Data Science & Intelligent Analytics PT
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Software development 2

level of course unit

Master's course

Learning outcomes of course unit

The following skills are developed in the course:

- Students are familiar with advanced concepts of software development (e.g. pipelines, testing, etc.) which are frequently applied in the field of data science.
- Students are familiar with the application of the concepts developed in frequently-used software development environments in the field of data analysis (e.g. in Python, MATLAB or R).
- Students can design advanced applications to automate basic functionalities.
- Students can implement designed applications independently.

prerequisites and co-requisites

1st semester: Students will have previous knowledge in the field of information technologies to the extent of 6 ECTS and therefore know the concept of the relational database and can read simple SQL queries. / 1st semester: Stu-dents will have previous knowledge in the field of information technologies to the extent of 6 ECTS and therefore know simple programming concepts (e.g. variables, branches, loops) as well as typical programming approaches (e.g. functional programming). / 2nd semester: SDDE.A1 module examination (Software Development 1)

course contents

The following content is discussed in the course:

- Architecture models for data-driven software development and systems
- Integration models and paradigms for implementing complex, process-oriented software ecosystems for analytical and data-driven systems
- Application of proven design patterns for data-driven applications
- Design and implementation of efficient and scalable software systems for data-driven applications
- Testing of software applications (e.g. unit tests, integration tests, etc.)

recommended or required reading

- Lutz, M (2013): Learning Python (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1449355739)

- Sommerville, I. (2015): Software Engineering, Global Edition (Ed. 10), Pearson Education, London (ISBN: 978-1292096131)
- Williams, L.; Zimmermann, T. (2016): Perspectives on Data Science for Software Engineering (Ed. 1), Morgan Kauf-mann, Burlington (ISBN: 978-0128042069)
- Crawley, M. J. (2007): The R Book (Ed. 1), John Wiley and Sons Ltd, Chichester (ISBN: 978-0-470-51024-7

assessment methods and criteria

Written exam

language of instruction


number of ECTS credits allocated


eLearning quota in percent


course-hours-per-week (chw)


planned learning activities and teaching methods

The following methods are used:

- Lecture with discussion
- Processing of exercises
- Interactive workshop

semester/trimester when the course unit is delivered


name of lecturer(s)

Prof. (FH) Dipl.-Informatiker Karsten Böhm

year of study


recommended optional program components


course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)