Data Science & Intelligent Analytics PT
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Software development 1

level of course unit

1st semester: Master's course / 2nd semester: Master's course

Learning outcomes of course unit

The following skills are developed in the course:

- Students are familiar with the basic concepts of software development (e.g. object orientation, functional program-ming 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 are familiar with the common tools used in the field of software development in Data Science.
- Students can design basic 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: Students 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:

- The process of software engineering and project management for data-intensive applications
- Programming paradigms for use in data science
- 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

- 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. (2012): The R Book (Ed. 2), 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) Lukas Demetz, PhD

year of study


recommended optional program components


course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)