|Lecturer:||David Gremminger, M.Sc.|
|Course description:|| |
|Recommended for this semester or higher:||1|
|Course can be taken as part of following programs/modules:|| |
Economics and Finance
Data Science in Business and Economics
|Prerequisite for:|| |
DS404B Big Data Computing
Previous knowledge in programming is required (preferrably with R). Basic knowledge about R and statistics.
|Course Type:||Lecture (2 weekly lecture hours)|
Will be offered via asynchronous lecture videos weekly + bi-weekly Q&A sessions.
Limited to 45 participants. Registration open for all (no first-come, first-served). Registration will open on September 1, 2021 on Alma - end of registration time: Sunday, October 10, 2021 (23:55pm). If the number of applications (limited to 45 participants) exceeds the number of places available, we unfortunately will not be able to accept all of the applicants. In this case, a random selection will be made from all the applications received.
|Method of Assessment:|| |
Written exam (90 Minutes), or oral examination, or assignments or presentation or online assessment (90 Minutes)
The course deals with the workflow of empirical analyses, applying all steps from data collection through data management to data output using the statistical software R. The focus is on dealing with large data sets and implementation of all data preparation and analysis steps in code to ensure replicability.
Through the tutorial, students will be able to reflect on the concepts presented in theory, and to apply these concepts to advanced research problems.
Grolemund, G. & Wickham, H. (2017): R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media.