Literature & Resources

While this course does not come with a list of mandatory readings, we will often refer to some central resources in R and python, which for the most part can always be accessed in a free and updated online version. We generally recommend you to use these amazing resources for problem-solving and further self-study on the topic.

Main Literature

These pieces of work can be seen as main references for data science using R and Python. We will frequently refer to selected chapters for further study.

R

  • Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O’Reilly Media, Inc. Online available here
  • Baumer, B., Kaplan, D. & Horton, N. (2020) Modern Data Science with R (2nd Ed.). CRC Press Online available here
  • Kuhn, M., Silge, J. (2020) Tidy Modeling with R Online available here

Python

  • VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. O’Reilly Media, Inc. Online available here

Supplementary literature

R

Further Ressources

Data Science Cloud services

  • Notebook bases:
    • Google Colab: Googles popular service for editing, running & sharing Jupyter notebooks (Only Python Kernel, but R kernel can be accessed via some tricks)
    • Deepnote: New popular online notebook service with good integration to other services (Python, R & more)
    • Kaggle: Also provides their own cloud-based service co create and run computational notebooks. Convenient, unlimited, but a bit slow (Pyhton, R ).

Community

  • Kaggle: Crowdsourced data science challanges. Nowadays also provides a vivid community where you find datasets, notebooks for all kind of data science exercises.

Tools & Helpers