Course(s) offered in spring semester 2022

Lecture number
22SQ07g
Semester
Spring semester 2022
Type
Vorlesung/Übung
Maximum number of participants
25

Events

Day Time Recurrence Duration Room
Wed. 12:00 to 14:00 Every two weeks 03/23/22 to 06/22/22 Gebäude Madrid - MAD 130
Thu. 10:00 to 12:00 Weekly 03/17/22 to 06/23/22 Gebäude Madrid - MAD 130

Lecturers

Prof. Dr.Claudius Gräbner-Radkowitsch

Phone
+49 461 805 2512
E-mail
claudius.graebner-radkowitsch-PleaseRemoveIncludingDashes-@uni-flensburg.de
Building
Gebäude Madrid
Room
MAD 219
Street
Munketoft 3b
Post code / City
24937 Flensburg
Show details

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]

Course outline