Data Science & Intelligent Analytics PT
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Software development 2 Lab

level of course unit

Master's course

Learning outcomes of course unit

The following skills are developed in the course:

- Students can implement advanced application concepts independently.
- Students can develop advanced application concepts and bring them into an implementable form.

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

In the lab, the contents of the ILV “Software Development 2” 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 or required reading

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. (2007): The R Book (Ed. 1), 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

language of instruction

German

number of ECTS credits allocated

2.5

eLearning quota in percent

0

course-hours-per-week (chw)

1

planned learning activities and teaching methods

The following methods are used:

- Processing of exercises
- Interactive workshop
Data Engineering /ILV / Course no.: SDDE.1 / 1st semester / ECTS: 4

semester/trimester when the course unit is delivered

2

name of lecturer(s)

Prof. (FH) Dipl.-Informatiker Karsten Böhm

year of study

1

recommended optional program components

none

course unit code

SDDE.6

type of course unit

practice

mode of delivery

Compulsory

work placement(s)

none