Project Management & Data Analytics (Master)

Lehrinhalte

PMDA

Art der Vermittlung

face to face

Art der Veranstaltung

compulsory

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

Lecture, discussion, exercises and group work, blended learning elements (self-studies, self-assessment quizzes)

Prüfungsmethode

• 51% exercises and group work assignments (assessment criteria: correctness of content and methodology, completeness of the proposed solution) • 49% written examination (assessment criteria: correctness of content and methodology, completeness of the answer, level of detail of the answer)

Voraussetzungen laut Lehrplan

Foundations of Data Analytics and Statistical Programming

Schnellinfos

Akademischer Grad

Bachelor

Unterrichtssprache

Englisch

Studienjahr, in dem die Lerneinheit angeboten wird

2026

Incoming

Nein

Lernergebnisse der Lehrveranstaltung

After successfully completing this course, students will be able to • explain the statistical tasks or approaches necessary in each step of a standard datamining process in order to build reasonable data mining models, • understand the difference between statistical approaches which confirm (e.g. contingency, correlation) and those which identify (e.g. principal component analysis) dependencies between variables, • describe the systems and implementation requirements of data mining approaches/models and offer detailed and technically proficient explanations of their advantages and drawbacks, • apply these data mining approaches in concrete and practically relevant scenarios, covering performing analysis, critically assessing the results and, if necessary, selecting alternative approaches to obtain optimum solutions to the problem at hand, and • enrich data by applying feature engineering.

Kennzahl der Lehrveranstaltung

0948-25-01-BB-DE-11