Moses - Data Engineering

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SS 2017 - WS 2018/19. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#61041 / #2

Validity:
SS 2017 - WS 2018/19

Default display language:
English

Module title:
Data Engineering

Credits:
6

Responsible person:
Hartmann, Timo

Grading:
benotet

Type of exam:
Portfolioprüfung

Zugehörigkeit

Faculty:
Fakultät VI

Institute:
Institut für Bauingenieurwesen

Area of expertise:
36312400 FG Systemtechnik baulicher Anlagen

Prüfungsausschuss:
Bauingenieurwesen

Kontakt

Office:
No information

Contact person:
Hartmann, Timo

E-mail address:
timo.hartmann@tu-berlin.de

Website:

http://civilsystems.tu-berlin.de

Learning Outcomes

More and more data is becoming available in the area of civil engineering that engineers need to make sense of and integrate in their design work ranging from sensor based measurements of infrastrutcure and buildings, to measurements of the environment (weather, water flows), to openly available geographic data. Lately civil engineers even have started to analyse sentiment data users have left on social media platforms such as Facebook or Twitter about their experience while using civil structures. Data engineering is the art of asking the right questions on any of these given datasets whether small or large. The goal of this module is to provide students the basic skills to answer these questions. The module will teach basic data mining and machine learning techniques, both semantical and numerical. The module will also provide insights in data visualization techniques. Students will apply all methods and techniques on a number of data sets from the civil engineering domain using the statistical data analysis software R.

Content

The module will teach the following methods: - data mining patterns and sequences - semantic text mining - regression analysis - correlation - clustering - Bayesian classification - decision trees and rule based classification - black-box methods - neural networks and support vector machines - data visualization: plotting and 3D

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Data Engineering	VL	3631 L 9034	SoSe	English	2
Data Engineering	PJ	3631 L 9035	SoSe	English	2

Workload and Credit Points

Data Engineering (VL):

Workload description	Multiplier	Hours	Total
Präsenzzeit	15.0	2.0h	30.0h
Vor-/Nachbereitung	15.0	4.0h	60.0h
			90.0h(~3 LP)

Data Engineering (PJ):

Workload description	Multiplier	Hours	Total
Project work (weekly)	15.0	6.0h	90.0h
			90.0h(~3 LP)

The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

- 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

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Systemtechnik I or a similar module teaching an introduction to stochastic Basic programming knowledge (for example from Bauinformatik)

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

Name	Points	Categorie	Duration/Extent
comments on literature	40	written	ca. 10 texts
data engineering project assignments	60	practical	ca. 6 assignments of aroun 1200 words

Grading scale

Notenschlüssel »Notenschlüssel 5: Fak III (1)«

Gesamtpunktzahl	1.0	1.3	1.7	2.0	2.3	2.7	3.0	3.3	3.7	4.0
100.0pt	95.0pt	92.0pt	89.0pt	86.0pt	83.0pt	80.0pt	77.0pt	74.0pt	71.0pt	68.0pt

Test description (Module completion)

comments on literature 40 points data engineering project assignments (weekly) 60 points

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:
Sommersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

Qispos

Recommended literature
No recommended literature given

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

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

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

Students of other degrees can participate in this module without capacity testing.

Miscellaneous

No information

Data Engineering