Duration
30h Th, 60h Proj.
Number of credits
| Master of Science (MSc) in Data Science | 5 crédits | |||
| Master of Science (MSc) in Data Science and Engineering | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
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
Contacts
Gilles Louppe (g.louppe@uliege.be)