Academic & Empirical Methods

level of course unit

Introduction

Learning outcomes of course unit

The students are able to:
• Describe and apply the fundamentals of academic work
• Research, evaluate and quote specialist literature
• Present and apply academic methods of literature analysis
• Understand and apply concepts and methods of descriptive and explorative statistics
• Independently analyze and structure data sets as well as present and critically evaluate information

prerequisites and co-requisites

none

course contents

• Principles of academia and academic work
o Science and academic language
o Literature research
o Citation and source work
o Avoidance of plagiarism
• Principles of data analysis
o Statistical characteristics and variables
o Univariate and multivariate description and exploration of data
o Correlation and regression analyses
o Basic programming knowledge for data preparation
o Analysis and presentation of information from data sets

The module contains 25% exercises. This form of teaching takes place in small groups.

recommended or required reading

• Heisen, M. R. and M. Theisen 2017. Wissenschaftliches Arbeiten: erfolgreich bei Bachelor- und Masterarbeit. Munich: Franz Vahlen
• Fahrmeir, L., R. Künstler, I. Pigeot, I. and G. Tutz, 2012. Statistics: Der Weg zur Datenanalyse. 7th edition. Berlin: Springer
• Fahrmeir, L., Kneib, T. & Lang, S., 2009. Regression: Modelle, Methoden und Anwendungen. 2nd edition. Berlin: Springer
• Hetland, M., 2005. Beginning Python From Novice to Professional. New York: Springer-Verlag
• Ernesti, J. and P. Kaiser, 2012. Python 3 - Das umfassende Handbuch. 3rd edition. Bonn: Galileo Computing
• Weitz, E. 2018. Konkrete Mathematik (nicht nur) für Informatiker. Mit vielen Grafiken und Algorithmen in Python. Wiesbaden: Springer