Energy & Sustainability Management

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

Digitization in Energy & Sustainability Management (E)