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Data Science Project



#40693 / #5

Seit SoSe 2021

Fakultät IV

TEL 14

Institut für Wirtschaftsinformatik und Quantitative Methoden

34361200 FG Agententechnologien in betrieblichen Anwendungen und der Telekommunikation (AOT)

Albayrak, Sahin

Lommatzsch, Andreas

POS-Nummer PORD-Nummer Modultitel
2346668 38071 Semantic Search Projekt
2350215 43051 Data Science Project

Learning Outcomes

Graduates of the module have gained practical experience in dealing with complex data science problems. They are able to formulate scientific questions and systematically validate them using empirical evaluations on datasets. Graduates can formulate and present the results of the examination according to scientific standards. They are able to pursue independent research approaches and to criticize foreign scientific texts.


In this course, a data science problem from a scientific or industrial domain is treated in a practice-oriented manner. The aim of the project is to formulate and validate a scientific question. For this purpose, own approaches to solving the problem are developed and implemented. The approach and the results of the validation are documented in a scientific report and presented in a presentation. Students deal in particular with the following topics: - Machine Learning - Feature Extraction - Deep Learning - Data Preprocessing - Model Evaluation - Machine Learning Libraries - Scientific Writing

Module Components


All Courses are mandatory.

Course Name Type Number Cycle Language SWS
Data Science Project PJ 0435 L 773 SS English 4

Workload and Credit Points

Data Science Project (PJ):

Workload description Multiplier Hours Total
Appointments / presence 15.0 6.0h 90.0h
Design, implementation, evaluation 15.0 6.0h 90.0h
Preparation & follow-up work 15.0 6.0h 90.0h
270.0h(~9 LP)
The Workload of the module sums up to 270.0 Hours. Therefore the module contains 9 Credits.

Description of Teaching and Learning Methods

Project work in groups, milestone planning, presentations, report.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Basic data science knowledge

Mandatory requirements for the module test application:

No information

Module completion



Type of exam

Portfolio examination

Type of portfolio examination

100 points in total



Test elements

Name Points Categorie Duration/Extent
(Deliverable assessment) Milestone presentations 20 oral 3 x 20 minutes
(Deliverable assessment) Project report 30 written > 10 pages
(Deliverable assessment) Project results 50 practical 13 weeks

Grading scale

Test description (Module completion)

No information

Duration of the Module

This module can be completed in one semester.

Maximum Number of Participants

The maximum capacity of students is 20.

Registration Procedures

Qispos/SAP/examination office. Additionally, a registration on the ISIS course page is mandatory.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

Electronical lecture notes

Availability:  available


Recommended literature
No recommended literature given.

Assigned Degree Programs

This module is used in the following Degree Programs (new System):

This moduleversion is used in the following modulelists:


No information