Data Science & Intelligent Analytics PT
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Software Development for Data Science 1

level of course unit

Master course

Learning outcomes of course unit

Graduates are familiar with software development concepts that are frequently used in data science. Furthermore, they are familiar with the deployment of these concepts in frequently used software development environments in the area of data analysis (e.g. in Python, MathLab or R). Students are also aware of the tools and software systems that are necessary for software development.

prerequisites and co-requisites

not specified

course contents

The course deals with the software development process while important aspects of software engineering are addressed in an overview (e.g. requirements acquisition and documentation). The core aspect is the usage of software systems in data-intensive application contexts. The topical field is observed at the concept level (e.g. procedural, object-oriented and functional programming paradigms) as well as in various programming languages with respect to concept characteristics (e.g. Python, MathLab and R). Deployed software ecosystems are illustrated in an overview and their application is demonstrated in detail.
Special focus is on the usage of effective and efficient data structures and their im-plementation.

The teaching content encompasses the following topics:
-The process of software engineering and project management for data-intensive applications
-Programming paradigms for usage in the area of data science
-Comparative illustration of suitable programming languages within the context of data-intensive applications
-Effective and efficient data structures for data-intensive applications
-Tools and software ecosystems for the development and testing of data-intensive software systems

recommended or required reading

- Häberlein, T. (2016) Informatik: Eine praktische Einführung mit Bash und Python. 2. Auflage, De Gruyter Oldenbourg, Berlin (ISBN: 978-3110496864).
- Sommerville, I. (2015) Software Engineering, Global Edition. 10. Auflage, Pearson Education, London (ISBN: 978-1292096131).
- Williams, L.; Zimmermann, T. (2016) Perspectives on Data Science for Software Engineering. 1. Auflage, Morgan Kaufmann, Burlington (ISBN: 978-0128042069).
- Crawley, M. J. (2007) The R Book. 1. Auflage, John Wiley & Sons Ltd, Chichester (ISBN: 978-0-470-51024-7).
- Bowles, M. (2015) Machine Learning in Python: Essential Techniques for Predictive Analysis. 1. Auflage, John Wiley & Sons Ltd, Chichester (ISBN: 978-1118961742).
- Lutz, M (2013) Learning Python. 1. Auflage, O'Reilly Media, Farnham.

assessment methods and criteria

Final examination

language of instruction


number of ECTS credits allocated


course-hours-per-week (chw)


planned learning activities and teaching methods

-Lecture with discussion
-Group work

semester/trimester when the course unit is delivered


name of lecturer(s)

Huber Stefan , MA

year of study


recommended optional program components


course unit code


type of course unit


mode of delivery

In-class course

work placement(s)



Mitarbeiterfoto Karsten Böhm
Prof. (FH) Dipl.-Informatiker Karsten Böhm
Director of Studies
+43 5372 71819 133
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