DS404B Big Data Computing

Lecturer:	Jonathan Fuhr, M.Sc.
Course description:	DS404B
Language:	English
Recommended for this semester or higher:	2
ECTS-Credits:	6
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:	---
Prerequisites:	Exam DS400 Data Science Project Management successfully passed.
Limited attendance:	20
Course Type:	Lecture
Date:	Lecture: on Tuesdays from 12 (noon) c.t. - 2pm, PC Lab 008, Nauklerstr. 47 In addition (attendance mandatory!): Monday, May 23; Monday, June 20; Monday, July 11 from 10 c.t. - 12 am, PC Lab 008, Nauklerstr. 47 Beginning April 19, 2022
Registration:	Limited to 20 participants. Registration open for all (no first-come, first-served): Registration in Alma will open on February 1 - end of registration time: April 10. If the number of applications (limited to 20 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.
Downloads:	ILIAS
Method of Assessment:	Written exam, or oral examination, or assignments or presentation or online assessment
Content:	This course deals with the computational challenges that arise when working with large datasets and/or computationally expensive methods, and introduces potential solutions to those challenges. The focus of the first part of this module will be on solutions that can be implemented locally (i.e., on one’s own machine), while the second part discusses external solutions (i.e., in the cloud / on a computing cluster). The course will primarily be taught in R, but several concepts are language-independent or can easily be extended to other languages.
Objectives:	Students can reflect the applicability of different concepts for different problems related to large datasets and complex computations. They can apply the taught methods and extensions independently to new problems; they are able to solve advanced computational problems with state-of-the-art methods independently.
Literature:	Walkowiak, S. (2016). Big Data Analytics with R. Packt Publishing. Kane, M., Emerson, J. W., & Weston, S. (2013). Scalable Strategies for Computing with Massive Data. Journal of Statistical Software, 55(1), 1–19. Accessible via EBSCOhost.