Introduction of Data Science
level of course unit
Learning outcomes of course unit
Students are able to describe the content, results/application and modus operandi of data science and translate “questions” into requirements
within the context of data science. Students are familiar with the basic functions for processing mass data in Energy Business. They are able to apply the most important evaluation functions based on simple examples. They are also familiar with fundamental program concepts for the evaluation of large data volumes.
prerequisites and co-requisites
Internet Technologies, Energy-Efficient Buildings
Students learn about the functions of table-calculation software (e.g. Microsoft Excel) and databases for processing large data volumes. The focus is on fundamental methods such as min/max value, mean value, standard deviation, data-compression methods due to temporal scanning and mean-value formation as well as methods for data sorting by means of load profile and assorted duration curve.
Moreover, students learn about billing for temporally variable dynamic rates, service-progressive rates as well as the legal preconditions for billing (supplier/network).
recommended or required reading
Grus J.: Einführung in Data Science: Grundprinzipien der Datenanalyse mit Python, 1. Auflage, O’Reilly Media, 2016
Fasel D.; Meier A.: Big Data: Grundlagen, Systeme und Nutzungspotentiale, 1. Auflage, Springer Verlag, 2016
Runkler T.A.: Data Analytics: Models and Algorithms for Intelligent Data Analysis, 2. Auflage, Springer Verlag, 2016
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