Advanced Data Analytics II

Lehrinhalte

• Data cleansing, imputation and feature selection or reduction methods • Data mining methods, especially for classification purposes • Approaches for building models with best predictive power • Developing advanced scripts in a professional programming language (e.g. R or Python)

Art der Vermittlung

Präsenzveranstaltung

Art der Veranstaltung

Pflichtfach

Empfohlene Fachliteratur

• Bruce, P. C., Gedeck, P. & Dobbins, J. (2024). Statistics for Data Science and Analytics. Wiley. • Levine, D. M., Szabat, K. A. & Stephan, D. F. (2019). Business Statistics: A First Course (8th ed.). Pearson. • McClave, J. T., Benson, P. G. & Sincich, T. (2017). Statistics for Business and Economics (13th ed.). Pearson. • Provost, F. & Fawcett, T. (2013). Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O'Reilly Media. Depending on the selected programming language: • Ashford, D. (2024). Python For Data Science: Hands-On Guide To Learn Python Programming For Data Analysis And Visualization. Independently published. • Peng, R. D. (2016). Exploratory Data Analysis with R. Lulu.com. • Vanderplas, J. (2023). Python Data Science Handbook: Essential Tools for Working with Data (2nd ed.). O’Reilly Media. • Warner A. (2022). Python for Absolute Beginners: A Step by Step Guide to Learn Python Programming from Scratch, with Practical Coding Examples and Exercises. Independently published.

Lern- und Lehrmethode

Case study using complex datasets, group work, presentations by students, blended learning elements (self-study research elements in the context of the case study)

Prüfungsmethode

• 50% assessment of periodic case study group presentations • 50% assessment of final presentation (assessment criteria: correctness of content and methodology, completeness of the solution, level of detail of the solution)

Voraussetzungen laut Lehrplan

Foundations of Data Analytics and Statistical Programming

Schnellinfos

Unterrichtssprache

Deutsch

Studienjahr, in dem die Lerneinheit angeboten wird

SS2027

Incoming

Nein

Lernergebnisse der Lehrveranstaltung

After successfully completing this course, students will be able to • apply their knowledge of the steps incorporated in a standard datamining process, • use their knowledge to implement complex data mining approaches such as classification techniques, • check, evaluate and improve the quality of data mining models, • identify and explain the causes and options for model quality restrictions, and • come up with economically reasonable suggestions, based on the results of the statistical analyses.

Kennzahl der Lehrveranstaltung

0948-25-01-BB-DE-12