Lehrende: Thomas Heil; Prof. Dr. Franziska Julia Peter
Veranstaltungsart: Seminar / Übung
Orga-Einheit: Corporate Management & Economics
Anzeige im Stundenplan: Data Science
Semesterwochenstunden: 3
Credits: 6,0
Standort: Campus der Zeppelin Universität
Unterrichtssprache: Englisch
Min. | Max. Teilnehmerzahl: 6 | 35
Prioritätsschema: Standard-Priorisierung
Inhalte: |Basics Introduction to course project & structure Subject-related introduction to the basics of R & overview of alternative software | Data Collection and Data Preparation Basics of data extraction Data Cleaning and Pre-Processing Descriptive Data Analysis Exercise & Lab Sessions | Data Analysis & Methods (I) Problem definition Supervised vs. Unsupervised Learning Classification & Clustering Text Mining Exercise & Lab Sessions | Data Analysis & Methods (II) Regression Decision Trees Model Selection & Evaluation Methodological Outlook Exercise & Lab Sessions | Project work Conception Research Phase Results & Paper & Presentation Prerequisites Basic knowledge of statistics Basic knowledge of R Learning objectives: The students are oriented about the conceptual basics and the different fields of application of data science. The students receive an overview of the central concepts and content-related subject areas. They are introduced to the general fundamentals of various techniques as well as their practical functioning and can furthermore critically evaluate them. They are familiar with the different process steps of a data science project, so that they can also independently apply the taught learning contents in the context of data collection, processing, analysis and interpretation. After participating in the course, the students should thus be able to carry out smaller Data Science projects on their own. Further information on the examination results: Midterm 50%, Endterm 50% Project work: Presentation & Paper Literature: Provost, F.& Fawcwtt, Tom (2013) Data Sciecne for Business Hastie, Tibshirani & Friedman (2008) The elements of statistical learning. James, Witten, Hastie & Tibshirani (2017). An introduction to statistical learning.
Weitere Informationen zu den Prüfungsleistungen: Midterm: 50%, Endterm: 50%
