Lecture number

22SQ07g

Semester

Spring semester 2022

Type

Vorlesung/Übung

Maximum number of participants

25

Events

Lecturers

Description

Learning goals:

You will be introduced to the statistical programming language R. At the end of the course you will be able to perform all essential steps of a quantitative data analysis in R yourself. This includes:

(i)               data acquisition and preparation,

(ii)             visualization of the data on a publication-ready level, and

(iii)           analysis of the data using both traditional statistics and regression analysis, as well as modern tools from the field of machine learning.

You will learn how to write visually appealing and reproducible reports in R Markdown and use the version control system Git for (collaborative) code development.

Type of examination:

Your final grade is composed of:

-        Two short reports comprising a reproducible data analysis during the semester (25% each; to be completed at home)

-        A final open book exam (50%)

Further information:

Students will need to bring their personal laptops to class; you will need to install R, R Studio, and Git on your Laptop. All software used during this course is free and open source. All educational ressources such as textbooks are also free and open source.

Literature

Ismai, C. & Kim, A. (2021): Statistical Inference via Data Science. Online: moderndive.com/index.html

Wickham, H. & Grolemund, G. (2017): R for Data Science. Online: r4ds.had.co.nz/index.html

Hanck, C., Arnold, M., Gerber, A. & Schmelzer, M. (2021): Introduction to Econometrics with R. Online: www.econometrics-with-r.org

Wickham, H. (2019): Advanced R. Online: adv-r.hadley.nz

More literature will be provided through the course outline [see Link above]