Software development 1
Niveau
1st semester: Master's course / 2nd semester: Master's course
Learning outcomes of the courses/module
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.
- 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 for the course
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 content
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
- 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 specialist literature
PRIMARY LITERATURE:
- Lutz, M (2013): Learning Python (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1449355739)
SECONDARY LITERATURE:
- 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)
- Lutz, M (2013): Learning Python (Ed. 1), O'Reilly Media, Farnham (ISBN: 978-1449355739)
SECONDARY LITERATURE:
- 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
German
Number of ECTS credits awarded
6
Share of e-learning in %
33
Semester hours per week
3.0
Planned teaching and learning method
The following methods are used:
- Lecture with discussion
- Processing of exercises
- Interactive workshop
- Lecture with discussion
- Processing of exercises
- Interactive workshop
Semester/trimester in which the course/module is offered
1
Name of lecturer
Prof. (FH) Lukas Demetz, PhD
Academic year
1
Key figure of the course/module
SDDE.2
Type of course/module
integrated lecture
Type of course
Compulsory
Internship(s)
none