htw saar Piktogramm QR-encoded URL
Back to Main Page Choose Module Version:
XML-Code

flag


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 / Can be repeated semesterly)

[updated 13.09.2018]
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:
- 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]
 
[Thu Nov 21 14:22:22 CET 2024, CKEY=sds, BKEY=scm3, CID=MASCM-141, LANGUAGE=en, DATE=21.11.2024]