A new diploma in data science

Last summer, NeuroSchool offered a new post-graduate diploma (DESU) in data science applied to neuroscience. Read morethe content of the diploma and the students' testimonies

A new diploma in data science

Between June and October 2022, NeuroSchool offered a brand new course: a post-graduate diploma (DESU) in data science applied to neuroscience. Twelve 2nd-year master’s or PhD students benefited from the 1st edition of the diploma.

All 12 selected students already had some knowledge of programming and experience in working with large amounts of data. Why did they take this course? They felt they needed more solid mathematics foundations, more practice in database management or in visualization tools, or maybe they were interested in machine learning. They got it all and more!

Throughout this intensive training (120 hours), they learnt about:

  • mathematics, computing, machine learning,
  • big data statistics,
  • database management, analysis and visualization tools,
  • programming techniques,
  • ethics and law in data science.

Tutored projects

What has certainly kept them most busy is the tutored project. They could either work with their own data, for instance from their PhD research project, or use a data set provided by a partner (lab, biotechnology company…). Using a set provided by a partner could be very interesting for students wishing to create links with companies, who could potentially hire them afterwards or collaborate with them if they stay in academia.

Here are some examples of tutored projects:

  • Sarah Bonnet worked on a diagnostic tool that uses radiological images to build prediction models for childhood pneumonia,
  • Raphaëlle Schlienger did a multivariate analysis of functional magnetic resonance imaging (fMRI) data to map spinal proprioceptive circuits,
  • Myriam Azzarelli used deep learning to detect a rat on a maze,
  • Justine Facchini and Jérémy Verneuil explored the Movie Database to evaluate if there is an influence of female representation on the critical success or profitability of a film, and even built a biased recommendation algorithm to give more visibility to female content.


What is your general opinion about this data science training ?

This course was a revelation for me

I registered in order to acquire new skills and better process my future data. In the beginning, data science was for me an obligation to perform better science, but thanks to the teachers, I now understand the logic behind the algorithms, and I have learnt to like data science. In my opinion, complementing neuroscience with data science is the future of science. I discovered a very interesting environment, which perfectly matches my desires and especially my curiosity. 

Which students would you recommend the course to?

  • “Neuroscience students who are already good with Python”
  • “I would strongly advise master’s students to do this training and warn PhD students about the time it takes.”
  • “I recommend this training to motivated students who want to perform “good” science, and understand and build their data. You shouldn’t be afraid of data science, if it’s well explained you can even like it. You just have to be motivated and patient! This training apprehends data science from a neuroscientific point of view and is totally adapted to neuroscience students.

Did the training help you to reach specific objectives and acquire skills?

Thanks to this training, I realized that I was quite capable of developing these skills and it gave me a huge desire to continue and improve further.

I had only coded a little when I registered for this course and I was afraid I would not have a sufficient level. 

  • “I now understand how to apply machine learning, and how to use Python properly!”
  • “I learned to properly prepare my database to optimize analyses, and above all, to understand and apply the different types of supervised and unsupervised algorithms. Some of them are very complex, and it is important to understand the mechanism before applying them and publishing articles. Each algorithm can be optimized.”


In your opinion, how did the training strengthen your professional profile and affect your employability?

  • “This training helped me find a possible reconversion in Data Science. This training alone is probably not enough, but it was a fantastic springboard.”
  • “I didn’t really know this field before I started the training. During my PhD, I had a lot of fun learning and using Python, whether programming small codes to automatically analyze my data, or discovering AI for video analysis. I explored this field much more during this training, and I really liked it! I had a lot of fun in this training, allowing me to define my professional project.”
  • “This training allowed me to get my foot in the door by giving me an overview of the Data Science environment.”
  • The course has strengthened my professional profile. I will not start as a data analyst for a company tomorrow, but it allowed me to acquire a good base to continue learning. Data science skills are desirable for research laboratories (essential in the United States, for example) but also for companies.


What an impressive amount of work! Congratulations to all these students.

We are certain the skills they developed will be a huge asset on their CV. Follow them (for instance on our NeuroCommunity group on LinkedIn), their career will certainly be spectacular!

Let’s also congratulate warmly the team of teachers that set up this new diploma under the supervision of Julie Koenig (AMU, INMED) and Nicolas Catz (AMU, LNC):

  • Annabelle Blangero (Ekimetrics),
  • Bruno Torresani (AMU, IMM),
  • Nicolas Rochet (Ada’Lab),
  • Aitor Gonzalez (AMU, TAGC),
  • Stefania Sarno (AMU, CenTuri / INMED),
  • Olivier Coulon (CNRS, INT),
  • Julien Lefèvre (AMU, INT).

We are also very grateful to the following tutors: 

  • Anna Montagnini (CNRS, INT)
  • Annabelle Blangero (Ekimetrics)
  • Catherine Wacongne (Another brain)
  • Guillaume Etter (McGill university)
  • Julie Koenig Gambini (INMED, AMU)
  • Nicolas Catz (LNC, AMU)
  • Nicolas Rochet (Ada’Lab)
  • Jean-Luc Anton (CERIMED)
  • Julien Sein (CERIMED)
  • Bruno Nazarian (CERIMED)

Many thanks, and let’s meet again soon for the 2nd edition of this DESU in data science! 

Did you enjoy this content ?

Any thoughts ?

Your opinion is important to us

Description de la soumission d'un avis

Your vote :
Your opinion is important to us