Energy Business FT
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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

course contents

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

assessment methods and criteria

Written examination

language of instruction


number of ECTS credits allocated


planned learning activities and teaching methods


semester/trimester when the course unit is delivered


name of lecturer(s)


year of study


recommended optional program components

Not specified

course unit code


type of course unit

Compulsory lecture

mode of delivery


work placement(s)

Not applicable