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)
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
language of instruction
number of ECTS credits allocated
eLearning quota in percent
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
mode of delivery