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Data Science Toolbox

Lecture Overview
Course type
Lecture
CP (ECTS)
6
Contact hours
4h per week
Exam
Written exam (90 minutes, graded)
Programs
Economics (B.Sc.)
Nachhaltiges Management (B.Sc.)
Volkswirtschaftslehre (B.Sc.)
Wirtschaftsinformatik (M.Sc.)
Wirtschaftsingenieurwesen (M.Sc.)
Wirtschaftsmathematik (M.Sc.)
Prerequisites

Semester
Winter
Time
Lecture: TBA
Tutorial: TBA
Location
TBA

Course description

Our world and in particular professional life are increasingly governed by data. Due to steadily amount, complexity, and importance of data, properly dealing with data has emerged as one, if not the most important competency today – basically regardless of business domain. This course will cover a “tool box” of methods for dealing with data and information, following the information life cycle. This includes approaches to collect data (e.g., by surveys, experiments, web crawling techniques), structuring and pre-processing (e.g., filtering, clustering), data visualization (e.g., static, online, networks), as well as analytical methods (network analysis). Moreover, the course covers fundamental statistical questions and applies the learned content directly within tools such as Java and R. The covered content is complemented and practically recapitulated by means of case studies and data-based examples. Moreover, the course is accompanied by guest lectures from practice. As a result, the course’s participants will learn to independently design and implement data-based projects. The course’s objective is to convey a basic understanding for data-based projects and research objectives as well as practical skills to deal with data. This will include, among other aspects, the following topics: Survey and experiment design, execution, and evaluation • web crawling • data visualization • filtering and clustering • data cleaning and pre-processing • (social) network analysis • machine learning 101 • linear regression.

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