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#61041 / #3

SS 2019 - WS 2019/20

English

Data Engineering

6

Hartmann, Timo

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät VI

Institut für Bauingenieurwesen

36312400 FG Systemtechnik baulicher Anlagen

Bauingenieurwesen

Kontakt


No information

Hartmann, Timo

timo.hartmann@tu-berlin.de

Learning Outcomes

The module will teach basic data mining and machine learning techniques. 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. 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 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. The class will teach the basics of data engineering - the art of asking the right questions for drawing insights from any of these data- sets.

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 - unsupervised learning - data visualization: plotting and 3D

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Data EngineeringVorlesung3631 L 9034SoSeEnglish2
Data EngineeringProjekt 3631 L 9035SoSeEnglish2

Workload and Credit Points

Data Engineering (Vorlesung):

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

Data Engineering (Projekt):

Workload descriptionMultiplierHoursTotal
Project work (weekly)15.06.0h90.0h
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

NamePointsCategorieDuration/Extent
comments on literature40writtenca. 10 texts
Final data analytics challenge (group work)20practicalreport of 5000 words
data engineering project assignments40practicalca. 7 assignments of around 900 words

Grading scale

Notenschlüssel »Notenschlüssel 5: Fak III (1)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt95.0pt92.0pt89.0pt86.0pt83.0pt80.0pt77.0pt74.0pt71.0pt68.0pt

Test description (Module completion)

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. 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 reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

Recommended literature
No recommended literature given

Assigned Degree Programs


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

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte 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