Training – UGent – Big Data – 26/2 to 4/6

Ugent Big Data

download the brochure here

Big Data

Wetenschappelijke coördinatie:
Prof. dr. Guy De Tré
Vakgroep Telecommunicatie en Informatieverwerking, UGent

  • Module 1: Gegevensbeheer
    26 februari, 5, 12, 19, 26 maart 2015

  • Module 2: Gegevensanalyse
    2, 23 en 30 april, 7 en 21 mei 2015

  • Module 3: Visualisatie
    28 mei en 4 juni 2015

Wetenschappelijk Coördinator

  • Prof. dr. Guy De Tré, Vakgroep Telecommunicatie en Informatieverwerking, Universiteit Gent


  • Vivek Bajaj, IBM
  • Antoon Bronselaer, Vakgroep Telecommunicatie en Informatieverwerking, Universiteit Gent
  • Hans Constandt, Ontoforce
  • Thomas Demeester, Vakgroep Informatietechnologie, Universiteit Gent
  • Wesley De Neve, Vakgroep Elektronica en Informatiesystemen, Universiteit Gent
  • Guy De Tré, Vakgroep Telecommunicatie en Informatieverwerking, Universiteit Gent
  • Filip De Turck, Vakgroep Informatietechnologie, Universiteit Gent
  • Wim De Wispelaere, Amplidata
  • Erik Duval, Departement Computerwetenschappen, KU Leuven
  • Jan Fostier, Vakgroep Informatietechnologie, Universiteit Gent
  • Alain Houf, Intersystems
  • Joris Klerkx, Departement Computerwetenschappen, KU Leuven
  • Peter Lambert, Vakgroep Elektronica en Informatiesystemen, Universiteit Gent
  • Femke Ongenae, Vakgroep Informatietechnologie, Universiteit Gent
  • Dirk Van den Poel, Vakgroep Marketing, Universiteit Gent
  • Inge Van Nieuwerburgh, Directie Onderzoeksaangelegenheden, Afdeling Universiteitsbiblioitheek, Universiteit Gent
  • Nico Verplancke, Vlaams Instituut voor Archivering




Module 1
Module 2
Module 3




Training – KULeuven – Data Science in Practice – 5-6 February 2015

Data Science in Practice


Modern information and communication technology is increasingly capable of collecting and generating large amounts of data that need to be analyzed to become useful or profitable. In fact, these amounts quickly become too large for immediate human understanding, leading to a situation in which “we are drowning in data but starved for knowledge”.

Data science represents an essential technology to transform such data into knowledge. It allows the automated discovery of interesting regularities or anomalies in large databases, thereby surpassing standard statistical summarizing. Typical tasks include the construction of predictive and descriptive models for classification, regression, clustering, associations, and probabilistic inference.

The DTAI research group of the department of Computer Science, KU Leuven, presents a course that provides a gentle introduction to data science for professionals who need to analyze data themselves, interpret results obtained using data science techniques, or give guidance to data analysts. The course introduces the principles, techniques and methodology of data science. It provides the attendants with an overview of the wide variety of data science techniques available, insight in which techniques are useful for what kind of tasks, expertise with practical data science tools, and real-life case studies.

The target audience of this course consists of professionals who experience a need for a better understanding of data science: which tasks can be solved, which techniques can be used, which are their strengths and weaknesses.


Registration deadline: 20 January 2015
Course: 5-6 February 2015

Click here to register.

More info:



European Presentation – Prof R. Zicari from Goethe University of Frankfurth – Bigdata & datadriven society

Roberto Zicari

Profesor Roberto V. Zicari (@odbmsorg), from the Goethe University  in Frankfurt recently gave a talk at Stanford on Big Data:

This is a very original presentation where bigdata is viewed from an European perspective: Big data a Data driven society

It contains information on the projects around  data driven innovation  from the European Commission.

If you are interested, this is the page with a link to download the presentation:

and you can watch the video on Youtube:

Repost – Vincent Granville – 20 short tutorials all data scientists should read (and practice)

The new, completed version of this Data Science Cheat Sheet can be found here.

We are now at 20, up from 17. I hope I find the time to write a one-page survival guide for UNIX, Python and Perl. Here’s one for R. The links to core data science concepts are below – I need to add links to web crawling, attribution modeling and API design. Relevancy engines are discussed in some of the tutorials listed below. And that will complete my 10-page cheat sheet for data science.

Here’s the list:

  1. Tutorial: How to detect spurious correlations, and how to find the …
  2. Practical illustration of Map-Reduce (Hadoop-style), on real data
  3. Jackknife logistic and linear regression for clustering and predict…
  4. From the trenches: 360-degrees data science
  5. A synthetic variance designed for Hadoop and big data
  6. Fast Combinatorial Feature Selection with New Definition of Predict…
  7. A little known component that should be part of most data science a…
  8. 11 Features any database, SQL or NoSQL, should have
  9. Clustering idea for very large datasets
  10. Hidden decision trees revisited
  11. Correlation and R-Squared for Big Data
  12. Marrying computer science, statistics and domain expertize
  13. New pattern to predict stock prices, multiplies return by factor 5
  14. What Map Reduce can’t do
  15. Excel for Big Data
  16. Fast clustering algorithms for massive datasets
  17. Source code for our Big Data keyword correlation API
  18. The curse of big data
  19. How to detect a pattern? Problem and solution
  20. Interesting Data Science Application: Steganography

Other Cheat Sheets

Vincent’s Cheat Sheets for Perl, R, Excel (includes Linest, Vlookup), Linux, cron jobs, gzip, ftp, putty, regular expressions, Cygwin, pipe operators, files management, dashboard design etc. coming soon

Cheat Sheets for Python

Cheat Sheets for R

Cross Reference between R, Python (and Matlab)

Cheat Sheets for SQL


Related linkThe Data Science Toolkit

Other interesting links


This post was written by the team behind DataCamp, the online interactive learning platform for data science.  

After being dubbed “sexiest job of the 21st Century” by Harvard Business Review, data scientists have stirred the interest of the general public. Many people are intrigued by this job, namely because the name has an interesting ring to it. But it is exactly the name that also raises a lot of questions. Because what is a data scientist and what do data scientists do exactly? Many of us who devote their lives to data science have frequently been confronted with questions like these.

The answers to these questions are mostly not as straightforward as you would expect: a short search on Google with the string of words “How to become a data scientist” shows that the concept has different meanings to different people. In addition, many articles indeed suggest various tools, courses and applications for people to become a data scientist, and with good reason: the options are unlimited. But let’s face it, for someone that is not familiar with the field, this advice may sometimes seem like a jungle of information. What’s more, they could work demotivating: the descriptions are sometimes fearfully long and the many details often hit the readers as an overwhelming avalanche.

DataCamp’s Guide to Becoming a Data Scientist

With all this in mind, DataCamp decided to help those who can’t see the forest for the trees: we designed a step-by-step infographic that clearly outlines how you can become a data scientist in 8 easy steps.  This visual guide is meant for everyone that is interested in learning data science or for everyone that has already become a data scientist but wants some additional resources for further perfection.  The infographic is called “Become a data scientist in 8 easy steps”. Have a look at it!

Become a data scientist in 8 easy steps

If you are thinking about becoming a data scientist, do not be taken aback by the eight steps that are presented in the infographic. We would like to emphasize that becoming a data scientist takes time and personal investment, but that the journey is everything but dull! And don’t forget, there are plenty of courses available to set you on the right way.

If you are already a data scientist, drop us a line if you think of other steps that you have undertaken in your professional journey.






Data scientists aren’t domain experts – Prof Stijn Viaene, 2013

Obtaining maximum value from Big Data projects requires multidisciplinary knowledge in:

  • Business to comprehend the business problems and identify business opportunities that can be tackled with Big Data
  • Analytical to skilfully apply the Big Data models that create the most value
  • IT/Database to understand the IT requirements related to Big Data projects
  • Management to view process as a whole with a focus on change management and people

This Executive Master Class provides participants with the necessary skills to successfully manage this multidisciplinary competencies and to create value with Big Data in their organization.

Are you ready to discover the opportunities out there and be part of a 17 day programme in Brussels and San Francisco?


Why this programme?

By participating in this Executive Master Class, you will:

  • Understand how exploiting Big Data can benefit your company
  • Get a thorough understanding of Big Data modeling techniques
  • Share best practices in Big Data inside and outside your sector
  • Develop a Big Data project for/in your organization


For whom?

Because this programme addresses both business and Big Data, it’s beneficial for a number of professionals. However, understanding Big Data models requires at least a basic analytical background.

This programme is ideal for a multidisciplinary team of 2 people from the following backgrounds:

  • Business analysts who want to use Big Data analytics in a business context
  • Professionals with an IT/technology background who want to learn how this technology can be used to create business value
  • Business experts with a strong analytical background who will be involved in Big Data projects


Module 1 – Modeling

(23, 24 & 25 October 2014; Brussels)

This module connects the worlds of data scientist and business expert by:

  • Presenting an overview of typical analytical & Big Data applications that create value in several industries (Finance, Retail, Telecom, Pharmaceuticals, …)
  • Creating awareness of the latest business trends in Big Data
  • Aligning Big Data investment with business benefits


Module 2 & 3 – Discovering

(27, 28 & 29 November; Ghent – 11, 12 & 13 December 2014; Leuven)

This module introduces participants to more advanced Big Data solutions, focusing primarily on creating value from large volumes of data.

  • The IT infrastructure that is needed to apply Big Data analytics
  • The main advantages & disadvantages of multiple Big Data technologies
  • Data modelling and visualization
  • How to get business value from text data, web data, social network, audio and video data: moving towards customer centricity
  • State of the art in Fraud Analytics: improving Fraud detection using social network analysis
  • Hands-on exposure to tools that turn Big Data into value


Module 4 – Operationalising

(19, 20 & 21 February 2015; Brussels)

This module focuses on turning the insights gained from the previous modules into actions by adapting business processes, architecture and BI systems.

  • How to take advantage of high-velocity data
  • The organisational capabilities that are needed to successfully implement Big Data projects
  • How to put Big Data into action using decision-management solutions


Module 5 – Cultivating

(6, 7, 8, 9 & 10 April 2015; Silicon Valley, San Francisco)

This module investigates how management can optimally exploit the newly created Big Data benefits by cultivating data-driven decision-making in their organisation.

  • A deep dive into organisational strategies to cultivate Big Data innovations
  • How to optimally communicate Big Data projects
  • How to deal with ethics and privacy in a big data world




Discover our Faculty:



23-25 October; 27-29 November; 11-13 December 2014; 19-21 February; 6-10 April 2015

17 days

Vlerick Campus Brussels , Vlerick Campus Ghent , Vlerick Campus Leuven ,Silicon Valley, San Francisco


11495 euros (excl. 21% VAT)
+ €850 catering costs & €1450 for module in San Francisco

Financial Benefits


Coursera – Social Media Analysis – Michigan Univerity

University of Michigan

The Social Network Analysis MOOC started this week on Coursera.
The course is given by Lada Adamic, an assiciate professor at MU who took a sabbatical year to go and work at Facebook. A year later she’s back with this inspiring course.
Lada Adamic will introduce you to social network mechanics and concepts. The tool of choice in this case is Gephi, which is a free to use graph/network visualisation tool.
This 8 week course combines video lectures with homework assignments during which you will learn to use Gephi and apply the freshly acquired knowledge on real data sets.
The course offers the possibility to apply for a certificate.

As a personal note from Glenn Vanderlinden:

I already went through the first couple of units and it looks rather interesting. It makes use of Gephi, which is to an extent an alternative to Neo4j. Might be interested for people who attended the last Meetup or who are interested in graph/network analysis. I hope this is useful for the community.


Lada Adamic

Lada Adamic

Coursera – Process Mining -TU Eindhoven – starts Nov 12th



Process Mining: Data science in Action

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

Course at a Glance

4-6 hours of work / week
English subtitles

Nice list of Data Science Bootcamp Programs – Posted by Ikechukwu Okonkwo


Thank  you   for creating the original post.


The Brussels Data Science Community is setting up a 12 week Boot Camp, if you want to participate follow this link.

We want to keep this post alive and up to date,so please send us your updates regularly.

You can use this form to update the list:

Data Science Bootcamp Programs – Full TIme, Part Time and Online

I’ve gotten a lot of inquires on options to move into Data science. This is my attempt to answer that question. If I excluded any programs from this, please feel free to ping me. You’ll see that there are quite a few options and you need to find the best fit based on your profile. This list does not include any university programs.

Everyone seems to reference the quote from Google Economist Hal Varian “Being a statistician is the sexiest job of the 21st century” and the McKinsey report about the shortage in Data Science talent.

Here we go…

Full Time

Zipfian Academy : This is not a 0-60 school. It’s more like 40-80. They are currently about to graduate their second cohort.

  • Notes : Of all the Data Science bootcamps, Zipfian has the most ambitious curriculum. Graduates from the first cohort are currently working in Data Scientist roles across the Bay Area. I’m currently part of the second cohort
  • Location : San Francisco, CA
  • Requirement : Familiar with programming, statistics and math. Quantitative background
  • Duration : 12 weeks
Update : Since the initial post went up a few months ago, Zipfian Academy has added two more programs
Data Engineering 12 – week Immersive : This follows the same format as the Data Science Immersive. The first cohort for this program will start January 2015
  • Notes : This follows the same format as the Data Science Immersive
  • Location : San Francisco, CA
  • Requirement :  Quantitative / Software Engineering background
  • Duration : 12 weeks
Data Fellows 6 – week Fellowship :  The first cohort for the fellows program will start Summer 2014
  • Notes : This program is free for accepted fellows
  • Location : San Francisco, CA
  • Requirement :  Significant Data Science Skills, Quantitative background
  • Duration : 6 weeks
Also see a recent google hangout explaining these new programs :  Zipfian Academy Data Fellows Program  – Information Session 
Insight Data Science : Accepts only PhDs or PostDocs. They have completed 5 cohorts in Palo Alto and are opening up a new class in New York this summer. From their website, it does look like they have almost perfect placement. It is project based self directed learning, so if you need some hand holding or you’re not already very familiar with the material this may not be the program for you
  • Notes : No Fees, pays Stipend
  • Location : Palo Alto, CA / New York, NY
  • Requirement : PhD / PostDoc
  • Duration : 6 weeks
Insight Data Engineering : They’ll enroll the first cohort this summer. Bootcamp will focus on the data engineering track. It is project based self directed learning, so if you need some hand holding or you’re not already very familiar with the material this may not be the program for you
  • Notes : No Fees
  • Location : Palo Alto, CA
  • Requirement : strong background in math, science and software engineering
  • Duration : 6 weeks

The Data Incubator  : Accepts only STEM PhDs or PostDocs. The first class is starting summer 2014.

  • Notes : No Fees
  • Location : New York, NY
  • Requirement : PhD / PostDoc
  • Duration : 6 weeks
Data Science Retreat : Follows the same format as Zipfian but is based in Europe

  • Notes : Curriculum is mostly in R, though they support other languages (python, clojure, julia ). They have tiered pricing for the class, so you can pay for which tier meets your needs
  • Location : Berlin
  • Requirement : Experience with programming, databases, R, Python
  • Duration : 12 weeks
Data Science For Social Good : hosted by the University of Chicago. The students work with non-profits, federal agencies and local governments on projects that have a social impact
  • Notes : they focus on civic projects or projects with social impact
  • Location : Chicago, IL
  • Requirement : It looks like they target academics (undergraduate and graduate students)
  • Duration : 12 weeks
Metis Data Science Bootcamp  : This looks like its modeled after the Zipfian program from a duration / structure / curriculum stand point. It is owned by Kaplan which also recently acquired Dev Bootcamp. Looks like the big .edu players are trying to make a play for the tech bootcamp space

  • Notes : It enrolls the first cohort Fall 2014
  • Location : New York, NY
  • Requirement : Familiarity with Statistics and Programming
  • Duration : 12 weeks
Data Science Europe Bootcamp : This looks like its modeled after the Insight program. Select a small group of very smart people with advanced degrees and help them get ready for Data Science roles in 6 weeks.
  • Notes : It enrolls the first cohort January 2015. Also if you don’t receive an offer for a quantitative job with 6 months of completing the course, you’ll receive a full refund on tuition paid
  • Location : Dublin, Ireland
  • Requirement : Quantitative Degree, Programming knowledge and Statistics background. It looks like they prefer graduate students and Post Docs but are open to applications from undergrads.
  • Duration : 6 weeks
Science to Data Science : They accept only PhDs / Post Docs or those close to completing their PhD studies. We are seeing more bootcamps adopt this model.

  • Notes : It enrolls the first cohort August 2014. There is a small registration fee for the course otherwise the program is free for participants
  • Location : London, UK
  • Requirement : PhD / Post Doc
  • Duration : 5 weeks

NYC Data Science Academy : This looks like its also modeled after the Zipfian 12 week immersive. Another option for non-postdocs on the east coast looking to make the transition to Data Science

  • Notes : It enrolls the first cohort February 2015. Just looking at the curriculum, it appears well thought out and seems to cover a lot of breadth. They focus on R and Python and spend significant amounts of the course time covering both ecosystems.
  • Location : Manhattan, NY
  • Requirement : Looks like they prefer people with STEM advanced degrees or equivalent experience in a Quantitative discipline or programming
  • Duration : 12 weeks

Microsoft Research Data Science Summer School  : targets upper level undergraduate students attending college in the New York area. Program instructors are research scientists from Microsoft Research

  • Notes : Each student receives a stipend and a laptop
  • Location : New York, NY
  • Requirement :  upper level undergraduate students interesting in continuing to graduate school in computer science or related field or breaking into Data Science
  • Duration : 8 weeks
Part Time
  • General Assembly – Data Science : San Francisco / New York. Part time program over 11 weeks (2 evenings a week)
  • Hackbright – Data Science  San Francisco. Full Stack Data Science class over one weekend
  • District Data Labs : Washington DC.  Data workshops and project based courses on weekends
  • Persontyle : London, UK. Offering R based Data Science short classes
  • Data Science Dojo : Silicon Valley, CA /  Seattle, WA / Austin, TX. Offering data science talks, tutorials and hands on workshops and are looking to build a data science community
  • AmpCamp : This is run by UC Berkeley AMPLab. Over two days, attendees learn how to solve big data problems using tools from the Berkeley Data Analytics Stack. The event is also live streamed and archived on YouTube
  • DataInquest : Silicon Valley, CA. They organize hands on tutorials / training on big data technologies. They offer three different courses and cover quite a variety of the latest technologies. Session run on weekends
  • NextML
  • BitBootCamp
These bootcamps are popping up and thriving because there is currently an imbalance between demand and supply of Data Science talent and the acceptance rates at some of full time bootcamps are anywhere from 1 in 20 to 1 in 40

p.s : I need to stress that with any of the programs listed above, you need to do your due diligence and ask the tough questions to find out if it’s a good fit for you. You probably want to be on the look out for programs that are not transparent about their placement.

Update 1 – 05/14  : Added the new Zipfian programs, Persontyle
Update 2 – 07/14 :  Added Metis, Data Science Europe,  Science to Data Science
Update 3 – 08/14 :  Added Data Science Dojo
Update 4 – 10/14 :  Added AMPLab

Update 5 – 11/14 :  Added Coursera/UIUC, Udacity Data Analyst Nanodegree, Thinkful, DataInquest
Update 6 – 12/14 :  Added NYC Data Science Academy
Update 7 – 01/15 :  Added Next.ML, Bitbootcamp, DataQuest

Bootcamp by Metis – 12 weeks in NYC – January 12, 2015 – April 3, 2015

Here bellow you can find an example of a Datascience Boot camp organised by Metis in New York. The objective is Learn Data Science in 12 weeks with 100% in-person instruction with experts from Datascope Analytics.

Application deadline December 8, 2014


This bootcamp runs in-person for 12 weeks, Monday through Friday, from 9 am – 6 pm. It is preceded by online pre-work focused on command line, Python, and installing various packages.


Applicants must have some previous experience programming (writing code) and studying or using statistics.


Upon graduating, you will be comfortable designing, implementing, and communicating the results of a data science project, including knowing the fundamentals of data visualization and having introductory exposure to modern big data tools and architecture such as the Hadoop stack. You should feel confident pursuing a job as an entry-level data scientist or data analyst. Read our syllabus