Zur Modulseite PDF generieren

#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.)130SoSe 2023WiSe 2025/26
Computer Science (Informatik) (M. Sc.)136SoSe 2023WiSe 2025/26
Elektrotechnik (M. Sc.)118SoSe 2023WiSe 2025/26
Informatik (B. Sc.)16SoSe 2023WiSe 2025/26
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)112SoSe 2023WiSe 2025/26
Technische Informatik (B. Sc.)16SoSe 2023WiSe 2025/26
Wirtschaftsinformatik (B. Sc.)212SoSe 2023WiSe 2025/26

Studierende anderer Studiengänge können dieses Modul ohne Kapazitätsprüfung belegen.

Sonstiges

Keine Angabe