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DS400 Data Science Project Management

Lecturer: Prof. Dr. Stefan Mayer
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:

Data Science in Business and Economics
Economics and Finance
European Management
General Management
International Business
International Economics
Management and Economics

Prerequisite for:

DS404B Big Data Computing

Prerequisites: Previous knowledge in programming is required (either R or Python), as well as basic knowledge in statistics.
Course Type: Lecture (4 weekly lecture hours)
Date:

Beginning of the first session: Tuesday, October 18, 2022 from 2pm c.t. - 6pm (room PC Lab 008, ground floor, Nauklerstr. 47)

Registration:

Limited to 28 participants.
Registration open for all (no first-come, first-served). Registration will open on September 1, 2022 on Alma - end of registration time: Sunday, October 9, 2022 (23:55pm). If the number of applications (limited to 28 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 (either R or Python), as well as basic knowledge in statistics.

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. The focus is on dealing with large data sets and implementation of all data preparation and analysis steps in code to ensure replicability.  The course will be taught “bilingual”: all procedures and analyses are explained both in R and Python, and students should be proficient in both languages by the end of the course.

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. VanderPlas, J. (2016). Python Data Science Handbook. O’Reilly Media. jakevdp.github.io/PythonDataScienceHandbook/