Information on individual educational components (ECTS-Course descriptions) per semester

Degree programme: Master Sustainable Energy Systems
Type of degree: FH Master´s Degree Programme
Summer Semester 2022

Course unit title Data Science
Course unit code 072722020301
Language of instruction German
Type of course unit (compulsory, optional) Compulsory
Semester when the course unit is delivered Summer Semester 2022
Teaching hours per week 2
Year of study 2022
Number of ECTS credits allocated Second Cycle (Master)
Number of ECTS credits allocated 3
Name of lecturer(s) Elias EDER, Klaus RHEINBERGER

Prerequisites and co-requisites


Course content

Learning outcomes

The course provides the classical tools of data analysis. Statistical and methodical basics are explained, and the tools are demonstrated in a programming environment using examples.

  • Data Structures: Samples, Features, Target
  • Statistical basics: (Co-)variance, correlation, statistical relationship vs. causality
  • Cross Validation: learning and test data sets, fit and prognosis
  • Supervised Learning: Regression vs. Classification, k-Nearest Neighbors, Linear and Logistic Regression, Linear Support Vector Machines, (Ensembles of) Decision Trees
  • Unsupervised Learning: Clustering, Principal Component Analysis
  • Tools: Data transformations, dummies, feature selection, regularization, grid search

Planned learning activities and teaching methods
  • Lectures
  • Programmer exercises, programming projects
  • Coaching

Assessment methods and criteria
  • Delivery or presentation of the programming project
  • Oral final examination



Recommended or required reading
  • Guido, Sarah; Müller, Andreas C. (2016): Introduction to Machine Learning with Python: A Guide for Data Scientists. Sebastopol, CA: O’Reilly UK Ltd.
  • VanderPlas, Jake (2016): Python Data Science Handbook: Essential Tools for working with Data. Sebastopol, CA: O’Reilly UK Ltd.
  • Géron, Aurélien (2019): Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. 2nd ed. O’Reilly UK Ltd.
  • McKinney, Wes (2017): Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. 2nd edition. Sebastopol, California: O’Reilly UK Ltd.
  • Provost, Foster; Fawcett, Tom (2013): Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly and Associates.

Mode of delivery (face-to-face, distance learning)

Presence course. Students will be informed of the lecturer's attendance requirements before the start of the course.