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Digitization in Energy & Sustainability Management (E)
level of course unit
ConsolidationLearning outcomes of course unit
The students are able to:
• Describe contents, results/applications and working methods of Data Science
• Apply basic functions in the processing of mass data including evaluation functions
• Describe basic concepts of programs for evaluating large quantities of data and independently create simple program codes for evaluations
- Apply tools for the evaluation of data
• Describe contents, results/applications and working methods of Data Science
• Apply basic functions in the processing of mass data including evaluation functions
• Describe basic concepts of programs for evaluating large quantities of data and independently create simple program codes for evaluations
- Apply tools for the evaluation of data
prerequisites and co-requisites
Scientific and Empirical Methods (WIS.1)
course contents
• Evaluation of measurement data
• Fundamentals of time series analysis
• Data protection and data security
• Fundamentals of time series analysis
• Data protection and data security
recommended or required reading
• Grus, J., 2016. Einführung in Data Science: Grundprinzipien der Datenanalyse mit Python. Sebastopol: O’Reilly Media
• Fasel, D., A. Meier, 2016. Big Data: Grundlagen, Systeme und Nutzungspotentiale. Wiesbaden: Springer Verlag
• Runkler, T.A., 2016. Data Analytics: Models and Algorithms for Intelligent Data Analysis. 2. Auflage. Wiesbaden: Springer Verlag
• Fasel, D., A. Meier, 2016. Big Data: Grundlagen, Systeme und Nutzungspotentiale. Wiesbaden: Springer Verlag
• Runkler, T.A., 2016. Data Analytics: Models and Algorithms for Intelligent Data Analysis. 2. Auflage. Wiesbaden: Springer Verlag
assessment methods and criteria
Examination and portfolio
language of instruction
Englishnumber of ECTS credits allocated
4eLearning quota in percent
30course-hours-per-week (chw)
2planned learning activities and teaching methods
Blended Learning
