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Data Science

Module name (EN): Data Science
Degree programme: Supply Chain Management, Master, ASPO 01.04.2017
Module code: MASCM-141
Hours per semester week / Teaching method: 4V (4 hours per week)
ECTS credits: 6
Semester: 1
Mandatory course: yes
Language of instruction:
German
Assessment:
Written exam and project (90 minutes / Weighting 1:1 / Can be repeated semesterly)

[updated 13.09.2018]
Curricular relevance:
MASCM-141 Supply Chain Management, Master, ASPO 01.04.2017, semester 1, mandatory course
Workload:
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:
- define and explain basic terms from the field of data science,
- illustrate interrelationships in the field of data science,
- understand and apply data modeling methods,
- apply and evaluate analytical methods of data mining,
- work in self-organized teams,
- consolidate and present work results,
- criticize and reflect upon project results.


[updated 13.09.2018]
Module content:
Introduction
-        Decisions, data, Business Intelligence (BI), Big Data
-        Data protection, data security
Data Analyses
-        ABC analysis
-        Cost-utility analysis (CUA)
-        Pivot analysis
Data Management
-        Entity Relationship Modell (ERM)
-        ACID principle
-        Online Transaction Processing (OLTP)
Data Warehouse
-        Dimensional modeling, star schema
-        Extract Transform Load (ETL) process
-        Online Analytical Processing (OLAP)
Data Mining
-        Cross Industry Standard Process (CRISP) for Data Mining
-        Supervised learning, cross-validation, leave-one-out
-        Algorithms, heuristics
Classification
-        Confusion matrix, Receiver Operating Curve (ROC)
-        Naive Bayes, decision trees, Artificial Neural Networks (ANN)
Cluster Analyses
- Hierarchical methods
- k-means
Association Rule Learning
- Support, confidence, lift
- Apriori algorithm
Prognoses
- Linear regression, time series analysis, smoothing
- Stochastic processes, autoregressive processes
Text Mining
- Stemming, Bag-of-Words (BOW) model, Part-of-Speech (PoS) tagging
- Frequency analysis, sentiment analysis, tag cloud
Big Data
- 5 Vs, NoSQL, In-Memory
- Hadoop, MapReduce, Spark


[updated 13.09.2018]
Teaching methods/Media:
Lecture with practical exercises on the PC using MS Excel, MS Access, ARIS 9.8, SAP BW 7.4 and KNIME Analytics. Project work in self-organized teams.

[updated 13.09.2018]
Recommended or required reading:
Recommended reading:
[Introduction]
Müller, R.M.; Lenz, H.-J.: Business Intelligence, Springer Verlag, Berlin, 2013.
[Data analyses]
Wies, P.: Excel 2013 Fortgeschrittene Techniken, Herdt Verlag, Bodenheim, 2013.
[Data management]
Schicker, E.: Datenbanken und SQL, 4. Auflage, Springer Vieweg, Wiesbaden, 2014.
Swoboda, B.; Buhlert, S.: Access 2013 Grundlagen für Datenbankentwickler, Herdt Verlag, Bodenheim, 2013.
[Data warehouse]
Bauer, A., Günzel, H.: Data-Warehouse-Systeme, 3. Aufl., Dpunkt-Verlag, Heidelberg, 2008.
Inmon, W. H.: Building the Data Warehouse, John Wiley & Sons, New York, 1996.
Kimball, R.: The Data Warehouse Toolkit, John Wiley & Sons, New York, 1996.
 
[Data mining]
Aggarwal, C.C.: Data Mining _ The Textbook, Springer Verlag, Cham, 2015.
Ester, M., Sander, J.: Knowledge Discovery in Databases, Springer Verlag, Berlin, 2000.
Han, J., Kamber, M., Pei, J.: Data Mining, 3. Aufl., Morgan Kaufmann, Waltham, 2012.
Runkler, T.A.: Data Mining, Vieweg + Teubner, Wiesbaden, 2010.
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, Pearson, Boston, 2006.
Witten, I.H., Eibe, F., Hall, M.A.: Data Mining, 3. Aufl., Morgan Kaufmann, Burlington, 2011.
 
[Big data]
Dorschel, J.: Praxishandbuch Big Data, Springer Gabler Fachmedien, Wiesbaden, 2015.


[updated 13.09.2018]
[Sat May  8 06:31:48 CEST 2021, CKEY=sds, BKEY=scm3, CID=MASCM-141, LANGUAGE=en, DATE=08.05.2021]