Introduction to Python for Healthcare

Dozent/in	Ass.-Prof. Christian Frederik Baumgartner
Veranstaltungsart	Vorlesung
Code	HS261114
Semester	Herbstsemester 2026
Durchführender Fachbereich	Gesundheitswissenschaften
Studienstufe	Master
Termin/e	Mi, 16.09.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 23.09.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 30.09.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 07.10.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 14.10.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 21.10.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 28.10.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 04.11.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 11.11.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 18.11.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 25.11.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 02.12.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 09.12.2026, 12:30 - 16:00 Uhr, 3.B58 Mi, 16.12.2026, 12:30 - 16:00 Uhr, 3.B58 Di, 19.01.2027, 14:00 - 16:00 Uhr, HS 4 (Prüfung)
Umfang	4 Semesterwochenstunden
Inhalt	Python is a crucial programming language in the fields of data analysis and machine learning. It is an essential tool for anyone aspiring to work with cutting-edge machine learning and data science technologies. In this course, students will learn to: • Set up Python on their personal computers. • Understand and use basic control structures and data types in Python. • Utilize key Python libraries for data analysis and visualization. • Train simple machine learning models. The course includes both lectures and hands-on practical sessions, allowing students to apply their newly acquired skills in real-world scenarios.
Lernziele	- Understand the fundamentals of Python programming, including syntax, data types, and control structures. - Develop the ability to write Python scripts for basic data manipulation and analysis. - Gain proficiency in using Python libraries such as Pandas, Matplotlib, and Scikit-Learn for healthcare data analysis. - Apply Python programming skills to real-world healthcare problems and datasets.
Sprache	Englisch
Anmeldung	Moodle: https://elearning.hsm-unilu.ch/course/view.php?id=1035
Leistungsnachweis	Course assessment. The course will be assessed with three types of learning evaluations: - Completion of weekly hands-on exercises (20%) - Written exam on the basics of Python (50%) - Quality of work and presentation of the final project (30%) IMPORTANT: In order to earn credits and participate at the exam registration via Uni Portal within the exam registration period is MANDATORY. Further information: www.unilu.ch/en/study/courses-exams-regulations/health-sciences-and-medicine/exams/
Abschlussform / Credits	Exercises, exam, presentation / 6 Credits
Hörer-/innen	Nein
Kontakt	christian.baumgartner@unilu.ch
Material	The lecture slides, exercise sheets as well as the student presentations will be made available to all students via Moodle.
Literatur	The course is loosely based on the book “Python for Data Analysis” by Wes McKinney (3rd edition). However, students are not required to purchase the book.