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Data Science

Module name (EN):
Name of module in study programme. It should be precise and clear.
Data Science
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Supply Chain Management, Master, ASPO 01.04.2017
Module code: MASCM-141
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P420-0336, P420-0337
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
4V (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
6
Semester: 1
Mandatory course: yes
Language of instruction:
German
Assessment:
Written exam and project (90 minutes / Weighting 1:1 / Repeated semesterly)

[updated 05.06.2025]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

MASCM-141 (P420-0336, P420-0337) Supply Chain Management, Master, ASPO 01.04.2017 , semester 1, mandatory course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 180 hours (equivalent to 6 ECTS credits).
There are therefore 135 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Stefan Selle
Lecturer:
Prof. Dr. Stefan Selle


[updated 26.04.2017]
Learning outcomes:
After successfully completing this module, students will be able to use suitable methods of data analysis to gain knowledge for decision-making in practical questions.
  
They will have fundamental knowledge of various types of machine learning: unsupervised learning, supervised learning, reinforcement learning.
  
Students will be able to apply the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology to carry out data analysis in a structured and systematic manner.
  
They will be familiar with different types of characteristics (e. g. nominal, ordinal, metric) and can preprocess data appropriately (e. g. by One Hot Encoding, normalization or standardization).
  
Students will be able to select appropriate decision-making procedures (e.g. regression or classification in the field of supervised learning) for specific problems.
  
Students will be able to implement the methods they have learned using a suitable tool (e.g., KNIME Analytics Platform), carry out parameter studies, and critically evaluate the results obtained using defined quality criteria.
  
Students will be able to prepare the insights gained from the data in a suitable manner (e.g., in the form of a visualization) and document them (e.g., in a project report or project diary) so that they can ultimately be presented to a selected audience (e.g., decision-makers within the company) in an understandable way.

[updated 05.06.2025]
Module content:
1.  Introduction
2.  Basics
3.  Data analysis process
4.  Preprocessing data
5.  5.3 Association rule learning
6.  Cluster analysis
7.  Classification
8.  Regression
9.  Ensemble learning
10. Artificial neural networks

[updated 05.06.2025]
Teaching methods/Media:
Inverted / flipped classroom with eLearning support (for example: LMS Moodle): Specially prepared documents (for example: lecture notes) / self-study media (for example: videos) on technical and methodological knowledge.
    
Lab course with exercises: Independent work on the PC to solve business-related tasks by applying the methods learned with the help of suitable tools (for example: KNIME Analytics Platform).
    
Project work: Case studies will be worked on in self-organized teams, and the results will be presented, discussed, and reflected upon (e.g., by keeping a project diary in the Mahara e-portfolio).

[updated 05.06.2025]
Recommended or required reading:
Udo Bankhofer und Jürgen Vogel: Datenanalyse und Statistik – Eine Einführung für Ökonomen im Bachelor, Gabler Springer Verlag, Wiesbaden, 2008.
  
Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosario Silipo: Guide to Intelligent Data Science – How to Intelligently Make Use of Real Data, 2nd edition, Springer, Berlin, 2020.
  
Uwe Haneke, Stephan Trahasch, Michael Zimmer, Carsten Felden: Data Science – Grundlagen, Architekturen und Anwendungen, dpunkt Verlag, Heidelberg, 2019.
  
Annalyn Ng und Kenneth Soo: Data Science – Was ist das eigentlich?! – Algorithmen des maschinellen Lernens verständlich erklärt, Springer Verlag, Berlin, 2018.
  
Forster Provost & Tom Fawcett: Data Science for Business. What you need to know about Data Mining and Data-Analytic Thinking, O’Reilly Verlag, Sebastopol, 2013.
  
Thomas A. Runkler: Data Mining – Methoden und Algorithmen intelligenter Datenanalyse, Vieweg+Teubner Verlag, Wiesbaden, 2010.
  
Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal: Data Mining – Practical Machine Learning Tools and Techniques, 4th edition, Morgan Kaufmann, Burlington, 2016.


[updated 05.06.2025]
 
[Mon Jun 30 21:26:12 CEST 2025, CKEY=sds, BKEY=scm3, CID=MASCM-141, LANGUAGE=en, DATE=30.06.2025]