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#41086 / #1

Seit SoSe 2023

English

Large-scale Data Engineering

12

Böhm, Matthias

Benotet

Portfolioprüfung

English

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34352900 FG Big Data Engineering

Keine Angabe

Kontakt


TEL 8-1

Damme, Patrick

patrick.damme@tu-berlin.de

Keine Angabe

Lernergebnisse

In this combined seminar/project module, students will learn how to critically read scientific publications, search for scientific literature on a given topic, write a high-quality scientific paper, create prototypes for specific projects, and give high-quality presentations on papers and prototypes. All of these aspects are covered with a special focus on the areas data engineering, data management, and machine learning systems. Together, the programming project and seminar are a solid foundation for subsequent master theses, both at a methodological level and specific topics.

Lehrinhalte

This module is comprised of a seminar and 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: A) Seminar on selected topics related to data and ML systems * 3 Lectures on scientific methods (structure of scientific papers, scientific reading and writing, experiments and reproducibility) * Reading selected papers and writing a 6-page summary paper (in LaTeX with provided template) * 20min oral presentation of summarized topic B) Programming projects on data and ML systems * Selection of a generic or seminar-topic-specific project * Discussion rounds on design, implementation, tests, and experiments * Prototype implementation, tests, and experiments * 15min oral presentation of the created prototype

Modulbestandteile

Compulsory area

Die folgenden Veranstaltungen sind für das Modul obligatorisch:

LehrveranstaltungenArtNummerTurnusSpracheSWS ISIS VVZ
Large-scale Data EngineeringPJWiSe/SoSeen4
Large-scale Data EngineeringSEMWiSe/SoSeen2

Arbeitsaufwand und Leistungspunkte

Large-scale Data Engineering (PJ):

AufwandbeschreibungMultiplikatorStundenGesamt
Attendance Discussion Rounds4.02.0h8.0h
Prototype Implementation1.0200.0h200.0h
Tests, Documentation, Experiments1.040.0h40.0h
Talk Preparation and Presentation1.020.0h20.0h
268.0h(~9 LP)

Large-scale Data Engineering (SEM):

AufwandbeschreibungMultiplikatorStundenGesamt
Attendance Lectures3.02.0h6.0h
Paper reading and writing1.065.0h65.0h
Talk Preparation and Presentation1.015.0h15.0h
86.0h(~3 LP)
Der Aufwand des Moduls summiert sich zu 354.0 Stunden. Damit umfasst das Modul 12 Leistungspunkte.

Beschreibung der Lehr- und Lernformen

Guided and self-organized reading of scientific papers, literature search, and writing of a summary paper. Guided and self-organized project work. In the beginning of the semester, students will hear presentations on reading scientific papers, finding related work, writing high-quality scientific papers, and giving a high-quality scientific presentation. Each student will be assigned an initial paper to read and understand. After that, students search for related work and write a short summary of the assigned paper, including some remarks on related work. In the end of the semester, each student gives a slide presentation in front of the group, followed by a discussion of the topic. Concurrently or in a subsequent semester, students also pick a programming project from a provided list, devise an initial design and then implement a prototype including documentation, tests, and relevant experiments. Theses programming projects are augmented by regular discussion rounds and a final presentation of the obtained results.

Voraussetzungen für die Teilnahme / Prüfung

Wünschenswerte Voraussetzungen für die Teilnahme an den Lehrveranstaltungen:

Completed basic courses on applied machine learning and data management

Verpflichtende Voraussetzungen für die Modulprüfungsanmeldung:

Dieses Modul hat keine Prüfungsvoraussetzungen.

Abschluss des Moduls

Benotung

Benotet

Prüfungsform

Portfolio examination

Art der Portfolioprüfung

100 Punkte insgesamt

Sprache(n)

English

Prüfungselemente

NamePunkteKategorieDauer/Umfang
(Deliverable assessment) Seminar Paper25schriftlich6 pages
(Deliverable assessment) Seminar Presentation15mündlich20 min
(Deliverable assessment) Project Implementation, Tests, Docs50praktischN/A
(Deliverable assessment) Project Presentation10mündlich15 min

Notenschlüssel

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

Prüfungsbeschreibung (Abschluss des Moduls)

The project can be conducted in teams of 1 to 3 students, but graded as a whole. All other parts of the portfolio exam are graded individually for every student.

Dauer des Moduls

Für Belegung und Abschluss des Moduls ist folgende Semesteranzahl veranschlagt:
1 Semester.

Dieses Modul kann in folgenden Semestern begonnen werden:
Winter- und Sommersemester.

Maximale teilnehmende Personen

Die maximale Teilnehmerzahl beträgt 20.

Anmeldeformalitäten

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

Literaturhinweise, Skripte

Skript in Papierform

Verfügbarkeit:  nicht verfügbar

 

Skript in elektronischer Form

Verfügbarkeit:  nicht verfügbar

 

Literatur

Empfohlene Literatur
Seminar-/project-specific literature will be discussed during the first lecture.

Zugeordnete Studiengänge


Diese Modulversion wird in folgenden Studiengängen verwendet:

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)125SoSe 2023SoSe 2025
Computer Science (Informatik) (M. Sc.)130SoSe 2023SoSe 2025
Elektrotechnik (M. Sc.)115SoSe 2023SoSe 2025
Informatik (B. Sc.)15SoSe 2023SoSe 2025
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)110SoSe 2023SoSe 2025
Technische Informatik (B. Sc.)15SoSe 2023SoSe 2025
Wirtschaftsinformatik (B. Sc.)210SoSe 2023SoSe 2025

Studierende anderer Studiengänge können dieses Modul ohne Kapazitätsprüfung belegen.

Sonstiges

Keine Angabe