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Informatik 5
Information Systems
Prof. Dr. M. Jarke
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You are here: Home Teaching WS 17/18 Data Visualisation and Analytics

Data Visualisation and Analytics

Type Weekly Practical course (basic level)
Term WS 17/18
Mentor(s)
Assistant(s)
  • Kathrin Gunkelmann
  • Lara Quack
  • Mirko Seithe

This course provides participants with a comprehensive and versatile toolbox of data visualisation and analysis methods, which can be transferred to a vast number of applications.

 

Nowadays many businesses produce and process huge amounts of data, for example in the manufacturing industry by deploying the industrial data space or the medical data space used by the health industry. Resulting data sets allow real-world questions to be answered in a full quantitative stance which used to be hard or even impossible to answer previously: has the internet had country-specific impacts on governance, has government spending on infrastructure had a tangible effect on private mobility and what decisions during a car's development cycle turn out to be most profitable in the end?

Methods, processes and tools for data analysis and visualization are therefore becoming key ingredients in producing knowledge necessary and instrumental for decision processes.

This lab course teaches methods and tools for analyzing and visualizing data sets. It conveys the technical foundation and gives ample opportunities for practicing data analysis and visualization on real-world data. Innovation of analysis is founded in method orchestration.

At the beginning of the course, the key concepts of data analysis and visualization are taught using the example R as free open-source software package. This part will focus on cluster analysis, the linear regression model and text analysis as well as on creating interactive visualization tools and web applets. Supplementary techniques, which can be covered offhand, include the visualization of maps and graphs, image analysis, combining multiple data sets and more. All of these methods and techniques are practiced using applied real-world data sets.

In the second part, students combine these methods to answer increasingly more complex questions. The heart of this course consists of group specific applied projects, in which existing data sets are analyzed to answer questions from the operative environment.

Apart from acquiring the functional and technical foundations, students will experience the operative potential of data analysis and its application in analysis processes. In effect, this data science course is turning into a living lab.

 

 

Course dates

Meeting typeDate/TimeRoomStarting on
Weekly meeting Friday, 09:30 - 14:30 Fraunhofer FIT, Sankt Ausgustin 20. October 2017
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