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Data Engineering (Ma)

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#61397 / #4

SoSe 2022 - WiSe 2023/24

English

Data Engineering (Ma) (Data Engineering)

6

Hartmann, Timo

Benotet

Portfolioprüfung

English

Zugehörigkeit


Fakultät VI

Institut für Bauingenieurwesen

36312400 FG Systemtechnik baulicher Anlagen

Bauingenieurwesen

Kontakt


TIB 1-B 13

Hartmann, Timo

timo.hartmann@tu-berlin.de

http://civilsystems.tu-berlin.de

Lernergebnisse

More and more data is becoming available in the area of civil engineering that engineers need to make sense of and integrate into their design work. However, how to leverage the potential of these data is not always clear, highly disputed, and often largely misunderstood. Therefore, it is important that engineers are able to understand the potential of data to support engineering design clearly and can develop strong, but realistic value propositions around data driven engineering applications. At the end of this class, students will know about the basics of data engineering analysis - the art of asking the right questions for drawing insights from any of these data- sets to support sustainable civil engineering tasks. Moreover, they will also be able to develop clear value propositions of how to support complex engineering design tasks with data driven analyses. More specific, after finalizing the module, students will be able to apply the most common data mining and machine learning methods to data sets from the wider civil engineering field. Students will also have a good knowledge of how to assess the performance and quality of models and how to evaluate their applicability for prediction and sustainable decision making. Students will also develop first thoughts on the ethical ramifications of analyzing data with respect to for example, accounting for minorities that might not be well represented in a data set, but also with respect to potential biases that are introduced by the analysis methods. Above and beyond the Bachelor module that we offer, at the end of this Masters module, students will also be able to design new data-driven value propositions to improve civil engineering decision making or design work with respect to improving the ability of civil engineered products to support social and environmental needs.

Lehrinhalte

The module will teach the following methods: - data mining patterns and sequences - semantic text mining - regression analysis - correlation - Bayesian classification - decision trees and rule based classification - black-box methods - neural networks and support vector machines - unsupervised learning - evaluation of predictive models - data visualization: plotting and 3D - business planning around data driven engineering applications

Modulbestandteile

Compulsory area

Die folgenden Veranstaltungen sind für das Modul obligatorisch:

LehrveranstaltungenArtNummerTurnusSpracheSWS ISIS VVZ
Data EngineeringVL3631 L 9034SoSeen2
Data EngineeringPJ 3631 L 9035SoSeen2
ISIS-Kurssuche nach Veranstaltungen des Moduls

Arbeitsaufwand und Leistungspunkte

Data Engineering (VL):

AufwandbeschreibungMultiplikatorStundenGesamt
Präsenzzeit15.02.0h30.0h
Vor-/Nachbereitung15.04.0h60.0h
90.0h(~3 LP)

Data Engineering (PJ):

AufwandbeschreibungMultiplikatorStundenGesamt
Project work (weekly)15.06.0h90.0h
90.0h(~3 LP)
Der Aufwand des Moduls summiert sich zu 180.0 Stunden. Damit umfasst das Modul 6 Leistungspunkte.

Beschreibung der Lehr- und Lernformen

- Read and comment on selected texts to acquire the fundamental knowledge about data engineering techniques - Reflection and discussion of the techniques based on the texts; practice and application examples during lectures - Project work: application of the techniques on a number of selected data sets from the civil engineering domain

Voraussetzungen für die Teilnahme / Prüfung

Wünschenswerte Voraussetzungen für die Teilnahme an den Lehrveranstaltungen:

The module can be completed without any specific prior knowledge. Ideally students have followed Systemtechnik I+II or a similar module teaching an introduction to stochastic. Some basic skills with R will also be helpful. This is the Data Engineering module for Master students. Bachelor students need to enroll in the Bachelor Module. In the Master module students will additionally learn how to think about the added business value of data driven engineering applications.

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
comments on literature40schriftlichca. 10 texts
Final data analytics challenge (group work)20praktischreport of 5000 words
data engineering project assignments40praktischca. 7 assignments of around 900 words

Notenschlüssel

Notenschlüssel »Notenschlüssel 6: Fak III (2)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt90.0pt85.0pt80.0pt75.0pt70.0pt66.0pt62.0pt58.0pt54.0pt50.0pt

Prüfungsbeschreibung (Abschluss des Moduls)

comments on literature data engineering project assignments (weekly) The final data analytics challenge will require students to work in groups to analyze a real world data set under consideration of practical questions. Additionally, a strong value proposition for the results of the data analysis from the perspective of improving engineering decision making will be required. This value proposition should include sustainable and ethical considerations. Students will vote on the winner of this final challenge.

Dauer des Moduls

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

Dieses Modul kann in folgenden Semestern begonnen werden:
Sommersemester.

Maximale teilnehmende Personen

Dieses Modul ist nicht auf eine Anzahl Studierender begrenzt.

Anmeldeformalitäten

Qispos

Literaturhinweise, Skripte

Skript in Papierform

Verfügbarkeit:  nicht verfügbar

 

Skript in elektronischer Form

Verfügbarkeit:  nicht verfügbar

 

Literatur

Empfohlene Literatur
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Zugeordnete Studiengänge


Diese Modulversion wird in folgenden Studiengängen verwendet:

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

Studierende anderer Studiengänge können dieses Modul ohne Kapazitätsprüfung belegen.

Sonstiges

Keine Angabe