Modulhandbuch

Power and Data Engineering (PDE)

Energy Data Engineering

Teaching Methods Lecture
Learning objectives / competencies

The students have an understanding of Big Data Analytics. They know the different process phases in Big Data Analytics (collection, processing, cleansing, explorative statistics, modeling, evaluation and representation of data). They know algorithm applied in the different phases and are able to select suitable methods for practical problems.

Further, students know about real time Big Data analytics. They can clearly differentiate between terms like pattern recognition, machine learning, and deep learning.

 
Duration 1
SWS 8.0
Effort
Classes 120
Self-study / group work: 120
Workload 240
ECTS 8.0
Requirements for awarding credit points

two written exams 90 minutes
Credits and Grades

8 credit points

 
Responsible Person

Sascha Niro M.Sc.
Frequency Every 2nd sem.
Usability

Master PDE
Lectures

Energieinformatik 2

Type Vorlesung/Labor
Nr. M+V3050
SWS 4.0

Energieinformatik 1/Energy Data Engineering 1

Type Vorlesung/Labor
Nr. M+V3049
SWS 4.0
Lecture Content
  • Data Mining Terminology and concepts
  • Data Mining process models
  • Exploratory Data Analysis
  • Descriptive Statistics
  • Classification and Regression Models (Decision Trees, Random Forest, K-nearest neighbours, Naive Bayes, ...)
  • Model Evaluation and Comparison
  • Clustering
  • Linear Regression
  • Time Series Analysis
Literature

Reddy, T. Agami, Applied data analysis and modeling for energy engineers and scientists; Springer Science & Business Media, 2011

Witten, I. H. and Hall, M. A., Data mining: Practical machine learning tools and techniques, 3rd ed.
Burlington, MA: Morgan Kaufmann, 2011

Han, J., Kamber, M., and Pei, J., Data Mining: Concepts and Techniques, 3rd ed. Burlington: Elsevier Science, 2011

Hastie, T., Tibshirani, R., and Friedman, J. H., The elements of statistical learning: Data mining,
inference, and prediction, 2nd ed. Springer series in statistics. New York: Springer, 2009

Alpaydın, E., Maschinelles Lernen. München: Oldenbourg, 2008.