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#40693 / #5

Seit SoSe 2021

English

Data Science Project

9

Albayrak, Sahin

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Wirtschaftsinformatik und Quantitative Methoden

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

No information

Kontakt


TEL 14

Lommatzsch, Andreas

andreas.lommatzsch@tu-berlin.de

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.

Content

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

Pflichtteil:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWS ISIS VVZ
Data Science ProjectPJ0435 L 773SoSeEnglish4

Workload and Credit Points

Data Science Project (PJ):

Workload descriptionMultiplierHoursTotal
Appointments / presence15.06.0h90.0h
Design, implementation, evaluation15.06.0h90.0h
Preparation & follow-up work15.06.0h90.0h
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:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

NamePointsCategorieDuration/Extent
(Deliverable assessment) Milestone presentations20oral3 x 20 minutes
(Deliverable assessment) Project report30written> 10 pages
(Deliverable assessment) Project results50practical13 weeks

Grading scale

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt95.0pt90.0pt85.0pt80.0pt75.0pt70.0pt65.0pt60.0pt55.0pt50.0pt

Test description (Module completion)

No information

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Sommersemester.

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

 

Literature

Recommended literature
No recommended literature given

Assigned Degree Programs


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

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)124SoSe 2021WiSe 2024/25
Computer Science (Informatik) (M. Sc.)132SoSe 2021WiSe 2024/25
Elektrotechnik (M. Sc.)120SoSe 2021WiSe 2024/25
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)117SoSe 2021WiSe 2024/25
Wirtschaftsingenieurwesen (M. Sc.)18SoSe 2021WiSe 2024/25

Miscellaneous

No information