Data Science & Intelligent Analytics PT
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Software Development for Data Science 2

level of course unit

Master course

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

course contents

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

German

number of ECTS credits allocated

3

course-hours-per-week (chw)

2

planned learning activities and teaching methods

-Lecture with discussion
-Group work

semester/trimester when the course unit is delivered

2

name of lecturer(s)

Huber Lukas , MSc

year of study

1

recommended optional program components

N.A.

course unit code

SEW.3

type of course unit

ILV

mode of delivery

In-class course

work placement(s)

N.A.

Kontaktpersonen

Mitarbeiterfoto Karsten Böhm
Prof. (FH) Dipl.-Informatiker Karsten Böhm
Director of Studies
+43 5372 71819 133
Karsten.Boehmfh-kufstein.ac.at
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Förderer

Mit Unterstützung von Bund, Land und Europäischer Union: