Why choose the Executive Master in Statistics & Big Data at Dauphine-PSL? Interview with alumni Redha Moulla

Among the Data Science courses, the Executive Master in Statistics & Big Data at Dauphine-PSL stands out for several strong points. Find out why you should choose this course, through the interview of alumni Redha Moulla.

The Data Science is on the riseThere are many training courses available. For aspiring Data Scientists, it can be difficult to choose.

In order to stand out from the crowd, the Paris Dauphine-PSL University has chosen to distinguish itself by an atypical approach. But what do students think? Are they satisfied with the skills they acquired after their training?

To find out, it seemed important to ask alumni directly for their opinion. We spoke to Redha Moulla, alumnus of the Executive Master Statistics & Big Data.

1) Why did you choose the Executive Master Statistics & Big Data (EM SBD) to continue your studies?

The choice of the Executive Master Statistics & Big Data was quite natural for me. First of all, there is the Dauphine-PSL institution, which is a strong place for applied mathematics and decision sciences.

The other key criterion that guided my choice was the pedagogical content of the trainingwhich is very different from other courses, which are often focused on machine learning only.

When I started my training at Dauphine Executive Education, I had already had a few years of experience in machine learningin research as well as in consulting.

The educational proposal of the Executive Master Statistics & Big Data was very different: it Expands the field of data science far beyond machine learning, to include statistical approaches, functional analysis, etc. This is a conception of data science that resonated perfectly with my own.

2) What are the keys that the training brought you? What do you retain from your training?

The first contribution of the training is perhaps to to have helped me to structure my thinkingin a field that is very heterogeneous in terms of the diversity of techniques and practices.

More specifically, the training was the occasion of a turning point – which had already begun before – for deepen my thinking about Bayesian approaches before adopting them. I’m also thinking of content that I probably wouldn’t have had access to otherwise, such as extreme value theory.

3) Since graduation, what have you become?

To tell you the truth, I changed jobs during the course, as a data science manager in a digital transformation consulting firm. Today, I am independent consultant and teacher in data science.

4) What do you think of the Big Data industry? Which sectors are recruiting the most?

It’s a little difficult to get a definitive idea of the Big Data sector in that it is constantly changing. Techniques and practices are changing at a speed that can sometimes seem unsustainable. What is constant and certain, however, is the growing interest in data on the part of companies, and we are probably only at the beginning.

In terms of the skills that are in demand, there is currently a lot of interest in what are called ML Engineersprofiles that combine knowledge of both data science and data engineering, capable of leading a data science project from data processing to production.

5) Any last words for students who want to go into Big Data?

The advice I often give to students and young data scientists, more generally, is to focus on the dataand not to neglect the mathematical aspects.

I am convinced that these are the keys to an efficient and responsible practice of data science. I believe that these are also the structuring values of Dauphine Executive Education’s Executive Master Statistics & Big Data.

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