DS400 Data Science Project Management / Online

Lecturer:	David Gremminger, M.Sc.
Course description:	DS400
Language:	English
Recommended for this semester or higher:	1
ECTS-Credits:	9
Course can be taken as part of following programs/modules:	Economics and Finance European Management General Management International Business International Economics Management and Economics Data Science in Business and Economics
Prerequisite for:	Exam DS404B Big Data Computing successfully passed
Prerequisites:	Previous knowledge in programming is required (preferrably with R). Basic knowledge about R and statistics.
Course Type:	Lecture (2 weekly lecture hours)
Date:	Beginning of the first lecture: Tuesdays, November 3, 2020 from 10am c.t. - 12 noon (online)
Registration:	Limited to 45 participants. Registration open for all (no first-come, first-served). Registration will open on October 1, 2020 on ILIAS - end of registration time: Monday, October 19, 2020 (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. Preferred access for master students from the Data Science in Business and Economics program. Previous knowledge in programming is required (preferrably with R).
Downloads:	Ilias
Method of Assessment:	Written exam (90 Minutes), or oral examination, or assignments or presentation or online assessment (90 Minutes) Examination Timetable will be available on the website of the examination office.
Content:	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.
Objectives:	Through the tutorial, students will be able to reflect on the concepts presented in theory, and to apply these concepts to advanced research problems.
Literature:	Grolemund, G. & Wickham, H. (2017): R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media.