Foundations of Data Analytics and Statistical Programming

Brief description

• Probability Distribution of Continuous and Discrete Random Variables • Distribution Tests • Normality Tests and Special Distribution Tests • Contingency • Correlation • Regression • Assessing Regression Models • Advanced Regression Modelling • Introduction to the basic concepts of a professional programming language (e.g. R or Python)

Mode of delivery

face to face

Type

compulsory

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 Edition. Pearson. • McClave, J. T., Benson, P. G. & Sincich, T. (2017). Statistics for Business and Economics (13th ed.). Pearson. • Peng, R. D. (2016). Exploratory Data Analysis with R. Lulu.com. • Provost, F. & Fawcett, T. (2013). Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O'Reilly Media.

Planned learning activities and teaching methods

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

Assessment methods and criteria

• 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)

Prerequisites and co-requisites

Keine

Infos

Cycle

Master

ECTS Credits

5.00

Language of instruction

English

Curriculum

Part-Time

Academic year

2025

Semester

1 WS

Incoming

Learning outcome

After successfully completing this course, students will be able to • differentiate univariate and multivariate methods from one another and explain their processes and applications, • explain the methodological and mathematical knowledge required for the preparation and basic analysis of data sets, • use a professional programming language (e.g. R or Python) to tackle statistical problems and generate concrete solutions based on data available by independently creating, testing and implementing scripts of low to medium complexity, • apply the essential fundamental principles of error-free and transparent programming, analyse the structure of more complex scripts and interpret individual commands, and • apply established methods for assessing and displaying results of statistical analyses including various diagram types, tables and reports and create these single-handedly.

Course code

0948-25-01-BB-DE-07