Announcing the Launch Event for the Dengue Hackathon

I’m excited to announce the launch event for the diHack’s Dengue Hackathon, at 6 p.m. on Tuesday, October 11th at the European Data Innovation Hub. We’ll present the dengue challenge, give examples of of how data science can help stop the spread of dengue, provide information about coming events, and leave time for networking. You can view the event on meetup here.


There are over 390 million cases of Dengue fever every year, and half of the world is currently at risk of contracting the Dengue virus. We believe that if we get enough data and data scientists together, we can make a difference in stopping the disease’s spread. 


You can check out our website here, and everyone is invited to the launch event. Don’t forget to share your data or ideas and sign up for the hackathon. 

Data Science Boot Camp: Week One

My name’s Alexander Chituc, and I’ll be your foreign correspondent in Brussels, regularly reporting on the diHub and the data science community here in Belgium. I’m an American, I studied philosophy at Yale, and I’m one of the seventeen boot-campers for the di-Academy.

It might be an unconventional way to start a Data Science Bootcamp, but the first week was devoted to working on our communication skills with Martine George, PhD, professor of Management Practice at the Solvay Brussels school of Economics and Management. The Director and Head of Marketing Analytics and Research at BNP Paribas Fortis for nearly four years, a database analysis manager for three years, a lecturer on Business Analytics for five, now Martine was teaching us about our personality types and how to effectively communicate with each other, upper management, and potentially, coworkers with drastically different styles of communication.


The main objectives of our training were to make us aware of our own communication style, to learn to adjust the presentations on the results of analytics to different audiences, and how we could convince clients of the importance of our results.

We learned our communication styles through using the Process Communication Model, a tool that “enables you to understand, motivate, and communicate effectively with others.” On the first day, we received our profiles determined by the results of a questionnaire we had taken the week before.

personality-resultsan example of a personality profile

The model divides people into six “base” personalities, with one “phase.” My own “Structure of Personality” had a base of Thinker (organized, responsible, logical), followed by Persister (dedicated, observant, conscientious), Rebel (spontaneous, creative, playful), Imaginer (calm, imaginative, reflective), Promoter (adaptable, persuasive, and charming), and Harmonizer (compassionate, sensitive, warm), in that order (I wont get into too much detail about the different types, except to share the fun fact that in earlier versions of the model, my base personality type, Thinker, was named “Workaholic,” but if you’re interested in learning more, you can visit the website).

six-personality-typesThe second day we focused on communication with managers and giving presentations taken into account what we had learned the first day.

One important aspect of this was writing good one-pager, something a busy executive can quickly read to understand what exactly you’ve learned in your analysis, how you did it, and what to do now. We went over some example one-pagers and explained where they went wrong and how we could improve them: making sure the business question is clear, making the conclusion explicit with an actionable next step, and removing any unnecessary information when explaining the method. No matter how exciting or interesting you might find the methodology of your report, executives and upper management typically don’t.

We also spent a good portion of the second day learning about giving presentations, and how to alter your presentation given a potential change in time. With focusing on governing thoughts, story boarding, and logically organizing our ideas, you can turn a thirty minute presentation into a five minute presentation if the need arises, and vice versa. After some work with Martine, structuring the major key ideas she wanted to express, Annelies gave a great five minute pitch for an app she wanted to build using using data science, and she could just as easily turn it into a thirty minute presentation.

The biggest take away from our training was to target the right group with the right message, and to cater your message not to your own communication style, but the communication style of your audience.

It was an unusual way to start a bootcamp, but communication is an often neglected skill for a data scientists, and beginning this way really put an emphasis on its importance. Next week we would be moving on to Predictive Modeling in R and Data Visualization and Story Telling.

Summer Camps and Leader Boards

My name’s Alexander Chituc, and I’ll be your foreign correspondent in Brussels, regularly reporting on the diHub and the data science community here in Belgium. I’m an American, I studied philosophy at Yale, and I’m one of the seventeen boot-campers for the di-Academy.


Of the hundred or so applicants who applied for the Di Hub’s Data Science Boot Camp, only 40 were selected for the five-week Summer Coding Camp. Most of us had little to no experience coding in Python and R – or in my case, coding – and the Summer Coding Camp was to serve two purposes: first, to narrow down this pool of applicants to the twelve who would eventually be selected for the boot camp, and second, to catch us up as quickly as possible with the coding skills we would need for our training to become data scientists.

I had already expected that there would be a lot to catch up on. I have a bachelor’s degree in philosophy, and my elective coursework was in psychology and writing. My coding experience consisted of one semester in college where I took a class in Object Oriented Programming in Java, seven years ago. Suddenly, I found myself in a room with a couple of Master’s in Statistics, several Master’s in Business Engineering, a few digital marketers, and a lot of data enthusiasts with backgrounds in computer science, all competing for twelve spots.

The Di Hub was open to all of us as a place to study during the camp, providing coaches to answer any questions we might have. Each week of the camp covered a different topic. The first week covered SAS, the second Python, the third R, the fourth statistics, and the fifth SQL. When we began the first week, I was relieved to see just about everybody struggle as much as I did. This didn’t surprise me: all training in SAS comes directly from the company, so regardless of your background, it was no natural that none of us knew how to code in it. It was by far the most intensive week of summer camp, and in the following weeks, many of us were still working on it it, preparing for the certification exam on September 16th, which only half of us passed (I’ll leave it up to you to guess whether I was one of them).

The second, third, and forth weeks we learned using Data Camp’s platform. We were assigned 17 courses to complete on their website: three in Python (at the time, their Python content was admittedly lacking, but they’ve recently added several more Python courses to their website), seven in R, and seven in statistics. During the fourth week, we were given the option of following the courses in R on Data Camp, or to do instead a separate module in SAS. As far as I know, everyone chose to do statistics in R. Doing work in SAS, after all, didn’t count for the leader board.

I should explain the leader board. It began as a joke, Nele announced it that Friday afternoon on Slack. After finishing the day’s coding, we were going to celebrate the completion of our second week, and before this celebration, Nele would be announcing our leader board. Suddenly, all of us became aware of the feature available to groups on Data Camp: a leader board that ranks all of the members of the group by experience earned completing exercises in their courses.

I noticed that I was, at the time, ranked at number twelve, and I was determined to make it into the top ten by the time the day was over. Between exercises, I compulsively checked the status of the leader board, figuring out just how many exercises I had to complete before I would pass number eleven, which I did, and then to pass number 10, which I did. That afternoon, Nele announced the leader board, and on the board were written only six scores. The top six, in descending order were Liza, Goran, Olivier, Agustina, Ruben, Victor, and you can imagine my disappointment.


The leader board was a source of healthy competition, granting bragging rights and a way to measure ourselves against each other and judge our prospects for being selected. It became a little more serious, however, when it was announced that there would be a job fair on September 9th, where we would all present ourselves to companies looking to hire data scientists, and finding a company to sponsor you would guarantee your seat in the boot camp. The order in which we were presenting was determined by your leaderboard ranking.

It was an intense five weeks, and we all learned much more than we could have on our own. I’ll have to devote an entire post to the job fair later on, but I’ll leave off this one by thanking all of the great coaches we had during the summer camp: coaching Python, Elie Jesuran from Keyrus, coaching R, Dominique De Beul, Eric Lecoutre and Pieterjan Geens from Business&Decision, and coaching SQL, Erwin Gurickx from Teradata.

Job @ Medialaan: Data Quality expert

Are you passionate about Data Crunching and keen on having business impact?

We are looking for a talented Data Quality Expert!

You will be part of our dynamic Research & Marketing team and report to the CRM manager of our central unit.

The Central CRM unit is a department in the organization that gives strategic and operational advice in how to build a trustworthy 2-way relationship with our customers. Our unit is charged with supporting a wide range of internal users and we help them to take important business decisions and to steer the strategy of the company.

Your Challenge:

Set up a Data Governance program together with all stakeholders (IT, Marketing, Sales…) that describes the establishment and deployment of roles, responsibilities, policies, and procedures concerning the acquisition, maintenance, dissemination and disposition of our data.

You main tasks are:

  • Identify, assess, fix, document, and communicate potential quality issues in the way data are collected, stored, processed, or used.
  • Understanding the data, looking for discrepancies, inconsistencies, data redundancy and taking steps to solve data deficiencies.
  • Be responsible for the enrichment of our data to create added value.
  • Continuous Monitoring output data quality through KPI dashboards and reports and make recommendations based on the outcome.
  • You are the person that leads all data quality related projects, which includes writing business requirements, managing the scope, budget and risks etc.
  • You have a crucial role in helping the organization to understand the value and impact of good data quality.


Additionally you will:

  • Setup business and validation rules together with stakeholders that govern our data.
  • Be responsible for business processes that create or change data.
  • Automation of solutions for data quality issues associated with all sorts of data (customer data, mobile data, online data, financial data etc.).
  • Design, implementation and testing of (automated) DQ processes; for example data cleansing operations or deduplication.
  • You contribute to the continuous improvement of our Data Governance methodology to keep it tuned with technology evolution (unstructured data, cloud, data privacy, …)
  • You act in a cross-functional team and work closely with our IT department.


Your profile: 

  • You have a passion for information management and you are challenged by the data struggle many companies are facing at the moment. You have good analytical skills and you are able to communicate both in business as well as in technical language.
  • You have at least 3 to 5 years’ experience as an analyst in a Data Quality and/or Business Intelligence environment.
  • You possess a basic understanding of data models and architecture, data governance/data management concepts, approaches, methodologies and tools.
  • You are good in translating analytical results into business concepts.
  • You have strong programming skills, preferably in SQL.
  • Knowledge of Tableau is also a plus.
  • You have strong communication skills and like to take initiative.
  • You like to work independently, to take ownership and you are accountable.
  • You have a solid understanding of the business and the importance of Data Quality.
  • You have excellent communication skills.
  • You are an objective and diplomatic person that likes to work in a team.
  • You are solution-minded.


Intrigued? Please send your motivation letter and resume to Maybe you can join our new CRM unit and build a great data story at MEDIALAAN.

Big Data and Ethics Meetup – Call for ideas/speakers.

ethicsPierre-Nicolas Schwab, the Big Data / CRM manager at RTBF (French-speaking public television) will organize in December a conference on ethical aspects of Big Data in the broadcasting industry. Although this conference will be reserved for members of the European Broadcasting Union (, Pierre-Nicolas would be interested to share views with Hub’s members on the topic of Big Data and Ethics.

I think Big Data and Ethics is a topic of importance that we’ve insufficiently covered until now during the diSummit nor our Meetups. I’d like our community  to contribute to this topic through the organization of a special meetup on November 17th. I am asking interested members to contact me if they want to contribute to this event in the form of presentation.

Specific topics could be:

  • Good and bad practices as far as data usage is concerned
  • Examples of Big Data cases that triggered negative reactions
  • The consumer’s perspective: does sharing data with third parties add value for the customer or for the firm?
  • Implicit limits existing in your industry as far as the use of customers’ data is concerned
  • Paradigm shifts and possible unethical changes caused by Big Data modelling in your industry
  • Changes brought by IoT and possible threats to privacy and ethics
  • Measurement of intrusiveness / perception of intrusiveness by users
  • Forward-looking cross-industry trends that may have a negative impact on customers’ perceptions of Big Data

I welcome expressions of interest for this topic and would be glad to organize a preparatory meeting with interested speakers. Pierre-Nicolas has already proposed to host this meeting at RTBF to have fruitful discussions.

Thank you in advance for your answers.


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