DATA Minor

PSL University is developing a range of minors focused on highly attractive themes, closely aligned with contemporary scientific and socio-economic challenges. Whether as an opportunity for broadening one’s horizons or for professional development, as part of a specialised academic path, or as a way to enrich research by exploring a new theme or applying new tools, PSL’s minors offer unique opportunities for Master’s and PhD students across all our institutions.

> A certification in AI and data science tailored to your discipline

The DATA minor at PSL University is undertaken alongside a main programme (Master’s and/or PhD). It may be spread over several years, subject to enrolment at PSL University. This certificate enhances students’ academic pathways. To validate the minor, a student should acquire 30 credits. This is equivalent to one semester of a Master’s degree. An admission committee will review the application at the end of the curriculum to deliver the certificate. This certificate can then be issued as a complement to the main PSL degree, once the latter has been validated.

>Specific Features and Structure of the DATA Minor

Each minor at PSL University follows its own specific validation requirements, which may include a selection of accredited courses, PSL Weeks, summer schools, or internships.

The DATA minor is designed to complement a major by providing skills in data science and artificial intelligence. Assessment is based on a combination of courses and validated experiences within the cross-disciplinary DATA programme, according to the criteria detailed below.

> Assessment and Validation Criteria for the DATA Minor

To obtain the DATA minor, students must acquire a total of 30 credits during their Master’s and/or PhD studies. The application for the certificate can only be submitted after the completion of the main degree (at the end of the second year of the Master’s or after the PhD defence). The 30 credits may be validated through three possible pathways, detailed below.

The first is by attending courses within the DATA programme. In particular, the pre-term weeks provide the necessary foundational knowledge and are therefore essential for progressing to more advanced courses, such as the DATA programme’s PSL Weeks. Secondly, depending on your main academic pathway, accredited courses within your Master’s degree may contribute to the required credits count.

Finally, relevant courses from prior education may also be taken into account. These latter two pathways may each contribute up to a maximum of 6 credits.

1. PARTICIPATION IN DATA PROGRAMME TRAINING COURSES INCLUDED IN THE MINOR

a) Pre-term Weeks (3 credits per week) :

- Week 1 – Mathematical and Computer Science Fundamentals (3 credits) – Online on Moodle Platform (Mid-August 2026)

Registration Form 1st week ( deadline 26th of July 2026) :DATA SCIENCE PROGRAM "Machine Learning and bigdata", PSL 2nd data preparatory week, 2026 – Remplir le formulaire

Provisionnal 1st week program:

Day 1: Functions and sequences

Day 2: Basic linear Algebra

Day 3 Differential Calculus & PCA

Day 4: Introduction to statistic & probability

Day 5: Databases

Online 1st Data preparatory week assessment:to be announced

- Week 2 – Machine Learning and Databases (3 credits) – In person from 26th of August to 1st of september 2026 at PariSanté Campus

Provisionnal 2nd week program:

Day 1

(course) Machine learning: recent successes.

(course) Introduction to machine learning.

(lab session) Introduction to Python and Numpy for data sciences.

Day 2

(course) Machine learning models (linear, trees, neural networks).

(course) Scikit-learn: estimation/prediction/transformation.

(lab session) Practice of Scikit-learn.

Day 3

(course) The linear model, optimization

(lab session) Logistic regression with gradient descent.

Day 4

(course) Introduction to Deep-Learning

(course) Introduction to unsupervised learning

(lab session) Practical session

Day 5

(course/lab session) Spark for ML, part 1 and 2

Online 2nd Data preparatory assessment: To be announced

These two pre-term weeks are considered prerequisites for the certificate, unless the student can demonstrate equivalent prior learning as part of their previous academic training.

b) PSL Weeks (3 crédits per week) :

The DATA programme offers intensive DATA Weeks covering a variety of data science themes and applications, such as AI and Ethics, Natural Language Processing, Statistical Physics and Machine Learning, among others.

Most of these courses are scheduled in line with the PSL Weeks calendar, with two weeks per academic year set aside for this purpose—one in November and one in March.

In the PSL Weeks catalogue, these courses can be identified by the “DATA” label.

1st semester : Week of 23 to 27 November 2026

> to be announced

Registration period for the first semester: 2to be announced

2nd semester: Week of 1st to 6th of March 2027

>to be announced

c) Hackathons (typically between 3 and 6 crédits) :

These involve an interdisciplinary project focused on a specific theme, often linked to your main academic programme.

2.COMPLETION OF “DATA”-ACCREDITED COURSES

Students can earn credits by completing DATA-labelled courses within their graduate programme ( Master / PhD)

The number of credits for the minor corresponds to the ECTS credits associated with the number of hours specified in the Academic program for each masters.

> Currently being updated

3. RECOGNITION OF PRIOR LEARNING AND PROFESSIONAL EXPERIENCES(maximum 6 credits)

Students who have already acquired skills in DATA may submit a portfolio including:

The syllabus of the relevant course
Proof of completion (transcript, number of ECTS credits and associated hours)
Supporting documents for practical experience (internship, project, etc.)

The number of ECTS credits may be counted differently within the framework of the minor. For example, a Python course previously awarded 5 ECTS may only be validated as 2 ECTS for the minor. It is possible to discuss this arrangement prior to submitting the portfolio.

Preparation of the recognition portfolio

The recognition portfolio for the DATA Minor must include the following documents:

Curriculum Vitae: A detailed overview of the academic and professional background, including relevant experience in data science and artificial intelligence.
PSL Enrolment Certificate 2025–2026: Proof of enrolment for the current academic year.
Transcript of Results for the Transverse DATA Programme: An official document confirming participation in, and results obtained from, the Transverse DATA training sessions.
DATA-labelled Courses within PSL Graduate Programmes: A list of courses taken as part of the minor, specifying the degree title, academic year, and corresponding transcripts. For internships with a strong AI component, the syllabus must be included.
Recognition of Prior Learning: For students applying for prior learning recognition, the portfolio must include the syllabi of the relevant courses, transcripts, total number of hours, awarded ECTS credits, and the name of the responsible instructor.

Students must submit their portfolio to mineuredata@psl.eu at the latest 1st december (11:59 p.m., Paris time).

Important information: if you have studied abroad in a non-French-speaking programme, please send your transcripts, diplomas, course syllabuses and any other documents, translated into English