2023-2024 / DATS0001-1

Foundations of data science


30h Th, 60h Proj.

Number of credits

 Master of Science (MSc) in Data Science5 crédits 
 Master of Science (MSc) in Data Science and Engineering5 crédits 


Gilles Louppe

Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

Data science is rooted in a rigorous and systematic methodology for understanding and interpreting data. This course seeks to instil the foundational principles of data science, with a particular emphasis on the scientific method and the iterative process of Bayesian modelling. Our perspective is that models are built iteratively: We build a model, use it to analyze data, assess how it succeeds and fails, revise it based on insights, and repeat. 

The lectures will closely follow each step of this loop (tentative and subject to change):

- Lecture 1: Build, compute, critique, repeat
- Lecture 2: Data 
- Lecture 3: Visualization
- Lecture 4: Latent variable models
- Lecture 5: Expectation-maximization
- Lecture 6: Variational inference
- Lecture 7: MCMC
- Lecture 8: Model criticism
- Lecture 9: Wrap-up case study

Learning outcomes of the learning unit

At the end of the course, the student will have gained the necessary experience, both theoretical and hands-on, for solving data-analysis problems. He/she will have acquired and practised the scientific method at the core of data science, including the representation, manipulation and visualization of the data, the design and use of Bayesian probabilistic models, their iterative criticism and improvement, as well as their applications for answering questions, claiming discoveries or making decisions. 

Prerequisite knowledge and skills

Programming experience. Probability and statistics. Elements of artificial intelligence.

Planned learning activities and teaching methods

- Lectures, with live coding sessions
- Reading assignments
- Homeworks

Mode of delivery (face to face, distance learning, hybrid learning)

Face-to-face course

Additional information:

Lectures are taught face-to-face. Homeworks are carried out remotely.

Recommended or required readings

Materials will be made publicly available on GitHub during the semester.

Exam(s) in session

Any session

- In-person

oral exam

Written work / report

Additional information:

The evaluation is divided into the following units:

- Homeworks
- Exam (data science study)

Work placement(s)

Organisational remarks and main changes to the course

The course hub is available at https://github.com/glouppe/dats0001-foundations-of-data-science


Gilles Louppe (g.louppe@uliege.be)

Association of one or more MOOCs