Software Development Lab 1
Niveau
Master's course
Learning outcomes of the courses/module
The following skills are developed in the course:
- Students can implement basic application concepts independently.
- Students can develop basic application concepts and put them into an implementable form.
- Students can implement basic application concepts independently.
- Students can develop basic application concepts and put them into an implementable form.
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
In the lab, the contents of the ILV “Software Development 1” are advanced with the aid of practical exercises. The knowledge gained will be discussed in the group and thus allow a deep insight into the material and consolidation of the knowledge, which was theoretically dealt with in the ILV.
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
The following examination methods are used in the course:
- Project work
- term paper
- Project work
- term paper
Language
English
Number of ECTS credits awarded
2.5
Share of e-learning in %
30
Semester hours per week
1.0
Planned teaching and learning method
The following methods are used:
- Lecture with discussion
- Processing of exercises
- Lecture with discussion
- Processing of exercises
Semester/trimester in which the course/module is offered
1
Name of lecturer
Prof. (FH) Lukas Demetz, PhD, Prof. (FH) Dr. Lukas Huber
Academic year
1
Key figure of the course/module
SDDE.4
Type of course/module
practice
Type of course
Compulsory
Internship(s)
none