Software Development 1
Niveau
Master's program
Learning outcomes of the courses/module
The participants:
• can apply the basic concepts of software development (e.g. object orientation, functional programming, etc.) that are frequently used in the field of data science
• can apply the concepts they have learned in software development environments commonly used in data analysis (e.g. in Python)
• can name the common tools used in software development in data science
• can design basic applications in order to automate basic functionalities
• can implement the applications they have designed independently
• can apply the basic concepts of software development (e.g. object orientation, functional programming, etc.) that are frequently used in the field of data science
• can apply the concepts they have learned in software development environments commonly used in data analysis (e.g. in Python)
• can name the common tools used in software development in data science
• can design basic applications in order to automate basic functionalities
• can implement the applications they have designed independently
Prerequisites for the course
Students have prior knowledge in the field of information technology amounting to 6 ECTS and are therefore familiar with simple programming concepts (e.g. variables, branches, loops) as well as typical programming approaches (e.g. functional programming).
Course content
The following topics are discussed in the course:
- The process of software engineering and project management for data-intensive applications
- Programming paradigms for use in the field of data science
- Effective and efficient data structures for data-intensive applications
- Tools and software ecosystems for developing and testing data-intensive software systems
- The process of software engineering and project management for data-intensive applications
- Programming paradigms for use in the field of data science
- Effective and efficient data structures for data-intensive applications
- Tools and software ecosystems for developing and testing data-intensive software systems
Recommended specialist literature
- Lutz, M (2025): Learning Python, O'Reilly (ISBN: 978-1098171308)
- 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 Kaufmann, Burlington (ISBN: 978-0128042069)
- 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 Kaufmann, Burlington (ISBN: 978-0128042069)
Assessment methods and criteria
Written exam
Language
German
Number of ECTS credits awarded
6
Semester hours per week
Planned teaching and learning method
The following methods are used:
- Lecture with discussion
- Working through practice exercises
- Interactive workshop
- Lecture with discussion
- Working through practice exercises
- Interactive workshop
Semester/trimester in which the course/module is offered
1