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a.MA.IBE.GLPP.26HS (Grundlagen Programmierung Python) 
Modul: Grundlagen Programmierung Python
Diese Information wurde generiert am: 23.09.2026
Nr.
a.MA.IBE.GLPP.26HS
Bezeichnung
Grundlagen Programmierung Python
Veranstalter
A Institut Bauprozesse IBP
Credits
2

Beschreibung

Version: 1.0 gültig ab 01.08.2026
Modul- und Kursbeschrieb 
 
Modultitel Modulbezeichnung
Nr. / Code   
Bezeichnung  Grundlagen Python-Programmierung
Fachbereich  Departement Architektur, Gestaltung und Bauingenieurwesen 
Veranstalter  Studiengang MSc Integrierte Bau- und Energiesysteme 
Modultyp  Pflichtmodul 
Angebot / Durchführung  Frühlingssemester
ECTS-Credits  2
Workload (in Stunden)  60
Modulverantwortung  Juan Mahecha
Kurzbeschrieb Modul  This module introduces first‑semester MSc IBE students to the fundamentals of Python programming with a strong focus on practical tasks from building physics, energy and building technology, circular economy, and applied mathematics. Students learn to write, read, and adapt code in Jupyter notebooks, building confidence through guided exercises and problem‑oriented assignments using real datasets such as simulation outputs and sensor measurements. The module enables students - with little or no prior coding experience - to use programming as a tool for data analysis, visualization, and reproducible reporting in an interdisciplinary context.
Bemerkung   
 
 
Beschreibung   
Handlungskompetenzen Technical Competence
Students can use Python for typical IBE workflows such as basic calculations, time‑series processing, data cleaning, and visualization. They create transparent and reproducible notebook reports with clear documentation, units, and assumptions.
 
Methodological Competence
Students translate domain‑specific problems into algorithmic steps, apply key programming constructs (variables, data types, loops, conditions, functions), and debug errors using tracebacks and systematic testing. They learn responsible use of AI tools to generate, adapt, and verify code.
 
Social Competence
Students collaborate in small groups during coding sessions, alternating between writing, reviewing, and discussing code in an interdisciplinary environment.
 
Self‑Competence
Students learn to organize their work, document decisions, and reflect critically on AI‑assisted coding. They develop confidence in iterating, debugging, and improving their own solutions.
 
Leistungsziele und Lerninhalte  Learning Objective
Students should be able to confidently use Python to solve fundamental MSc IBE tasks by writing clear, reproducible notebook‑based code, processing real datasets, and creating meaningful visualizations that support engineering and design decisions. They can debug errors, verify results, and use AI tools responsibly to generate, adapt, and assess code within domain‑specific workflows.
 
Learning Content
The learning content focuses on a compact Python toolkit that applies directly to MSc IBE coursework and professional workflows. It includes the foundational elements of Python programming—syntax, data types, loops, conditions, functions—and essential methods for data handling and visualization, with an emphasis on reproducible notebook‑based reporting. Exercises draw on domain‑relevant contexts such as building physics, energy and building technology, circular construction, applied mathematics, and project work. Students work with realistic tasks including heat‑loss calculations, processing time‑series data, computing indicators like discomfort hours, and navigating real‑world data‑quality challenges. Throughout the module, strong attention is placed on clarity of assumptions, unit consistency, transparent documentation, and producing interpretable outputs that support sound engineering and design decisions.
Lehr- und Lernmethoden  Hands‑on, learning‑by‑doing module using interactive Jupyter notebooks. Sessions combine short inputs, live coding, guided exercises, and supervised problem‑solving based on authentic MSc IBE tasks. Debugging and verification are practiced throughout
Lehrmittel und -materialien  Curated Jupyter notebooks, small domain‑relevant datasets, reference sheets for Python and pandas, debugging guidelines, and visualization checklists
Leistungsnachweis  A cumulative notebook portfolio plus a presentation of a short mini‑project requiring data processing, visualization, and written interpretation in a reproducible notebook format
Bestehensbedingungen  Modulnote ≥ 4.00
 
Unterrichts- und Prüfungssprache  English 
Zulassung / Vorkenntnisse  Zuteilung zum Grundlagenmodul aufgrund der Vorbildung der/des Studierenden durch die Aufnahme-Kommission im Rahmen der Studienzulassung, in Absprache mit der Studienleitung.
 
Allgemeine Bemerkungen   
 
 
Modulverzeichnis Studiengang MSc Integrierte Bau- und Energiesysteme 2026/27 
Version 27.03.2026, Änderungen vorbehalten 
 
 
Kurs: Grundlagen Programmierung Python
Nr.
a.MA.IBE.GLPP.26HS.K
Bezeichnung
Grundlagen Programmierung Python

Hinweis

  • Für das Stichdatum 01.08.2026 ist kein Modulbeschreibungstext im System verfügbar.