Portal
Foundations of Data Analytics and Statistical Programming
Lehrinhalte
• 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)
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 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.
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
Keine
Schnellinfos
Unterrichtssprache
Englisch
Studienjahr, in dem die Lerneinheit angeboten wird
WS2027
Incoming
Nein
Lernergebnisse der Lehrveranstaltung
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.
Kennzahl der Lehrveranstaltung
0948-25-01-BB-DE-07