Introduction to Python for Healthcare

Dozent/in	Ass.-Prof. Christian Frederik Baumgartner
Veranstaltungsart	Vorlesung
Code	HS251113
Semester	Herbstsemester 2025
Durchführender Fachbereich	Gesundheitswissenschaften
Studienstufe	Master
Termin/e	Mi, 17.09.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 24.09.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 01.10.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 08.10.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 15.10.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 22.10.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 29.10.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 05.11.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 12.11.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 19.11.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 26.11.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 03.12.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 10.12.2025, 14:15 - 18:00 Uhr, 4.A05 Mi, 17.12.2025, 14:15 - 18:00 Uhr, 4.A05 Di, 13.01.2026, 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	https://elearning.hsm-unilu.ch/course/view.php?id=936
Leistungsnachweis	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
Hinweise	Teaching methods: - Lectures: Weekly lectures to introduce and explain core concepts. - Weekly Hands-On Sessions: Practical exercises and coding sessions to apply concepts learned in lectures. - Project Work: A final project to synthesize learning and demonstrate practical application in a healthcare context. The final project will be conducted in the format of a machine learning competition where students will try to obtain the best possible prediction performance on a real-world medical dataset. Please note that the course will *not* be graded based on the ranking in this competition. - Presentations: Opportunities for students to present their final project and receive feedback.
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.
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.