The students are able to design modern architectures for processing big data.
They are familiar with different approaches and are able to test and operate suchlarge-scale processing systems.
They are able to provide the collected data by programming and configuring interfaces.

none

The module covers the following topics/contents:

• Data preparation (Data Wrangling)
• Interoperability and scripting (interfaces)
• Management and monitoring of data and data sources
• Cloud-based infrastructures of data-intensive software development
• Tools and Technologies
• Data and text mining
• Generation of data (data simulation)
• Dimension reduction techniques

Lecture script as provided in the course (required)
McKinney, W., (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly UK Ltd., 2nd edition
VanderPlas, J., (2016). Python Data Science Handbook: Essential Tools for working with Data. O'Reilly UK Ltd., 1st edition

Integrated course - teaching & discussion, demonstration, practical examples, home work

Immanent examination character:presentation, assignment reports, written/oral exam