Data Science & Intelligent Analytics PT
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Big Data Processing

level of course unit

Master course

Learning outcomes of course unit

Students are familiar with the special challenges associated with the storage and processing of large data volumes (5V model volume, variety, velocity, veracity, value). Furthermore, they are familiar with available options for countering these problems and able to independently develop and apply solutions with respect to a specific problem area.

prerequisites and co-requisites

Software Development for Data Science 1 and 2
Data Engineering for Data Science

course contents

Students are introduced to the basic properties of big data. Special emphasis is placed on handling this data while acquired knowledge is consolidated with examples. Suitable frameworks are introduced for solving big data problems and processed within the context of interactive workshops. Applicable examples:

-Apache Hadoop
-Apache Spark
-Apache Flink
-Apache Storm
-Apache Samza
-Apache Kafka

These frameworks are to be explained and used based on case examples. Cen-trally provided data labs can be accessed for this purpose.

recommended or required reading

- EMC Education Services (2015) Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. 1. Auflage, Wiley, Indianapolis (ISBN: 978-1118876138).
- O'Neil, C.; Schutt, R. (2013) Doing Data Science. Straight Talk from the Frontline. 1. Auflage, O'Reilly Media, Sebastopol (ISBN: 978-1449358655).
- Provost, F.; Fawcett, T. (2013) Data Science for Business: What you need to know about data mining and data-analytic thinking. 1. Auflage, O'Reilly Media, Sebastopol (ISBN: 978-1449361327).
- Narkhede, N.; Shapira, G.; Palino, T. (2017) Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale. 1. Auflage, O'Reilly Media, Farnham (ISBN: 978-1491936160).
- Jain, V. K. (2017) Big Data and Hadoop. 1. Auflage, Khanna Book Publishing, New Delhi (ISBN: 978-9382609131).
- Karau, H.; Warren, R. (2017) High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark. 1. Auflage, O'Reilly Media, Farnham (ISBN: 978-1491943205).

assessment methods and criteria

Final examination

language of instruction


number of ECTS credits allocated


course-hours-per-week (chw)


planned learning activities and teaching methods

-Lecture with discussion
-Group work
-Interactive workshop

semester/trimester when the course unit is delivered


name of lecturer(s)

Head of studies

year of study


recommended optional program components


course unit code


type of course unit


mode of delivery

In-class course

work placement(s)



Mitarbeiterfoto Karsten Böhm
Prof. (FH) Dipl.-Informatiker Karsten Böhm
Director of Studies
+43 5372 71819 133
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