Software Development for Data Science 2
level of course unit
Learning outcomes of course unit
Graduates consolidate software development concepts that are frequently used in data science. Special emphasis is on integration in other software systems while the usage of web-based approaches constitutes a focal point.
Another aspect is knowledge of design patterns that are frequently used in data-intensive applications or that are relevant for the structure of efficient data-driven application architectures. The course content is rounded off with expertise in effi-cient software systems that provide data scaling for the data to be analyzed even in case of increasing requirements.
prerequisites and co-requisites
Software Development for Data Science 1
Knowledge of software development for data-driven applications is consolidated in the course. The three topical fields of software architecture, system integration and sample-based design form the core of observations.
The teaching content encompasses the following topics:
-Architecture models for data-driven software development and systems
-Integration models and paradigms for the implementation of complex, process-oriented software ecosystems for analytical and data-driven systems
-Application of proven design patterns for data-driven applications
-Conceptualization and implementation of efficient and scalable software systems for data-driven applications
recommended or required reading
- Häberlein, T. (2016) Informatik: Eine praktische Einführung mit Bash und Python. 2. Auflage, De Gruyter Oldenbourg, Berlin (ISBN: 978-3110496864).
- Sommerville, I. (2015) Software Engineering, Global Edition. 10. Auflage, Pearson Education, London (ISBN: 978-1292096131).
- Williams, L.; Zimmermann, T. (2016) Perspectives on Data Science for Software Engineering. 1. Auflage, Morgan Kaufmann, Burlington (ISBN: 978-0128042069).
- Crawley, M. J. (2007) The R Book. 1. Auflage, John Wiley & Sons Ltd, Chichester (ISBN: 978-0-470-51024-7).
- Bowles, M. (2015) Machine Learning in Python: Essential Techniques for Predictive Analysis. 1. Auflage, John Wiley & Sons Ltd, Chichester (ISBN: 978-1118961742).
- Lutz, M (2013) Learning Python. 1. Auflage, O'Reilly Media, Farnham.
assessment methods and criteria
– Final examination
language of instruction
number of ECTS credits allocated
planned learning activities and teaching methods
-Lecture with discussion
semester/trimester when the course unit is delivered
name of lecturer(s)
Huber Lukas , MSc
year of study
recommended optional program components
course unit code
type of course unit
mode of delivery