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Data Science (BA_WING_5_4)
Department
- Bachelor's program Industrial Engineering & Management
Course unit code
- BA_WING_5_4
Level of course unit
- Bachelor
Year of study
- Fall 2024
Semester when the course unit is delivered
- 5
Number of ECTS credits allocated
- 2.0
Name of lecturer(s)
- Assoz. FH-Prof. Dipl.-Ing. Dr. techn. Ferdik Manuel
Learning outcomes of the course unit
- The students know the central topics and tasks of Data Science as well as its typical methods and tools. They will be able to lead Data Science projects and apply simple analysis methods themselves.
Mode of delivery
- face-to-face (Präsenzveranstaltung)
Prerequisites and co-requisites
- DBI, PRO, PQM, SCM, ERP, PRE, P&M, AUT
Recommended optional program components
- none
Course contents
- * Data Science Basics
- Terms
- Data (Classic, Big, Attributes, ...)
- DS Problems
* Basic Statistics
- Measures of Central Tendency
- Measures of Dispersion
- Visualization
* Data Acquisition
- Measuring Chain
* Preprocessing
- Data Cleaning
- Data Integration
- Data Reduciton
- Data Transformation
* Classic DS Methods
- Association Analysis
- Classification
Recommended or required reading
- Data Mining - Concepts and Techniques (J. Han, M. Kamber, J. Pei)
Data Science - Grundlagen, Methoden und Modelle der Statistik
(S. Scheid, S. Vogl)
Planned learning activities and teaching methods
- The course comprises an interactive mix of lectures, discussions and individual and group work.
Assessment methods and criteria
- Exam
- Course immanent examination
- Grading based on Participation, Homework and Final Written Exam
Language of instruction
- English
Work placement(s)
- none