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Data Science VO

level of course unit

second cycle, Master

Learning outcomes of course unit

• are able to describe the content, results/uses and operating methods of data science
• are able to turn “questions” into requirements in the context of data science
• based on this, are able to define the process and tools and to implement/apply these

prerequisites and co-requisites

According admission requirements

course contents

• Introduction (data, information, knowledge, time components, goals)
• Data process (collection, preparation, analysis, presentation)
• Data preparation (adjustment, re-modelling, re-scaling, storage)
• Approaches to analyzing data
• Presentation/visualization of results
• Software (open source and proprietary software)
• Machine learning - process, approaches, implementation

recommended or required reading

• Dorschel (2015): Praxishandbuch Big Data: Wirtschaft – Recht – Technik, Sprin- ger 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

written exam

language of instruction


number of ECTS credits allocated


planned learning activities and teaching methods


semester/trimester when the course unit is delivered


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


mode of delivery


work placement(s)

not applicable