| 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 |
| 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 |
| Prüfung |
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. |