Business Analytics: Case Studies (Summer Term)
Business Analytics: Case Studies (Summer Term 2023)
Business analytics (BA) is a systematic approach that applies qualitative, quantitative, and statistical computational tools and methods to analyze data, gain insights, inform, and support decision-making. In this respect, methods from the field of machine learning (ML) have gained particular attention as they give computers the ability to perform tasks without being explicitly programmed to do so. Advances in ML enable the development of intelligent systems with human-like cognitive capacity that penetrate our business and personal life in every conceivable way. This is demonstrated by many diverse examples, such as fraud detection, predictive maintenance, credit scoring, next-best offer analysis, speech and image recognition, or natural language processing.
This course offers students, who already have a fundamental understanding of BA and ML, the opportunity to deepen their knowledge by developing data-driven processing pipelines and applying modern learning algorithms to solve real-world problems from research and practice. Students can either bring their own interesting BA/ML cases or are provided with exciting challenges from a predefined selection. Depending on the availability of open topics, there is also the chance to work on current cases from our collaboration partners.
The course has a strong practical focus and requires a high degree of self-initiative and dedication by the participants. At the beginning of the semester, some conceptual basics are repeated as a refresher. However, the in-depth investigation of relevant methods, procedures and principles required by the circumstances of the individual cases is done independently by the students in self-study. Students are encouraged to work (in groups) on the chosen projects to solve upcoming challenges in cooperation. To monitor the learning progress during the course, open consultation meetings are offered on a continuous basis, in which the applied approaches and procedures can be reflected in a participatory manner. The final results are presented and discussed at the end of the semester.
Please note that the number of participants for this course is limited. Below you can find further information on the registration process. After a successful course registration, more details on course updates, course materials, technical setup, and other information will be announced in StudOn.
Learning objectives and skills
- can translate domain-specific circumstances from real-world cases into well-defined problems that can be addressed with data-driven algorithmic approaches,
- have a deep understanding of data-driven processing pipelines,
- can implement modern methods and algorithms to solve real-world problems from research and practice,
- can compare and assess different algorithmic approaches and methodical procedures to evaluate their suitability,
- can document the achieved results in a scientific manner,
- work in groups and present their results together,
- develop skills in collaborative interaction with peers.
Course information
| Courses | Lecture: Business Analytics: Case Studies (1,5 SWS)
Project seminar: Business Analytics: Case Studies (2,5 SWS) |
| Credits | 5 ECTS |
| Lecturers | Prof. Dr. Patrick Zschech (Lecture)
Prof. Dr. Mathias Kraus (Exercise) |
| Time | Mixture of lecture and project seminar sessions over the entire semester.
Lecture: Wednesday 9:00 – 12:00 (4 sessions à 2 x 90 min) Project Seminar: Wednesday 9:00 – 12:00 (5 sessions à 2 x 90 min) For further information see schedule. |
| Location | The course will be held excusively online, consisting of virtual sessions via Zoom. For further information, please refer to the detailed schedule. |
| Recommended prerequisites | Profound knowledge in data analysis techniques, predictive modelling principles, statistics, and machine learning as taught, for example, in the Bachelor course “Business Analytics: Technologien, Methoden und Konzepte”.
Profound programming skills, preferably in Python. The number of participants is limited. Please register for the course via StudOn. |
| Integration in curriculum | First to fourth semester |
| Module compatibility | Master International Information Systems (from 2021/22): Module in the section Information Systems – Data & Knowledge
Master International Information Systems (from 2018/19): Module in the section Information Systems – Data & Knowledge (Elective) Master International Information Systems (from 2016/17): Module in the section Information Systems – Extension Courses (Elective) |
| Method of examination | Project report and presentations, partly in groups |
| Grading procedure | Project report (80%) and presentation (20%) |
| Module frequency | Each semester |
| Workload | Contact hours: 60 h
Independent study: 90 h |
| Module duration | 1 Semester |
| Teaching and examination language | English |
| (Recommended) reading | All relevant material will be provided during the course. |
Registration
The number of participants is limited and requires a registration via StudOn. The registration period is between March 11 – April 28, 2023. If you register before April 12 there is a higher chance of participation with early bird confirmation. In case of excess demand, participants will be selected by drawing lots. Firm confirmation of registration at the latest until April 26, 2023.
Schedule (lecture, exercise, project seminar)
Please note that the schedule is not yet finalized and we reserve the option for changes.
| CW | Organization / Lecture | Dates / Times | Project Seminar | Dates / Times |
|---|---|---|---|---|
| Course Registration | March 11 – April 28, 2023 | |||
|
16
|
Opening Session
|
April 19, 9:00-10:30
|
||
|
17
|
Project kick-off (Case selection) +
1) Intro data-driven pipelines and machine learning |
April 26, 9:00-12:00
|
||
|
18
|
Refreshment Python, Intro Colab, etc.
|
May 3, 9:00-12:00
|
||
|
19
|
2) Domain and data understanding +
3) Baseline models and evaluation |
May 10, 9:00-12:00
|
||
|
20
|
1. Consultation: Problem and data understanding
|
May 17, 9:00-12:00
|
||
|
21
|
4) Data-driven improvement +
5) Model-driven improvement |
May 24, 9:00-12:00
|
||
|
22
|
2. Consultation: Baseline models and evaluation
|
May 31, 9:00-12:00
|
||
|
23
|
6) Model assessment and interpretation +
7) Reporting of results and scientific writing |
June 7, 9:00-12:00
|
||
|
24
|
ECIS’2023
|
–
|
ECIS’2023
|
–
|
|
25
|
3. Consultation: Data-driven improvement
|
June 21, 9:00-12:00
|
||
|
26
|
Presentation preliminary results
|
June 28, 9:00-14:00
|
||
|
27
|
4. Consultation: Model-driven improvement
|
July 5, 9:00-12:00
|
||
|
28
|
||||
|
29
|
5. Consultation: Model assessment and interpretation
|
July 19, 9:00-12:00
|
||
|
31
|
Submission project seminar
|
Friday, August 4, 23:59
|
||
|
32
|
Final presentation
|
August 9, 9:00-14:00
|