Literature & Resources

While this course does not come with a list of mandatory readings, we will often refer to some central resources in python and R, 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.

Python

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

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

Supplementary literature

R

• R Markdown: The Definitive Guide
• Efficient R Programming
• Functional Programming in R (Stanford, Hadley)
• Exploring Enterprise Databases with R

Further Ressources

Community

• Kaggle: Crowdsourced data science challanges. Nowadays also provides a vivid community where you find datasets, notebooks for all kind of data science exercises.
• Stackoverflow: Q&A community for coding issues. Most coding questions you could come up with have already been answered, or will be answered fast (if you ask right ;)).
• Danish Data Science Community: Community of Data Scientist here in DK. Go here for finding project partners, check whats thrending in Danish DS, and ask conceptual questions.

Tools & Helpers

• rmd to ipynb converter
• ipynb to pdf converter