“A top choice for me”: Matthew Whall, alumnus of the Master for Smart Data Science shares his experience

After earning a bachelor’s degree in statistics, economics, and French at the University of Cape Town, and working for four years as a Data Scientist, Matthew Whall joined ENSAI’s master’s program in Smart Data Science (class of 2025). This intensive program allowed him to deepen his expertise in data science. Now a machine learning engineer at a South African insurance consulting firm, Matthew shares his experience and insights with ENSAI.

The Master for Smart Data Science is a one-year program taught entirely in English. Lectures in Statistics, Applied Mathematics and Computer Science are given by academics and professionals in Data Science.

ENSAI : Matthew, could you tell us about your background? Where are you from, and what were you doing before joining ENSAI’s Master for Smart Data Science?

Matthew Whall : I am from Cape Town, South Africa. Before coming to ENSAI, I completed a four-year bachelor’s degree majoring in statistics, economics and French at the University of Cape Town (UCT). After graduating from UCT, I worked for 4 years in Cape Town as a Data Scientist before deciding to further my studies at ENSAI.

Data Science programs are available worldwide. What led you to choose ENSAI’s master’s specifically? Did France play a role in your final decision?

I have always had an interest in France and the culture – it’s part of the reason I studied French during my undergraduate and in high-school. The opportunity was made possible through the France-South Africa Scholarship who generously helped fund me to complete my Master’s in France.

It was overwhelming at first when presented with the different options for studying Data Science in France. However, there were many reasons why I chose the Master for Smart Data at ENSAI in the end. This programme stood out with its high Eduniversal ranking and Bienvenue en France label illustrating its prestige and welcoming nature for foreign students.  Moreover, I had been looking for a programme that I could do in one year to limit time off work and ENSAI was a top choice due to it being taught in English and only being one year long.

Its course content is the perfect blend between theoretical learning and practical application, covering topics from machine learning to time series while also teaching how to work with big data and even extending to a professional context through the Smart Data Project and Internship.

All together, its prestige, practicality and range of skills covered in the course made it the top choice for me.

Now that you’ve graduated and are working in data science, how do you look back on the Master’s program? What would you say are its main strengths?

I was very impressed by the high calibre of the program. From the organisation and clear communication from Cécile Terrien and François Portier to the skill and care from the professors. I enjoyed getting to know the small class of only 11 students from all over the world.

It is a challenging programme because of the amount of work that needs to be done in the short period of time, but there was constant support from ENSAI, ensuring that the class was managing the workload and able to achieve their best.

The programme covers a wide range of topics in data science from theoretical concepts to more practical skills. This combination of theoretical and practical ensured a good base for the professional world. Some of the skills I appreciated the most were the topics in high performance computing as they were useful during my internship and my current job now as a machine learning engineer. The school promotes a sense of group success, which allowed our class to work together instead of against each other.

On the more practical side, for the Smart Data Project we were paired with an external company to complete a project with them. I did this with ATEME, working on deep learning video compression techniques.

Similarly, the six-month internship allowed us to gain further experience working as a data scientist in the professional world. I completed mine at the Datalab’, SNCF Voyageurs.

To me, these professional opportunities were indispensable and were a key part of the programme.

During the six-month coursework at ENSAI, which classes did you find the most valuable or enjoyable?

I enjoyed Machine Learning for Data Science and Natural Language Processing (NLP). Machine Learning because I felt it was taught very well despite its challenging content which provided a base for everything else that was covered in the programme.

NLP because it covered topics that are so relevant in the current AI explosion and gave me a glimpse into how these AI tools we use almost everyday work.

In your current job, how often do you use the skills you gained at ENSAI? How would you say they contribute to your day-to-day work?

I am currently employed at Credeq.AI which is an insurance consulting company in Cape Town, South Africa. The skills I learnt during the master’s were exactly what this employer was looking for during my hiring process and they definitely helped me land the job and pass the technical interview. The high-performance computing and big data modules that I learnt at ENSAI have been particularly useful for figuring out how to run efficient code on big datasets almost daily.

What advice would you give to someone considering joining this program?

If you have the opportunity to attend this programme I would say go for it! Not only is it a highly regarded programme for advancing your career as a data scientist but it also offers the opportunity to engage students from around the world with teachers who are leading experts in their fields. The school is situated on the peaceful Ker Lann Campus which is a short distance outside of Rennes – a city that has an amazing student life. Although it is a challenging programme it is worth it and will always remain a very memorable experience for me.

Thank you, Matthew!

Do you want to become a data scientist? Learn more about ENSAI’s Smart Data Science Master’s program.