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Data Science and Scientific Computing track core course

Instructor: Bernd Bickel, Jon Bollback, Christoph Lampert, Gasper Tkacik

Teaching Assistants: Anna Levina, Srdjan Sarikas

The course will consist of 4 segments of approximately 3-4 weeks each. Each segment will follow the same structure: the first week will consist mainly of lectures and background material study, the second week will introduce a small project based on real data or a computational assignment, whose results will then be presented in the last week. The emphasis will be on dealing with data and computations in a hands-on fashion. 

Tentative summary of the course segments:

Segment 1: Working with real data (Instructor: Gasper Tkacik)

Goal: Characterizing basic statistical properties of an unknown dataset

Segment 2: Predictive models (Instructor: Christoph Lampert)

Goal: understand and be able to handle predictive models

Segment 3: Simulations and numerics (Instructor: Bernd Bickel)

Goal: Understand and apply basic computational techniques for simulations in a variety of applied science problems, with focus on differential equations

Segment 4: Bayesian models (Instructor: Jon Bollback)

Goal: Understand and apply Markov Chain Monte Carlo methods, particularly for inference of model parameters






Requirements/Exams: homework sheets, group projects


Credits: 6 ECTS

Final Grade: 50% homework, 50% projects

Prerequisites: list of skills (PDF)   mock exam to test if you have the prerequisites (PDF)






Schedule (subject to change)

Date Topic Location Other
Feb 29 Cycle 1: Histograms Mondi 3  
Mar 2 Cycle 1: Histogram statistics, error bars Mondi 3  
Mar 7 Cycle 1: Correlations Mondi 3  
Mar 9 Cycle 1: PCA, K-means Mondi 3  
Mar 14 Cycle 1: Power spectra Mondi 3  
Mar 16 Cycle 1: Power spectra Mondi 3  
Apr 4 Cycle 1: K-means, ICA Mondi 3  
Apr 6 Cycle 1: Presentations Mondi 3  
May 17 Cycle 3 Mondi 3  
May 18 Cycle 3 Mondi 3  
May 23 Cycle 3 Mondi 3  
May 25 Cycle 3 Mondi 3  


File Due Date Example solutions
  Segment 1  
See lecture notes and below HW for Week 1 due Mar 8 (11am) to TAs  
See updated lecture notes and also below HW for Week 2 due March 20 to TAs  
See updated lecture notes and files below HW for Week 3 due March 28 to TAs, Projects due April 6 in class  
  Segment 2  
See S02E01.pdf file below HW for Week 1 due Apr 19 to TAs  
See S02E02.pdf file below HW for Week 2 due Apr 25 to TAs  
See S02E03.pdf file below description of final project (due Apr 27) and report (due May 5 to ChLa)  

Additional Downloads

Cycle1 Lecture notes (updated) containing homework assignments (gray text) and the project.

Ruderman image dataset

Additional image downloads

Relevant Numerical Recipes chapters


Segment 2:

Segment 3:

Lecture 1 (including exercise 1), Matlab scripts for lecture 1, additional notes (Witkin, Baraff, see HW)
Lecture 2 (including exercise 2), Matlab script for recording a movie
Lecture 3
Lecture 4
Project description, Code skeleton

Segment 4:

Lecture 1 (including homework 1)

Lecture 2

Lecture 3 (including project description)

Python code for trees and models (this code requires the following C library for functionality)


To take a look at the additional Downloads, please click here. (you must be logged in!)