86840 - Practical Statistics for Physics and Astrophysics

Learning outcomes

Nontrivial data analysis problems are frequently encountered in modern astronomy, cosmology and physics. They require an understanding of statistical methods, practical skills with software tools and sometimes some ingenuity that comes with experience. The student will gain a practical knowledge of statistical methods and software as applied to many example problems. Basic probability theory will be covered before learning about Bayesian and frequentist inference problems, Monte Carlo techniques, Fisher matrices, parameter estimation, non-parametric tests, hypothesis testing, and supervised and unsupervised classification and regression problems. The student will become familiar with current software in Python for analysing data and fitting models while getting an understanding of the theory behind them.

Course contents

The course will cover the theoretical background required to understand many of the modern data analysis techniques used in astrophysics and cosmology.

Some of the topics covered are:

1) the basic mathematics of probability

2) common probability distributions

3) Bayesian inference

4) regression 

5) hypothesis testing

6) model selection

7) error forecasting and experimental design

8) numerical methods for Bayesian inference

9) some basic machine learning techniques

Readings/Bibliography

Detailed lecture notes are provided.

Teaching methods

Every week there are 2 hours of lectures on the theory of statistics and 4 hours of computer laboratory where practical mini-projects are done.

Assessment methods

The computer tutorials / mini-projects are graded.

An orale exam.

Teaching tools

Blackboard

Power point 

Python notebooks for tutorials

Online mini-courses on specific software tools.

Office hours

See the website of Robert Benton Metcalf