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Methods of Data Analysis

Instructor: Gasper Tkacik

Teaching Assistants: Anna Andersson, Jan Humplik



The course assumes introductory knowledge of physics, and familiarity with a programming language (Matlab, Mathematica, C, etc) that supports numerical computation. We will cover one topic per week, and have one problem set per week that will require working with real or simulated data (analysis plus some programming); the homework load is substantial. This is a very hands-on course that should provide useful practical experience. 


100% grade based on 6 problem sets, 2 weeks time for the completion of each. At each recitation: (i) TAs will help with the currently outstanding homework (from the lecture 1 week back), and (ii) TAs will discuss the results from the homework that was due (from 2 weeks back).





Schedule (subject to change)

Date Topic Location Other
Nov 28 Correlation functions, spectral estimation, and filtering Mondi 3  
Dec 5 Random numbers, Gillespie (SSA) simulation Mondi 3  
Dec 12 Monte Carlo and entropic sampling Mondi 3  
Dec 19 Working with probability distributions, kernel density estimation, maxent models Mondi 3  
2015 topics to be announced    


File Due Date
Homework 1  
Homework 2  
Homework 3  
Homework 4  

Additional Downloads

Lecture 1

Notes and Problem Set 1.

Files (Matlab format) or files (text format), both including relevant chapters from Numerical Recipes.

Lecture 2

Notes and Problem Set 2.

Chapters from Numerical Recipes.

Lecture 3

Notes and Problem Set 3.

Lecture 4

Notes and Problem Set 4.

Dataset from Sachs et al.

Lecture 5

Notes and Problem Set 5.


Lecture 6

Notes and Problem Set 6.