Data Analysis and Big Data
Component code / course code: UF-21.2
Semester: winter / summer
ECTS credits: 2
Lecture hours per week (SWS): 2
Lecturer: Prof. Dr. Winter
Language: German / English
Prerequisites:
Knowledge of statistics and data analysis as taught, for example, in the course Business Statistics
Qualification objectives:
In this course, students learn the theoretical foundations required to analyse large amounts of data ("big data"). They will then be able to apply selected machine learning methods using Python and present, interpret and critically evaluate the
resulting findings. Furthermore, they are familiar with the opportunities and risks of data and data analyses and have developed their own initial ethical standpoint on this.
Course contents:
- Introduction and basics
Data, data structures, databases, data plausibility checks - Supervised learning
Classification models (decision trees, k-nearest neighbours, support vector machine) and regression models, their areas of application as well as application and implementation in Python - Unsupervised learning
Cluster analysis (k-means method, DBSCAN) and association analysis (a-priori algorithm) as well as text-generating AI, their areas of application as well as application and implementation in Python
Teaching format (e.g. online/in person lecture/Seminar/Lab etc.): Courses with integrated exercises and case studies as well as self-study
Examination type: written exam
