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WiSe 2020/21 - WiSe 2020/21

English

Research Oriented Course (ROC) on Data Science and Engineering Systems and Technologies

9

Markl, Volker

Benotet

Portfolioprüfung

English

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34351500 FG Datenbanksysteme und Informationsmanagement (DIMA)

Keine Angabe

Kontakt


EN 7

Markl, Volker

sekr@dima.tu-berlin.de

https://www.tu.berlin/dima

Lernergebnisse

Big Data (BD) and Machine Learning (ML) are key drivers underlying the current wave of innovation in artificial intelligence and data science. Indeed, these drivers have had a profound impact on both the economy and the sciences. This course targets research-oriented students who aim to pursue a PhD in Big Data Management or Data Science and Engineering Systems and Technologies. Upon completion of this course, students will have learned about contemporary research methodology, including scientific reading, writing, presenting, prototyping and experimental design, gained both theoretical and practical skills in data management and big data technologies, and be attuned to today’s major research challenges in scalable data management and processing. The course is designed to principally impart technical skills (20%), method skills (40%), systems skills (20%), and social skills (20%).

Lehrinhalte

The central focus of this module is on contemporary research methodology (CRM), data management technologies, and current research challenges. After an initial presentation on CRM, including scientific reading, writing, presenting, prototyping and experimental design, in subsequent lectures, students will read about foundational data management methods/technologies and offer a presentation, which will then be followed by an instructor led presentation addressing related advanced topics. Topics of discussion, include data storage and indexing, specification and compilation of data analysis programs, query optimization and self-tuning, adaptive methods, processing data science pipelines as well as responsible data management. In an accompanying lab component, students will prototype and evaluate discussed methods, technologies, and settings in a methodical and scientific way, and produce a scientific report on their findings.

Modulbestandteile

Compulsory area

Die folgenden Veranstaltungen sind für das Modul obligatorisch:

LehrveranstaltungenArtNummerTurnusSpracheSWS ISIS VVZ
Research Oriented Course (ROC) on Data Science and Engineering Systems and TechnologiesIV0434 L 479WiSeen6
ISIS-Kurssuche nach Veranstaltungen des Moduls

Arbeitsaufwand und Leistungspunkte

Research Oriented Course (ROC) on Data Science and Engineering Systems and Technologies (IV):

AufwandbeschreibungMultiplikatorStundenGesamt
Plenary Sessions15.04.0h60.0h
Lab Course (Programming)15.02.0h30.0h
Lab Course (System Setup)15.02.0h30.0h
Preparation (including Reading, Literature Search, and Presentations)15.02.0h30.0h
Lab Course (Experimental Setup)15.02.0h30.0h
Lab Course (Performance Evaluation)15.04.0h60.0h
Report15.02.0h30.0h
270.0h(~9 LP)
Der Aufwand des Moduls summiert sich zu 270.0 Stunden. Damit umfasst das Modul 9 Leistungspunkte.

Beschreibung der Lehr- und Lernformen

This Integrated Course (Integrierte Veranstaltung, IV) consists of: (i) lectures on key concepts, (ii) discussions, (iii) student lead presentations (including literature search), and (iv) a systems research project including (1) system setup, (2) prototyping, (3) experimental design, and (4) performance evaluation as well as (v) creating a presentation and report on the findings. Active participation and contributions to all parts of this course are essential.

Voraussetzungen für die Teilnahme / Prüfung

Wünschenswerte Voraussetzungen für die Teilnahme an den Lehrveranstaltungen:

Computer science topics addressed in TU Berlin modules in the Bachelor’s curriculum, particularly, both ISDA (Information Systems and Data Analysis) and DBPRA (Practical Database Systems Lab) or their equivalents, as well as good programming skills in C, Java, and SQL are all required. Additionally, an undergraduate course in linear algebra, probability, and statistics. Knowledge of master's level coursework in database technology (DBT) and advanced information management (AIM) is necessary. This course will be offered in English. Thus, fluency in English is also required.

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
Technology Presentation (Deliverable Assessment)20mündlich30 min. / about 30 slides
Experimentation Presentation (Deliverable Assessment)20mündlich30 min. / about 30 slides
Written Mid-term Test/Quiz (Examination)20schriftlichmax 75 minutes
Final Report (Deliverable Assessment)40schriftlichabout 30 pages

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 portfolio exam (worth 100 points) is comprised of four parts: (i) technology presentation (20 points), (ii) a quiz on database technology and research methodology (30 points), (iii) performance evaluation presentation ,and (iv) a final report (30 points). The final grade will be computed according to the Grading Table 2 of Faculty IV, according to German law, § 47 (2) AllgStuPO TU Berlin.

Dauer des Moduls

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

Dieses Modul kann in folgenden Semestern begonnen werden:
Wintersemester.

Maximale teilnehmende Personen

Die maximale Teilnehmerzahl beträgt 8.

Anmeldeformalitäten

Prior to the start of the first lecture, students must register themselves in the DIMA Course Registration Tool: http://www.dima.tu-berlin.de/. In addition, students must register both in ISIS (the course organization tool) -and- QISPOS (the TU Berlin Examination Management Tool) within the first six weeks of the current semester.

Literaturhinweise, Skripte

Skript in Papierform

Verfügbarkeit:  nicht verfügbar

 

Skript in elektronischer Form

Verfügbarkeit:  nicht verfügbar

 

Literatur

Empfohlene Literatur
Readings in Database Systems, 5th Edition, Peter Bailis, Joseph M. Hellerstein, Michael Stonebraker, editors, http://www.redbook.io/
Various Research Papers, made available during the first lecture
Mining of Massive Datasets, J. Leskovec, A. Rajaraman, and J. D. Ullman, Cambridge University Press, 2014 (Freely Available Book: infolab.stanford.edu/~ullman/mmds/book.pdf).
Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten and Eibe Frank, Morgan Kaufmann, 2011.
Hadoop: The Definitive Guide (4th Edition), Tom White, O’Reilly Media, 2015.
Supplementary reading material may be assigned to complement course lectures.

Zugeordnete Studiengänge


Diese Modulversion wird in folgenden Studiengängen verwendet:

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Dieses Modul findet in keinem Studiengang Verwendung.

Sonstiges

This course targets research-oriented Bachelor’s and Master’s students interested in focusing on Database Systems and Information Management in Computer Science (Major: System Engineering), Computer Engineering (Major: Information Systems and Software Engineering), and Industrial Engineering, as well as students pursuing the Data Science and Engineering Master’s Track.