Welcome to the Master of Data Science at the Hertie School Data Science Lab. We are looking forward to having you join us in Berlin.

This guide gathers everything we recommend you review before the programme begins - a chance to refresh the programming and mathematics fundamentals that your first-year courses build on, so you can hit the ground running.

This preparation package gathers the materials your first-year courses are built on, alongside recommended platforms for reviewing the fundamentals in programming and mathematics. There is no need to rush, and you are not expected to master everything - the aim is to arrive confident with the basics.

Do I already know enough - and what is enough? A fair question, and the best way to judge is against the courses themselves. The course syllabi and the summer bootcamp below set out exactly what your first-year classes assume and cover. Skim them, see where you feel solid and where you don’t, and focus your preparation there.

The summer bootcamp

Before teaching begins, the summer bootcamp brings everyone up to a common baseline and is the clearest picture of where your courses start from.

It runs through the essentials you’ll lean on from day one: setting up R and RStudio, the basics of base R (objects, vectors, functions and dataframes), version control with Git, data visualisation with ggplot2, and data wrangling with dplyr - plus a first look at vectors and matrices that feeds straight into Mathematics for Data Science. Teaching pairs short lectures with hands-on labs on real datasets, all geared towards preparing you for the Introduction to Data Science course.

  • Bootcamp

    Summer bootcamp materials

    Last year's materials give a good sense of the topics covered and the level expected.

    View materials

Essential course materials

These are the materials featured on your course syllabi - the books and references your classes draw on directly. You may well want to start looking into them already.

  • Syllabus

    Introduction to Data Science

    The syllabus for your first-year IDS course - what it covers, what it assumes, and the resources it points to.

    Open syllabus (PDF)
  • Syllabus

    Mathematics for Data Science

    The syllabus for the Mathematics for Data Science course, setting out the topics it reviews and expands on.

    Open syllabus (PDF)
  • Book - free online

    R for Data Science (2e)

    The core reference featured in the IDS syllabus. A practical, hands-on introduction to data science in R - free to read online.

    Read online
  • Recommended reading

    An Introduction to Statistical Learning (DUMMY)

    A widely used, beginner-friendly introduction to statistical learning - free to read online. (PLACEHOLDER EXAMPLE.)

    Read online
  • Recommended reading

    Placeholder Dummy text

    (PLACEHOLDER EXAMPLE.)

    Read online

Additional online resources

As an MDS student you also receive premium licences for two self-learning platforms. These are excellent for additional practice and review at your own pace - use them to shore up whichever areas the materials above show you need.

For different learning styles - video lectures, extra exercises and more - the Further resources page lists optional extras.

Questions before you arrive? You can reach the Data Science Lab via the Hertie School DSL page. See you soon - and welcome aboard!