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| Detailinformationen |
| Quellcurriculum |
Masterstudium Economic and Business Analytics 2025W |
| Lernergebnisse |
Kompetenzen |
| (*)Students are able to identify, model and solve basic business analytic problems, in particular problems and tasks in descriptive and predictive analytics. They are able to identify different areas of analytics and describe models to classify the readiness and capabilities of organization with respect to analytics and digital transformation.
Course Goals
The course aims to provide an overview of techniques in business analytics and on the digital transformation process. Students learn how digital technologies can be employed to generate business value. They are empowered to analyze business transformation processes and have basic knowledge on how they are supported by technology in current organizations. The course contains a practical part using the programming language R where students learn basic skills in processing data and extracting information relevant for business decisions by employing various techniques from data science. Case studies are presented throughout the course to illustrate the impact of the presented theory and techniques.
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| Fertigkeiten |
Kenntnisse |
(*)- Learning Outcome 3 (LO3): Apply basic concepts of descriptive and predictive analytics using R
- Learning Outcome 4 (LO4): Analyze results obtained by application of analytics algorithms
- Learning Outcome 5 (LO5): Identify different areas of analytics and describe models to classify the readiness and capabilities of organization with respect to analytics and digital transformation.
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(*)- Learning Outcome 1 (LO1): Understand the basics of methods and algorithms in descriptive and predictive analytics
- Learning Outcome 2 (LO2): Understand concepts and processes with regard to the digital transformation of organizations
Course Content
Course topics:
Part 1: Introduction to Analytics
- Data understanding and preparation
- Model evaluation and overfitting
- Classification and regression trees
- Classification using linear discriminant functions (including linear and logistic regression)
- Clustering
- Text mining
Part 2: Introduction to Digital Transformation
- Basic definition and ideas of business analytics and digital transformation
- Competing on analytics
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| Beurteilungskriterien |
(*)Regular homework exercises that must be submitted online via Moodle. The homework exercises are worth 20 points. Exam at the end of the semester with 80 points. At least 40 points on the exam are needed to pass the course. There is a possibility to repeat it in case of negative results or scheduling issues (retry exam). The exam consists of theoretical and practical questions. It lasts 90 minutes.
Final grades will be given as follows:
| Percent | Grade |
|---|
| 87,5 - 100 | 1 |
| 75 - 87 | 2 |
| 62,5 - 74,5 | 3 |
| 50 - 62,0 | 4 |
| 0 - 49,5 | 5 |
Both the homework exercises and the exam cover LO1, LO3, LO4. The exam additionally covers also LO2, LO5.
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| Lehrmethoden |
(*)The course uses a combination of different teaching methods in order to
- maximize the motivation and attention of the students.
- address the learning objectives in the didactically best way.
This includes the following
- Teacher-centred information inputs, supported by slides and literature
- Development of content in collaboration with the students on the computer and the black board
- Submission of homework exercises by students to ensure comprehension of the content
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)- Slides
- In-class programming exercises with solutions
- Reading material
- F. Provost, T. Fawcett: Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, O'Reilly, current edition
- T. H. Davenport, J. Harris: Competing on analytics - The new science of winning, Harvard Business Review Press, current edition
- D. Bertimas, A. O’Hair, W. Pulleyblank: The Analytics Edge, Dynamic Ideas LLC, current edition
- Pointers to additional literature
(All content is provided via Moodle)
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| Lehrinhalte wechselnd? |
Nein |
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