DATS0001 Foundations of Data Science

Materials for DATS0001 Foundations of Data Science, ULiège, Fall 2022.

Agenda

Date	Topic
September 26	Course syllabus Lecture 1: Introduction `nb01`: Build, compute, critique, repeat [notebook] Reading: Blei, Build, Compute, Critique, Repeat, 2014
October 3	Lecture 2: Data `nb02`: Tables [notebook] `nb03`: Data wrangling [notebook] Reading: Harris et al, Array programming with NumPy, 2020
October 10	Lecture 3: Visualization `nb04`: Plots `nb05`: Visualizing high-dimensional data
October 17	Lecture 4: Probabilistic modeling `nb06`: Probabilistic modeling
October 24	Lecture 5: Approximate Bayesian inference (part 1) `nb07`: Point-valued estimates
November 7	Lecture 6: Approximate Bayesian inference (part 2) `nb08`: Markov Chain Monte Carlo, Variational inference
November 14	Lecture 7: Model criticism `nb9`: Model evaluation `nb09`: Model revision and comparison
November 21	Lecture 8: Applications `nb12`: Answering questions and claiming discoveries `nb13`: Bayesian decision theory
November 28	Lecture 9: Wrap-up case study `nb14`: Wrap-up case study

TBD.

Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
assets		assets
data		data
figures		figures
pdf		pdf
README.md		README.md
course-syllabus.md		course-syllabus.md
index.html		index.html
nb01-box-loop.ipynb		nb01-box-loop.ipynb
nb02-tables.ipynb		nb02-tables.ipynb
nb03-data-wrangling.ipynb		nb03-data-wrangling.ipynb