Digitization in Energy & Sustainability Management (E)
Niveau
Consolidation
Learning outcomes of the courses/module
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
Prerequisites for the course
Scientific and Empirical Methods (WIS.1)
Course content
• Basic programming knowledge for data preparation
• Analysis and presentation of information from data sets
Recommended specialist literature
• 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
Assessment methods and criteria
Examination and portfolio
Language
English
Number of ECTS credits awarded
4
Share of e-learning in %
30
Semester hours per week
2.0
Planned teaching and learning method
Blended Learning
Semester/trimester in which the course/module is offered
2
Name of lecturer
Asc. Prof. (FH) Dipl.-Ing. Christian Huber
Academic year
1
Key figure of the course/module
DIT
Type of course/module
integrated lecture
Type of course
Compulsory
Internship(s)
none