369410a Applied Statistics

Zuletzt geändert:	12.05.2023 / von Carlsburg
EDV-Nr:	369410a
Studiengänge:	Data Science (Master, berufsbegleitend) , Prüfungsleistung im Modul Applied Statistics in Semester 1 2 Häufigkeit: immer
Dozent:
Sprache:	Deutsch
Art:	-
Umfang:	4 SWS
ECTS-Punkte:	6
Workload:	Die Prüfungsleistung beinhaltet die schriftlichen Bearbeitung von Fallstudien.
Prüfungsform:
Beschreibung:	In this course we cover a vast set of tools for modeling and understanding complex datasets. In particular, we discuss the fields of exploratory data analysis (EDA) and statistical learning, which is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning (James et al, 2013). Throughout the course you are provided with applications which illustrate how to implement each of the statistical methods using Python as well as the popular statistical software package R. Agenda: Introduction to Applied Statistics Descriptive Statistics Exploratory Data Analysis (EDA) Statistical Hypothesis Testing Inferential Statistics Introduction to Statistical Learning Assessing Model Accuracy Linear Regression Classification Methods Resampling Methods Linear Model Selection Decision Trees Bagging & Random Forrest Clustering
Literatur:	Bzdok, D. (2017). Classical statistics and statistical learning in imaging neuroscience. Frontiers in neuroscience, 11, 543. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188. Fields, A. (2018). Discovering statistics using IBM SPSS statistics. Thousand Oaks, CA. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). New York: Springer. Kozyrkov, C (2018). What Great Data Analysts Do — and Why Every Organization Needs Them. Harvard Business Review (December). VanderPlas, J. (2016). Python data science handbook: essential tools for working with data. O'Reilly Media, Inc. Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O'Reilly Media, Inc. Weitere Literatur finden Sie in der HdM-Bibliothek.