Overview of the Academic Data Science programs in Belgium by Bart Hamers

Bart Hamers

In this article, I try to make an overview of the academic programs on data science currently given in Belgium.

I wasn’t very selective in my choice as data science is a very broad domain. All educational tracks will have components of statistics, machine learning, artificial intelligence or other related domains. Upon you to decide which program best fits your needs and interests.

In this overview my main focus was on the master-after-master programs. But some normal master programs were also added to the list, as they specialize in data science related field.

These programs typically take place over one or two academic year and are open to anybody with the necessary prerequisites.

I tried to be exhaustive, but might have missed some. Please use the comment box at the end of the list to communicate updates.



University of Brussels

Master of Science in de ingenieurswetenschappen: computerwetenschappen

More info can be found here.


University of Antwerp

Master in computer science with a specialization in  data science: profile Artificiële Intelligentie

More info can be found here.

University of Gent

Master of Science in Marketing Analysis

This 9-month full-time program in predictive analytics (from October to July) is taught 100 % in English. Students will receive classes by world-renowned experts in their field. Data-mining techniques are introduced in the application domain of analytical Customer Relationship Management (CRM)/Marketing. The main emphasis is on classification techniques (binary as well as multi-class): starting with the classical statistical techniques (e.g. logistic regression) over decision trees (including random forests) to artificial neural networks. Sufficient time is also devoted to the modeling process as such (Knowledge Discovery in Databases including the data pre-processing step) as well as checking (predictive) model quality, e.g. AUC on a test sample.

More info can be found here.

Marketing Engineering

Marketing Engineering is an elective part of the two-year master program of Business Engineering at Ghent University. It offers five in-depth courses about analytical Customer Relationship Management (CRM)/Business Intelligence topics in English covering data-mining techniques.

More info can be found here.

Master of Science in Statistical Data Analysis

The Master of Statistical Data Analysis is a one-year advanced master program, but it can be competed in 2 to 4 years as a part student. The program offers training in modern statistical methodology and data analysis from a wide variety of fields, including biology, bio-informatics, economy and marketing, environmental and life sciences, engineering, mathematics and physics, psychology and social sciences.

More info can be found here.

University of Leuven (KUL)

Master of Artificial Intelligence

The understanding of the principles of intelligence, the development of artificial intelligence and its many applications in different areas requires a multi-disciplinary approach. The Master of Artificial Intelligence programme is internationally and interdisciplinary oriented. It aims at educating students from a wide variety of different backgrounds. A basic introductory part of the programme consists of fundamentals of artificial

intelligence and a broadening course that the student selects from either cognitive science, philosophy of mind and artificial intelligence or privacy and big data.

Within the programme students have the choice between three options:

More info can be found here.

Master of Financial and Actuarial Engineering

The Master of Financial and Actuarial Engineering programme provides students with up-to-date, sound and advanced financial, actuarial and statistical skills. It is an intensive, advanced, one-year programme for university graduates interested in a career in risk management, financial and/or actuarial engineering within financial institutions. The discipline focuses on the application of quantitative methods to problems involving risk or uncertainty. You will gain an understanding of current and future problems and solutions in insurance and banking and be equipped with fundamental and conceptual knowledge of the mathematical and economic aspects of financial theory and insurance techniques.

More info can be found here.

Master of Statistics

MSc in Statistics, an interdisciplinary programme whose teaching is grounded in internationally-recognised research. Choose from

among a number of approaches: biometrics, social behavioural and educational statistics, business statistics, industrial statistics, general statistical methodology, or an all-round statistics profile.

More info can be found here.

Master of Bioinformatics

This interdisciplinary two-year programme focuses on acquiring

  • basic background knowledge in diverse disciplines belonging   to the field of bioinformatics, including statistics, molecular biology and computer science
  • expert knowledge in the field of bioinformatics
  • programming skills
  • engineering skills

More info can be found here.


University of Leuven (UCL Louvain-La-Neuve)

Master in Statistics

The Master in Statistics will enable you to become acquainted with the state-of-the art of statistical science and methodology. The skills and statistical competences you will acquire are highly appreciated in professional environments. The structure of the master program into orientations, finalités and options fits well to the wide variety of domains in which statistics play a vital role, ranging from medical sciences (pharmacy, public health, etc.), over human sciences (economics, psychology, etc.), to natural sciences (chemistry, biology, physics, etc.). In order to ensure tight connections with professional life in all these domains, our team of lecturers is completed with a number of statistical consultants.

More info can be found here.


University of Liege

Master in Statistics

The common courses provide the essential notions that underlie all statisticians’ activities : linear models, multivariate analysis, discrete date, non-parametric statistics, the Bayesian approach, data mining and statistics software.

The master’s professional focus offers an introduction to a variety of applied disciplines in the field of statistics (biostatistics, genetic statistics, experimental plans, databases…).

More info can be found here.


Solvay Business School

Master in Quantitative Finance

The Advanced Master in Quantitative Finance offers prospective students a rich curriculum combining finance, statistics, econometrics, programming and mathematics. This Master guarantees a full coverage of financial disciplines, such as asset and derivative pricing, numerical methods and programming skills.

More info can be found here.


Vlerick Business School

Executive master class creating business value with big data.

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.

More info can be found here.


Transnational University Limburg – Hasselt – Maastricht

Artificial Intelligence

The programme is organised under the Department of Knowledge Engineering of Maastricht University’s Faculty of Humanities and Sciences. Researchers in the department are developing intelligent systems for a wide range of applications including patient diagnosis, image recognition, traditional games like chess, interactive computer games, information retrieval and data mining.

Artificial Intelligence is a two-year master’s programme taught entirely in English.

More info can be found here.

Master of Statistics

Hasselt University’s Master of statistics with specializations: Biostatistics, Bioinformatics and Epidemiology & Public Health Methodology, keeps abreast of such evolutions. The master programme combines a solid study of principles of applied and biostatistics with up-to-date information on topics such as clinical trials, public health, longitudinal data, survival analysis, genetics, survey methodology… The specialization in Bioinformatics makes it possible to keep an even closer pace with the more specific professional needs and skills required due to the development of novel experimental techniques in molecular biology and genetics.

Hasselt University’s Master of statistics acquired accreditation from the prestigious Royal Statistical Society.

More info can be found here.


Other courses interesting for a data scientist.

  • Entrepreneurship

More info can be found here.


Short Training Sessions (Academically)


The Flames network organizes trainings for researcher. Check the very extensive training program.

More info can be found here.


Centre for permanent education (University of Gent, faculty of sciences)

Permanente vorming “Multilevel Analysis for Grouped and Longitudinal Data”

More info can be found here.

  • Special Program: Big Data  (Already started in 2015.)
    • More info can be found here.

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

How to Become a Data Scientist by Kunal Jain

full article from Kunal Jain: How Do I Become a Data Scientist?

Last week, Kunal Jain shared a framework to help you answer the question, “Should I become a data scientist (or business analyst)?“. For the people, who clear the cut-offs, the next obvious question is “How do I become a data scientist?”

Having said that, if I was starting his career today, would he choose the same path? The answer is NO.

Step 1: Graduate from a top tier university in a quantitative discipline

Step 2: Take up a lot of MOOCs on the subject – but do them one at a time

  • Python:
    • Introduction to Computer Science and Programming using Python – eDX.org
    • Intro to Data Science – Udacity
    • Workshop videos from Pycon and SciPy – some of them are mentioned here
    • Selectively pick from the vast tutorials available on the net in form of iPython notebooks
  • R:
    • The Analytics Edge – eDX.org
    • Pick out a few courses from Data Science specialization to complement Analytics Edge
  • Other courses (applicable for both the stacks):
    • Machine Learning from Andrew Ng – Coursera
    • Statistics course on Udacity
    • Introduction to Hadoop and MapReduce on Udacity

Step 3: Take a couple of internships / freelancing jobs

Step 4: Participate in data science competitions

Step 5: Take up the right job which provides awesome experience

The Hottest Jobs In IT: Training Tomorrow’s Data Scientists By Bob Violino


If you thought good plumbers and electricians were hard to find, try getting hold of a data scientist. The rapid growth of big data and analytics for use within businesses has created a huge demand for people capable of extracting knowledge from data.

The McKinsey Global Institute, the business and economics research arm of McKinsey & Co., has predicted that by 2018 the United States could face a shortage of between 140,000 to 190,000 people with deep analytical skills, as well as a shortage of 1.5 million managers and analysts who know how to use the analysis of big data to make effective decisions.

“With the rise of big data projects, more people are needed to gather and interpret data,” says John Reed, senior executive director of Robert Half Technology, a provider of technology staffing services.

(photo source: iStock)

Some of the top positions in demand include business intelligence analysts, data architects, data warehouse analysts and data scientists, Reed says. “We believe the demand for data expertise will continue to grow as more companies look for ways to capitalize on this information,” he says.

A survey by Robert Half Technology last year suggested most companies aren’t maximizing their data collection and don’t have the people in place to do so, Reed says. Of the 1,400 U.S.-based CIOs the firm surveyed, 76 percent said their companies weren’t gathering customer data such as demographics or buying habits. Among those that were gathering such data, more than half said they lacked sufficient staff to access customer data and generate reports and other business insights from it.

New data degree programs

Perhaps the best indicator of the need for these skills is the number of data science programs springing up around the country. This year new programs have launched or will soon begin at institutions including Indiana University, University of California, Berkeley, and University of Rochester.

“We have seen tremendous growth in the number of students taking courses in computer science, both computer science majors and majors in other disciplines,” says Henry Kautz,  chairman of the Department of Computer Science at the University of Rochester. “Much of this growth can be attributed to the rising interest in data science and big data.”

The university offered an undergraduate data-science major as an independent studies concentration in 2013-2014 and will offer it as full Bachelor of Arts and Bachelor of Science programs starting in 2014-2015. “Our goal is to grow to 50 students in each graduating class within four years,” Kautz says. Core courses in the Rochester program provide a strong background in statistics, programming, algorithms and machine learning.

“Beyond courses, students in the program are encouraged to participate in undergraduate research projects with faculty,” Kautz says. “Both faculty and the university’s Career Center help students find paid summer internships in the industry. Based on our experience with our computer science program, we expect the majority of data science majors to participate in both research projects and summer internships before graduating.”

Graduates who understand fundamental methods of machine learning — or data mining and predictive analytics — are in high demand across all industries, Kautz says.

Some data science programs are being offered completely online. In January 2014, Indiana University’s School of Informatics and Computing launched an Online Certificate in Data Science program with a broad online curriculum that can be tailored to students’ interests.

“We see data science as one of the most important new areas to develop in this field in the last few years, and we have thus developed an ambitious new program in this area,” says Robert Schnabel, dean of the School of Informatics and Computing at Indiana University.

The school had 36 students sign up for the program within two months of announcing it. “Based on current applications and Web traffic, we expect this number to more than double for the fall 2014 semester and to eventually grow to 200 students,” Schnabel says. The school plans to offer a master’s degree in data science beginning in 2015.

“We see the data science job market requiring two types of professionals: those with deep technical skills, and managers and analysts with the knowledge to use the analysis of big data to make effective decisions,” Schnabel says. The program “has been developed to provide strong learning opportunities for students in both of these areas.”

These programs are needed to help narrow the skills gap “and enable people to tackle complex quantitative problems and drive innovation in the years to come,” says David Dietrich, head of the Data Science Curriculum at EMC Corp.’s EMC Education Services.

“Many of these schools felt the need to respond quickly and created certifications and degree programs based on existing courses across several disciplines,” Dietrich says. “Although this was an important step, schools will need to continue to develop new content to teach people about emerging big-data technologies and advanced algorithms that can be executed with these tools at scale as the industry continues to evolve in the coming years.”

Academic programs are useful in helping to meet the growing demand for these professionals, Robert Half’s Reed adds. “But much of the demand is immediate, so companies are looking at how they can either recruit these workers or help their current teams acquire the necessary skills through training and professional development,” he says.

Good data scientists often have training in computer science and applications, modeling, statistics analytics and math, Reed says. “In addition, they are savvy business people who know how to communicate with a firm’s leadership, and influence how an organization approaches a business challenge. Good data scientists don’t just address business problems, but also pick the right problems to address — those with solutions that will most benefit the organization.”

Bob Violino is an award-winning freelance writer and editor who covers information technology, consumer electronics and business topics. He has held senior editorial positions at publications including InformationWeek and InternetWeek.