning Research Group)

1 Description of Position :

Candidates are expected to trigger and promote active collaborations within the context of IB2 with molecular, biological and medical research groups at the ULB. They are furthermore expected to lead a high-quality research and teaching program in the specified areas, preferably with a specialization in the analysis of genomic, transcriptomic and/or epigenomic data and the design and deployment of advanced data science (e.g. machine learning, data mining, big data) methods. Significant research and teaching experience, as well as a strong publication record in international journals and/or high impact international conferences are required. The successful candidate will be invited to apply for a grant from the European Research Council (ERC) and for any sources of outside funding (FNRS, Europe, Regional funds, etc…) enabling them to develop their research. The ULB Research Department will assist with applications. As stated by its statutes, the University of Brussels is a non-discriminating institution and all its members are expected to adhere to its fundamental principles.

  • Description of Scientific and Pedagogical Objectives:
    The candidate will take part in teaching activities in the Bachelor and Master programs in Computer Science and the Master in Bioinformatics and Modeling and will participate in the supervision of Master dissertations. The first years she/he will have a reduced teaching charge. After a few years this charge will increase to reach a level comparable to that of her/his colleagues (typically 4 or 5 hours a week for two semesters, plus some supervision of exercise sessions). The ability to strengthen existing research areas at ULB will be considered as an asset, too. The position involves also commitment to administrative tasks. She/ he will furthermore be expected to take an active role in the management and supervision of the research activities in the Interuniversity Institute for Bioinformatics in Brussels (IB2). For any additional information (e.g. concerning courses to be taught or the research carried out in the Department) please see contact details below.
  • Field of Research :
    All applicants need to hold either a PhD in Computer Science, Bioinformatics, Computational Biology, Bioengineering, or related disciplines. Experience in interdisciplinary collaboration as well as significant research stays in foreign universities or research laboratories are important assets.
  • The teaching duties at the time of hiring will include :
    The first years the applicant will be involved in Master-level courses in Bioinformatics and Computational Biology currently organized in the Master in Computer Science and the Master in Bioinformatics and Modeling. For candidates not fluent in French, a temporary period of teaching in English may be granted for teaching at the Master level. After this period (for up to three years) the position will become permanent, requiring that the applicant will also be able to teach in French at the Bachelor level.

2. Teaching duties may be reviewed periodically and are subject to modification over time.

3. For further information, please see link: http://wwwdev.ulb.ac.be/greffe/files/5008.pdf

4. The application file should also include the following :

  • application letter
  • CV, including a list of publications (cf. template downloadable at http://www.ulb.ac.be/tools/CV-type.rtf)
  • documents attesting to 4 years of teaching and research experience
  • a report of around 3500 signs on the candidate’s research activities and research project, indicating how the candidate would integrate into ULB research teams
  • a report of about 3500 signs on the candidate’s previous teaching activities and a project describing their teaching mission during the 5 years following their appointment, that integrates, in a coherent manner, into the vision of the unit in which the candidate will be working and the educational profiles of the programmes on which the candidate will be teaching.
  • a note on international projects and achievements
  • please provide full names and email addresses for five referees who may be contacted by the university bodies responsible of evaluating candidate applications. We ask that you be mindful of providing a balanced gender divide between referees and ensure that they have no conflict of interest. Candidates applying for several vacancies are required to send a separate file for each one.

Internal administrative data : Vacancy number : 15/A076 University payroll position : 15-B-CCO-127 (F) (1.00 ETP) Administrative Board reference : CoA. 23/11/15 pt III.02

The ABC of Datascience blogs – collaborative update

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A – ACID – Atomicity, Consistency, Isolation and Durability

B – Big Data – Volume, Velocity, Variety

C – Columnar (or Column-Oriented) Database

  • CoolData By Kevin MacDonell on Analytics, predictive modeling and related cool data stuff for fund-raising in higher education.
  • Cloud of data blog By Paul Miller, aims to help clients understand the implications of taking data and more to the Cloud.
  • Calculated Risk, Finance and Economics

D – Data Warehousing – Relevant and very useful

E – ETL – Extract, transform and load

F – Flume – A framework for populating Hadoop with data

  • Facebook Data Science Blog, the official blog of interesting insights presented by Facebook data scientists.
  • FiveThirtyEight, by Nate Silver and his team, gives a statistical view of everything from politics to science to sports with the help of graphs and pie charts.
  • Freakonometrics Charpentier, a professor of mathematics, offers a nice mix of generally accessible and more challenging posts on statistics related subjects, all with a good sense of humor.
  • Freakonomics blog, by Steven Levitt and Stephen J. Dubner.
  • FastML, covering practical applications of machine learning and data science.
  • FlowingData, the visualization and statistics site of Nathan Yau.

G – Geospatial Analysis – A picture worth 1,000 words or more

H – Hadoop, HDFS, HBASE

  • Harvard Data Science, thoughts on Statistical Computing and Visualization.
  • Hyndsight by Rob Hyndman, on fore­cast­ing, data visu­al­iza­tion and func­tional data.

I – In-Memory Database – A new definition of superfast access

  • IBM Big Data Hub Blogs, blogs from IBM thought leaders.
  • Insight Data Science Blog on latest trends and topics in data science by Alumnus of Insight Data Science Fellows Program.
  • Information is Beautiful, by Independent data journalist and information designer David McCandless who is also the author of his book ‘Information is Beautiful’.
  • Information Aesthetics designed and maintained by Andrew Vande Moere, an Associate Professor at KU Leuven university, Belgium. It explores the symbiotic relationship between creative design and the field of information visualization.
  • Inductio ex Machina by Mark Reid’s research blog on machine learning & statistics.

J – Java – Hadoop gave it a nice push

  • Jonathan Manton’s blog by Jonathan Manton, Tutorial-style articles in the general areas of mathematics, electrical engineering and neuroscience.
  • JT on EDM, James Taylor on Everything Decision Management
  • Justin Domke blog, on machine learning and computer vision, particularly probabilistic graphical models.
  • Juice Analytics on analytics and visualization.

K – Kafka – High-throughput, distributed messaging system originally developed at LinkedIn

L – Latency – Low Latency and High Latency

  • Love Stats Blog By Annie, a market research methodologist who blogs about sampling, surveys, statistics, charts, and more
  • Learning Lover on programming, algorithms with some flashcards for learning.
  • Large Scale ML & other Animals, by Danny Bickson, started the GraphLab, an award winning large scale open source project

M – Map/Reduce – MapReduce

N – NoSQL Databases – No SQL Database or Not Only SQL

O – Oozie – Open-source workflow engine managing Hadoop job processing

  • Occam’s Razor by Avinash Kaushik, examining web analytics and Digital Marketing.
  • OpenGardens, Data Science for Internet of Things (IoT), by Ajit Jaokar.
  • O’reilly Radar O’Reilly Radar, a wide range of research topics and books.
  • Oracle Data Mining Blog, Everything about Oracle Data Mining – News, Technical Information, Opinions, Tips & Tricks. All in One Place.
  • Observational Epidemiology A college professor and a statistical consultant offer their comments, observations and thoughts on applied statistics, higher education and epidemiology.
  • Overcoming bias By Robin Hanson and Eliezer Yudkowsky. Present Statistical analysis in reflections on honesty, signaling, disagreement, forecasting and the far future.

P – Pig – Platform for analyzing huge data sets

  • Probability & Statistics Blog By Matt Asher, statistics grad student at the University of Toronto. Check out Asher’s Statistics Manifesto.
  • Perpetual Enigma by Prateek Joshi, a computer vision enthusiast writes question-style compelling story reads on machine learning.
  • PracticalLearning by Diego Marinho de Oliveira on Machine Learning, Data Science and Big Data.
  • Predictive Analytics World blog, by Eric Siegel, founder of Predictive Analytics World and Text Analytics World, and Executive Editor of the Predictive Analytics Times, makes the how and why of predictive analytics understandable and captivating.

Q – Quantitative Data Analysis

R – Relational Database – Still relevant and will be for some time

  • R-bloggers , best blogs from the rich community of R, with code, examples, and visualizations
  • R chart A blog about the R language written by a web application/database developer.
  • R Statistics By Tal Galili, a PhD student in Statistics at the Tel Aviv University who also works as a teaching assistant for several statistics courses in the university.
  • Revolution Analytics hosted, and maintained by Revolution Analytics.
  • Rick Sherman: The Data Doghouse on business and technology of performance management, business intelligence and datawarehousing.
  • Random Ponderings by Yisong Yue, on artificial intelligence, machine learning & statistics.

S – Sharding (Database Partitioning)  and Sqoop (SQL Database to Hadoop)

  • Salford Systems Data Mining and Predictive Analytics Blog, by Dan Steinberg.
  • Sabermetric Research By Phil Burnbaum blogs about statistics in baseball, the stock market, sports predictors and a variety of subjects.
  • Statisfaction A blog by jointly written by PhD students and post-docs from Paris (Université Paris-Dauphine, CREST). Mainly tips and tricks useful in everyday jobs, links to various interesting pages, articles, seminars, etc.
  • Statistically Funny True to its name, epidemiologist Hilda Bastian’s blog is a hilarious account of the science of unbiased health research with the added bonus of cartoons.
  • SAS Analysis, a weekly technical blog about data analysis in SAS.
  • SAS blog on text mining on text mining, voice mining and unstructured data by SAS experts.
  • SAS Programming for Data Mining Applications, by LX, Senior Statistician in Hartford, CT.
  • Shape of Data, presents an intuitive introduction to data analysis algorithms from the perspective of geometry, by Jesse Johnson.
  • Simply Statistics By three biostatistics professors (Jeff Leek, Roger Peng, and Rafa Irizarry) who are fired up about the new era where data are abundant and statisticians are scientists.
  • Smart Data Collective, an aggregation of blogs from many interesting data science people
  • Statistical Modeling, Causal Inference, and Social Science by Andrew Gelman
  • Stats with Cats By Charlie Kufs has been crunching numbers for over thirty years, first as a hydrogeologist and since the 1990s, as a statistician. His tagline is- when you can’t solve life’s problems with statistics alone.
  • StatsBlog, a blog aggregator focused on statistics-related content, and syndicates posts from contributing blogs via RSS feeds.
  • Steve Miller BI blog, at Information management.

T – Text Analysis – Larger the information, more needed analysis

U – Unstructured Data – Growing faster than speed of thoughts

V – Visualization – Important to keep the information relevant

  • Vincent Granville blog. Vincent, the founder of AnalyticBridge and Data Science Central, regularly posts interesting topics on Data Science and Data Mining

W – Whirr – Big Data Cloud Services i.e. Hadoop distributions by cloud vendors

X – XML – Still eXtensible and no Introduction needed

  • Xi’an’s Og Blog A blog written by a professor of Statistics at Université Paris Dauphine, mainly centred on computational and Bayesian topics.

Y – Yottabyte – Equal to 1,000 exabytes, 1 million petabytes and 1 billion terabytes

Z – Zookeeper – Help managing Hadoop nodes across a distributed network

Feel free to add your preferred blog in the comment bellow.

Other resources:

Nice video channels:

More Jobs ?

hidden-jobs1

Click here for more Data related job offers.
Join our community on linkedin and attend our meetups.
Follow our twitter account: @datajobsbe

Improve your skills:

Why don’t you join one of our  #datascience trainings in order to sharpen your skills.

Special rates apply if you are a job seeker.

Here are some training highlights for the coming months:

Check out the full agenda here.

Join the experts at our Meetups:

Each month we organize a Meetup in Brussels focused on a specific DataScience topic.

Brussels Data Science Meetup

Brussels, BE
1,417 Business & Data Science pro’s

The Brussels Data Science Community:Mission:  Our mission is to educate, inspire and empower scholars and professionals to apply data sciences to address humanity’s grand cha…

Next Meetup

DATA UNIFICATION IN CORPORATE ENVIRONMENTS

Wednesday, Oct 14, 2015, 6:30 PM
57 Attending

Check out this Meetup Group →

Event – Official Opening of the European Data Innovation Hub in Brussels – October 20th 10AM

HUBdatainnovation invitation

We are so pleased to announce that our Hub will officially be inaugurated by Alexander De Croo, Bianca Debaets, Frank Koster, Marietje Schaak and Jörgen Gren. Over 100 directors and managers have already confirmed their presence to this ribbon cutting ceremony that will be held in our new offices in the Axa building.

266px-Alexander_de_croo_675 bianca-in-brussel frank marietjejorgen_gren

Join us on October 20th at 10AM to meet young data startups, talk to representatives of the academic world, share your ideas with the politics representatives that support us and discover our datascience training offering.

There are only a few more spaces left so please hurry and reserve your seat via our eventbrite page.

I’m looking forward to meeting you in our new offices soon,

Philippe Van Impe
asbl European Data Innovation Hub vzw 
Inspire – Innovate – Connect

Coached MOOC – Edx – Data Science and Machine Learning Essentials – Brussels

Intro to machine learning

About this course

Demand for Data science talent is exploding. Learn these essentials with experts from M.I.T and the industry, partnering with Microsoft to help develop your career as a data scientist. By the end of this course, you will know how to build and derive insights from data science and machine learning models. You will learn key concepts in data acquisition, preparation, exploration and visualization along with examples on how to build a cloud data science solution using Azure Machine Learning, R & Python.

Data Science is an essential skill for analyzing and deriving useful insights from data, big and small. McKinsey estimates that by 2018, a 500,000 strong workforce of data scientists will be needed in US alone. The resulting talent gap must be filled by a new generation of data scientists.
This course is organized into 5 weekly modules each concluding with a quiz. By achieving a passing grade in the final course assessment you will receive a certificate demonstrating that you have acquired data science skills and knowledge. Apart from answering your questions on the forum, faculty will host an office hour to address questions you may have while undertaking this course.

Get an ID verified certificate to demonstrate your data science knowledge and share on Linked-in.

What you’ll learn

  • The data science process
  • Overview of data science theory
  • Data acquisition, ingestion, sampling, quantization, cleaning and transformation
  • Building data science workflows with Azure ML
  • Data science tools including R, Python and SQL
  • Data exploration and visualization
  • Building and evaluating machine learning models
  • Publishing machine learning models with the Azure ML

View Course Syllabus

Why we coach MOOCs

The European Data Innovation Hub is partnering with top experts to offer MOOC participants the possibility to do these online courses in group. During the duration of the Mooc participants will be welcome to come to the Hub in Brussels to work and to go through exercises with other participants. On specific days one or more domain expert will be present to coach the students.

Planning

  1. Sign up to this edx course here
  2. Join the meetup group here to reserve your seat.

Meet the online instructor:

bio for Dr. Steve Elston

Dr. Steve Elston

bio for Cynthia Rudin

Cynthia Rudin

Meet the coach:

datasiencebe.com mooc coaching  FullSizeRender (1)

Ward Vanden Berghe, Hendrik D’Oosterlinck

Certificate

Pursue a Verified Certificate to highlight the knowledge and skills you gain ($50)

View a PDF of a sample edX certificate
  • Official and Verified

    Receive a credential signed by the instructor, with the institution logo to verify your achievement and increase your job prospects

  • Easily Shareable

    Add the certificate to your CV, resume or post it directly on LinkedIn

  • Proven Motivator

    Get the credential as an incentive for your successful course completion

Job opportunities ?

hidden-jobs1

Click here for Data related job offers.
Join our community on linkedin and attend our meetups.
Follow our twitter account: @datajobsbe

Have you been to our Meetups yet ?

Each month we organize a Meetup in Brussels focused on a specific DataScience topic.

Brussels Data Science Meetup

Brussels, BE
1,382 Business & Data Science pro’s

The Brussels Data Science Community:Mission:  Our mission is to educate, inspire and empower scholars and professionals to apply data sciences to address humanity’s grand cha…

Next Meetup

How Data Science is Transforming Sales and Marketing

Thursday, Sep 24, 2015, 6:30 PM
180 Attending

Check out this Meetup Group →

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