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Introduction to Data Science for Multidisciplinary Engineers

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#51126 / #1

Seit WiSe 2024/25

English

Introduction to Data Science for Multidisciplinary Engineers

6

Cominola, Andrea

Benotet

Schriftliche Prüfung

English

Zugehörigkeit


Fakultät V

Institut für Strömungsmechanik und Technische Akustik

35312000 FG Smart Water Networks (SWN)

Physikalische Ingenieurwissenschaft

Kontakt


FSD

Cominola, Andrea

andrea.cominola@tu-berlin.de

https://www.tu.berlin/en/swn

Lernergebnisse

In this course, students will gain basic introductory knowledge and skills in data science, focusing on its application in engineering problems. The students will first learn the theoretical fundamentals of data analysis and statistical modelling, along with basic concepts on both supervised and unsupervised learning, and data visualization techniques. This introductory course prepares students for more advanced studies in data-driven engineering fields, mathematical modelling, Machine Learning, and Artificial Intelligence. The students will learn how to use different data science techniques through guided activities and applications inspired by relevant - yet simplified - engineering problems. They will learn how to program different data analysis tools in Python (starting from the basics of writing Python functions and defining classes), fostering their proficiency both in using data science tools and programming. They will learn how to properly visualize and interpret data.

Lehrinhalte

Data science is a growing field both in research and practice. Being a cross-disciplinary science, it is becoming increasingly relevant in several fields of applications and engineering sectors. This course will provide the students with a solid initial basis on the main data science techniques. The course content will cover the following core topics: - Introduction to Data Science - Data Collection and Processing (data cleaning and preprocessing) - Exploratory Analysis (descriptive statistics, basic data visualization, trend analysis) - Statistical Analysis and Testing - Plotting and data visualization techniques - Introduction to supervised learning concepts (basic regression and classification) - Introduction to unsupervised learning concepts (basics of clustering and dimensionality reduction) During the software-based guided lab activities, the students will be actively guided to use data science tools in practice and develop solutions for a set of different problems in data science. These activities will rely on open available datasets and introductory examples. Jupyter notebooks and Python will be used in the lab activities. Assessment includes a written exam on theory and applications.

Modulbestandteile

Compulsory area

Die folgenden Veranstaltungen sind für das Modul obligatorisch:

LehrveranstaltungenArtNummerTurnusSpracheSWS ISIS VVZ
Introduction to Data Science for Multidisciplinary EngineersIVWiSeen4
ISIS-Kurssuche nach Veranstaltungen des Moduls

Arbeitsaufwand und Leistungspunkte

Introduction to Data Science for Multidisciplinary Engineers (IV):

AufwandbeschreibungMultiplikatorStundenGesamt
Attendance15.04.0h60.0h
Pre/post processing15.04.0h60.0h
Exam preparation1.058.0h58.0h
Exam1.02.0h2.0h
180.0h(~6 LP)
Der Aufwand des Moduls summiert sich zu 180.0 Stunden. Damit umfasst das Modul 6 Leistungspunkte.

Beschreibung der Lehr- und Lernformen

The lectures will be mainly in a frontal presentations format and in person. Slides will be made available to students. The course includes tutoring sessions to guide the student through different data science tasks, mostly in the form of guided software-based activities. Guided activities may be complemented by open tasks, to be tackled by the students with guidance and feedback from the tutors. A final written exam will be completed in person at the end of the course. Further instructions about the final schedule, lecture rooms, and on how to get access to the lectures and exercise materials for the course will be communicated to the registered students via the e-learning ISIS platform.

Voraussetzungen für die Teilnahme / Prüfung

Wünschenswerte Voraussetzungen für die Teilnahme an den Lehrveranstaltungen:

Basic knowledge in concepts of mathematics and basic stochastic/probability theory is required. Basic programming knowledge and previous experience with Python/Matlab/R is a plus for the lab activities, but not mandatory. Guided practical activities will be performed in the course using Python and Jupyter Notebooks.

Verpflichtende Voraussetzungen für die Modulprüfungsanmeldung:

Dieses Modul hat keine Prüfungsvoraussetzungen.

Abschluss des Moduls

Benotung

Benotet

Prüfungsform

Written exam

Sprache(n)

English

Dauer/Umfang

2 hours

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

Dieses Modul ist nicht auf eine Anzahl Studierender begrenzt.

Anmeldeformalitäten

Course registration via Prüfungsamt.

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
Maschinenbau (M. Sc.)12WiSe 2024/25SoSe 2025
Physikalische Ingenieurwissenschaft (B. Sc.)24WiSe 2024/25SoSe 2025

Studierende anderer Studiengänge können dieses Modul ohne Kapazitätsprüfung belegen.

Sonstiges

Keine Angabe