Moses - Data Analytics for Civil Engineers

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SoSe 2022 - WiSe 2023/24. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#61041 / #6

Validity:
SoSe 2022 - WiSe 2023/24

Default display language:
English

Module title:
Data Analytics for Civil Engineers

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 into their design work. Examples of such data sources are ranging from sensor based measurements of infrastructure 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 engineered products. 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. 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.

Content

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

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:

The module can be completed without any specific prior knowledge. Ideally students have followed Systemtechnik I or a similar module teaching an introduction to stochastic. Some basic skills with R will also be helpful.

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
Final data analytics challenge (group work)	20	practical	report of 5000 words
data engineering project assignments	40	practical	ca. 7 assignments of around 900 words

Grading scale

Notenschlüssel »Notenschlüssel 6: Fak III (2)«

Gesamtpunktzahl	1.0	1.3	1.7	2.0	2.3	2.7	3.0	3.3	3.7	4.0
100.0pt	90.0pt	85.0pt	80.0pt	75.0pt	70.0pt	66.0pt	62.0pt	58.0pt	54.0pt	50.0pt

Test description (Module completion)

comments on literature data engineering project assignments The final data analytics challenge will require students to work in groups to analyze a real world data set under consideration of practical questions. Students will vote on the winner of this final challenge.

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 Analytics for Civil Engineers