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Programmierpraktikum Large-scale Data Engineering (unbenotet)

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#41094 / #1

SoSe 2023 - WiSe 2023/24

German

Programmierpraktikum Large-scale Data Engineering (unbenotet) (Programmierpraktikum Large-scale Data Engineering)
Project Large-scale Data Engineering (unbenotet) (Project Large-scale Data Engineering)

9

Böhm, Matthias

unbenotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34352900 FG Big Data Engineering

No information

Kontakt


TEL 8-1

Damme, Patrick

patrick.damme@tu-berlin.de

https://www.tu.berlin/en/dams

Learning Outcomes

In this project module, students will learn how to create prototypes for specific projects, and give high-quality presentations on these prototypes. These aspects are covered with a special focus on the areas data engineering, data management, and machine learning systems.

Content

This module is comprised of a programming project in the large context of big data engineering, i.e., topics related to scalable data and ML systems. In detail, the module is structured as follows: * Selection of a project * Discussion rounds on design, implementation, tests, and experiments * Prototype implementation, tests, and experiments * 15min oral presentation of the created prototype

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Large-scale Data EngineeringPJWiSe/SoSeEnglish4

Workload and Credit Points

Large-scale Data Engineering (PJ):

Workload descriptionMultiplierHoursTotal
Attendance Discussion Rounds4.02.0h8.0h
Prototype Implementation1.0202.0h202.0h
Tests, Documentation, Experiments1.040.0h40.0h
Talk Preparation and Presentation1.020.0h20.0h
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

Guided and self-organized project work. Students pick a programming project from a provided list, devise an initial design and then implement a prototype including documentation, tests, and relevant experiments. These programming projects are augmented by regular discussion rounds and a final presentation of the obtained results.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Completed basic courses on applied machine learning and data management

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

ungraded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

German/English

Test elements

NamePointsCategorieDuration/Extent
Implementation, Tests, Docs85practicalN/A
Presentation15oral15 min

Grading scale

At least 50 points in total needed to pass.

Test description (Module completion)

The project can be conducted in teams of 1 to 3 students, but graded as a whole.

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:
Winter- und Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 20.

Registration Procedures

Registration via email to Patrick Damme (patrick.damme@tu-berlin.de)

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

Recommended literature
Project-specific literature will be discussed during the first discussion round.

Assigned Degree Programs


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

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
This module is not used in any degree program.

Miscellaneous

Studiengänge: - Informatik (B.Sc.) - Technische Informatik (B.Sc.) - Wirtschaftsinformatik (B.Sc.)