Advanced Data Analytics II

Brief description

• 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)

Mode of delivery

Präsenzveranstaltung

Type

Pflichtfach

Recommended or required reading and other learning resources/tools

• 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.

Planned learning activities and teaching methods

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

Assessment methods and criteria

• 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)

Prerequisites and co-requisites

Foundations of Data Analytics and Statistical Programming

Infos

Language of instruction

German

Academic year

SS2027

Incoming

No

Learning outcome

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.

Course code

0948-25-01-BB-DE-12