Humboldt Data Science Statistical Methods and Python tools for Data Analyses

Welcome on the Data Science Webpage

by Marek Kowalski, Mickael Rigault, Jakob Nordin

Course content

In this course you will learn about stastical methods used in data analysis. It will provide a theoretical foundation and introduce and practice python tools. The course will cover:

  • Characterising Distributions
  • Standard Theoretical Distributions
  • Errors
  • Estimation
  • Maximum Likelihood
  • Least Square
  • Hypothesis testing
  • Probability and Confidence Levels
  • Nuisance Parameters & Numerical Minimization
  • Monte Carlo Methods

General information

The latest classes are here.

The directory hosting the class document is here.

Time and location:

  • Exercises: 12h20-13h05 room 2.201 (or 2.202)
  • Lecture: 13h20-14h50 room 2.201 (or 2.202)