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README.md
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course-syllabus.md
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lecture1.md
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nb01-box-loop.ipynb
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DATS0001 Foundations of Data Science

Materials for DATS0001 Foundations of Data Science, ULiège, Fall 2025.

Agenda

Date Topic
September 15 Course syllabus [PDF]
Lecture 1: Build, compute, critique, repeat [PDF]
nb01: Build, compute, critique, repeat [notebook]
Reading: Blei, Build, Compute, Critique, Repeat, 2014 [Section 1]
Reading: Box, Science and Statistics, 1976
September 22 Lecture 2: Data and exploratory analysis [PDF]
nb02a: Arrays with NumPy
nb02b: Arrays with JAX
nb03b: Data wrangling with Pandas
nb02d: Exploratory data analysis
September 29 Lecture 3: Visualization [PDF]
nb03: Plots with Matplotlib
Reading: Rougier et al, Ten Simple Rules for Better Figures, 2014
October 6 Lecture 4: Latent variable models [PDF]
nb04b: Probabilistic PCA
Reading: Blei, Build, Compute, Critique, Repeat, 2014 [Sections 1-3]
October 13 Lecture 5: State-space models [PDF]
nb05: State-space models
October 20 Lecture 6: Markov chain Monte Carlo [PDF]
nb06a: Markov chains
nb06b: MCMC
Reading: Gelman et al, Bayesian Data Analysis, 3rd, 2021 [Chapter 11]
November 3 Lecture 7: Expectation-maximization [PDF]
nb07: Expectation-maximization
Reading: Dempster et al, Maximum Likelihood from Incomplete Data via EM, 1977
November 10 Lecture 8: Variational inference [PDF]
nb08a: Coordinate ascent variational inference
nb08b: Automatic differentiation variational inference
Reading: Kucukelbir et al, Automatic differentiation variational inference, 2016
November 17 Lecture 9: Model criticism and validation [PDF]
nb09a: Model checking
nb09b: Model comparison
nb09c: Bayesian Occam's razor
November 24 No lecture (general strike)
December 1 Lecture 10: Wrap-up case study [PDF]
nb10: Estimating air pollution from satellite data
December 19 Homework deadline

Homework

See instructions in the homework notebook. Deadline: December 19, 2025.

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