Recommended Reading and Activities

We strongly encourage all students to explore the reading list and suggested activities provided below and to close any potential gap in required background knowledge. These background texts and online courses cover topics students are expected to be familiar with prior to commencing their graduate studies in Data Science at ETH Zurich:

Statistics and Probability Theory

John A. Rice. 2007. Mathematical Statistics and Data Analysis. Third Edition. Brooks/Cole, Belmont, CA. ISBN 978-0-495-11868-8. (Chapters 1-11 and Chapter 14)

Calculus and Linear Algebra

Students are expected to have passed courses in Calculus and Linear Algebra.

Programming

Advanced programming skills are required for the Master’s program in Data Science. Students who have not been exposed to much programming are strongly advised to gain additional programming experience before the program commences.

Databases and Data Modelling

The external pageStanford University MOOC “Databases” by Jennifer Widom provides students with a good introduction to databases:

Introduction to Machine Learning

The external pageCoursera MOOC “Machine Learning” by Andrew Ng is a good introductory course on Machine Learning:

Self-study on Missing Requirements

This Downloaddocument (PDF, 183 KB) gives a few pointers to ETH lectures (most in German) and to a few books to choose from (in English) to support you in your self-study.