DS400 Data Science Project Management

Lecturer:	Ass.-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:	For more information please refer to alma.
Prerequisite for:	DS404B Big Data Computing
Prerequisites:	Previous knowledge in programming is required (either R or Python, ideally basic knowledge in both), as well as a basic understanding of statistics. Ensure to review the provided R / Python refresher slides and videos before the first lecture.
Course Type:	Lecture (4 weekly lecture hours)
Date:	Beginning of the first session: October, 17 - November 28 (room PC Lab 008, ground floor, Nauklerstr. 47) from 2pm - 6pm c.t. *** Special Lecture Dates (PC Lab): Friday, November 03, 2023 from 9am- 4pm c.t Friday, November 10, 2023 from 9am-4pm c.t Thursday, November 16, 2023 from 2pm-6pm c.t Thursday, November 23, 2023 from 2pm-6pm c.t
Registration:	Limited to 28 participants. Registration open for all (no first-come, first-served). Registration will open on September 1, 2023 on alma - end of registration time: Sunday, October 8, 2023 (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, ideally basic knowledge in both), as well as a basic understanding of statistics. Ensure to review the provided R / Python refresher slides and videos before the first lecture.
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/