Advanced Business Analytics

Exam number: 6770

Semester: from 1st semester

Duration of the module: One semester

Form of the module (i.e. obligatory, elective etc.): Elective

Frequency of module offer: Winter semester 2016/2017, winter semester 2017/2018

Prerequisites: None

Applicability of module for other study programmes:
Obligatory or elective in other study programmes. For further information check regulations of the study programme.

Person responsible for module: N.N.

Name of the professor: Dr. Claus Gwiggner (Universität Hamburg, winter semester 2017/2018); Prof. Dr. Jan Fabian Ehmke (winter semester 2016/2017)

Language of teaching: English

ECTS-Credits (based on the workload): 6

Workload and its composition (self-study, contact time):
Contact time (Lecture, tutorial etc.): 60 h; self-study: 120 h

Contact hours (per week in semester): 4

Methods and duration of examination:
Written exam (90 min.), written seminar paper (15 pages) and presentation of the paper

Emphasis of the grade for the final grade: Please check regulations of the study programme

Aim of the module (expected learning outcomes and competencies to be acquired):
Subject related skills:
Students are able to derive relevant business knowledge through methods of Intelligent Data Analysis from large, complex databases. They know and are able to adapt and implement process models of intelligent data analysis for decision support of business problems. Based on the business problem at hand, students can select and apply the appropriate data analysis models, data mining methods and algorithms and derive plans of actions to improve the business problem. Students also have basic knowledge on fundamentals of simulation systems and know several fields of application for simulation systems.
Interdisciplinary skills:
- Independent working
- Analytical thinking
- Presentation skills
- IT-based problem solution in teams

Contents of the module:
- Process models of intelligent data analysis (e.g., CRISP or KDD)
- Data mining models and algorithms
- Introduction to data mining software (e.g., R, RapidMiner, KNIME)
- Selection of particular methods of explorative data analysis, descriptive and predictive modeling (e.g. cluster analysis, association analysis, classification)
- Analytics consulting project with real data

Teaching and learning methods:
Seminaristischer Unterricht, (Klein-)Gruppenarbeit, Einsatz von Beispielen aus der Praxis, Projektarbeit, Präsentationen, Diskussionen

Special features (e.g. percentage of online-work, practice, guest speaker, etc.):
This course consists of two parts. The first part (2/3) of the course will be devoted to the theory and methods of data science and will cover fundamentals of storage and analysis of huge amounts of business data. In the second part (1/3), students will apply this knowledge to real-world challenges (cases) with large amounts of real-world data by means of a fictitious analytics consulting project.

Literature (compulsory reading, recommended literature):
Provost, F.; Fawcett, T. (2013): Data Science for Business, O’Reilly
Berthold, M.; Borgelt, C.; Höppner, F.; Klawonn, F. (2011): Guide to Intelligent Data Analysis, Springer

Further information:
Registration in Moodle Viadrina required.