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Data Science UE

level of course unit

second cycle, Master

Learning outcomes of course unit

Students
• are familiar with a software with libraries for carrying out data analysis
• are able to use this software
• can carry out appropriate evaluations and analyses using the software for defined examples

prerequisites and co-requisites

According admission requirements

course contents

• Introduction to software which will be used (e.g. Python)
• Collecting and preparing data using software
• Analysis and presentation of exemplary data using different approaches (e.g. regression, decision trees, etc.)

recommended or required reading

• Dorschel (2015): Praxishandbuch Big Data: Wirtschaft – Recht – Technik, Springer Gabler Verlag
• Grus (2016): Einführung in Data Science: Grundprinzipien der Datenanalyse mit Python, O’Reilly Media
• McKinney (2015): Datenanalyse mit Python: Auswertung von Daten mit Pandas, NumPy und IPython, O’Reilly Media
• Guido, Mueller (2016): Introduction to Machine Learning with Python, O’Reilly Media
• Gibson, Patterson (2016): Deep Learning: The Definitive Guide: A Practitioner's Approach, O´Reilly Media

assessment methods and criteria

Assignments

language of instruction

German

number of ECTS credits allocated

4

planned learning activities and teaching methods

practical course

semester/trimester when the course unit is delivered

2

name of lecturer(s)

Director of studies

year of study

1. study year

recommended optional program components

not applicable

course unit code

not applicable

type of course unit

compulsory

mode of delivery

practice

work placement(s)

not applicable