Programming

Why R and Python?

A question that comes up often is why the MDS programme focuses on two programming languages, when Python clearly leads the pack as the default language in machine learning, deep learning and many other data and DevOps workflows. There are a few reasons:

• R is still superior for statistics. It remains the stronger language for building statistical models, with a robust ecosystem of packages maintained by scientists and statisticians - an area where Python still lags.

• It depends where you want to work. Python is the desired language for industry roles, but for government agencies, research foundations, think tanks, international organisations or academia, R (and Excel) is still the go-to tool. Being proficient in both gives you the best exposure to the job market and the ability to work in and manage cross-language teams.

• Multilingual coding is a strong signal. Being able to code effectively in more than one language is a sign of good technical talent to potential employers.

What’s expected

The clearest picture of what your courses assume is the Introduction to Data Science syllabus - it sets out what the course covers and the resources it draws on. A standout among them is R for Data Science (2e), a practical, free-to-read introduction that’s well worth starting before you arrive.

• Syllabus

  Introduction to Data Science

  What the IDS course covers, what it assumes, and the materials it points to.

  Open syllabus (PDF)
• Book - free online

  R for Data Science (2e)

  The core R reference featured in the IDS syllabus - free to read online.

  Read online

Practising with DataQuest

As part of your MDS education you have access to a premium DataQuest account - a self-learning platform with real-world coding challenges to help you build technical skills effectively. It will accompany and complement your in-class training throughout the programme, and it’s the best way to get your Python and R up to the level the first-year courses assume.

DataQuest is organised into learning paths. Three are most relevant to you - we recommend starting with Data Scientist in Python and Data Analyst in R to refresh the essential foundational coding skills before the semester begins.

• Recommended

  Data Scientist in Python

  Python foundations through data analysis, visualisation and cleaning.

  View path
• Recommended

  Data Analyst in R

  R foundations for data analysis - useful from day one of the programme.

  View path
• Advanced / optional

  Data Engineer

  Pipelines, large datasets and production databases for those further ahead.

  View path

Python fundamentals

If you are not yet confident with your Python, please complete Part 1: Python Introduction of the Data Scientist in Python path on DataQuest before the new semester.

For Parts 2 & 3, data visualisation and data cleaning will be essential in your Machine Learning class. Continue with the subsequent parts during your studies at Hertie to complement what you learn in class.

R fundamentals

Similarly, if you are not yet confident with your R, please complete Part 1: Introduction to R of the Data Analyst in R path on DataQuest before the new semester.

If you already know everything in Part 1, start on Part 2 and Part 3, as that knowledge will be useful for your Introduction to Data Science course. That class will not be an introduction to R, so please arrive prepared to get the most out of it. Continue with the subsequent parts during your studies to complement your learning in class.

The Data Engineer path (for the more advanced)

If you are at a more advanced level in Python and R and want to explore other relevant areas, take a look at the Data Engineer learning path. The foundational sections are the same as the other paths, but there is additional focus on building data pipelines, handling large datasets with parallel processing and data structures, optimising dataframe memory footprint for large operations, and many other interesting topics.