Course - Health Data Analysis with AI - IT6117
Health Data Analysis with AI
New from the academic year 2026/2027
About
About the course
Course content
The course introduces applying artificial intelligence (AI) methods to structured and textual health data and information.
Learning outcome
- Knowledge. Learn to process and analyze health data and information. Apply AI methods from supervised, semi-supervised, or unsupervised learning to the health data. Understand in what situation these learning approaches are suited, how they work, their advantages and shortcomings, and the type of insight they can provide.
- Skills. Understand the data to be analyzed, how it needs to be preprocessed, and how domain knowledge within should be treated. Can choose suitable AI method(s) to analyze and extract insight from the data. Be able to understand what the AI method learned and how it can support real-world decisions. Can communicate the results to others, both experts and decision-makers.
Learning methods and activities
Lectures and laboratory exercises.
Curriculum, lectures, labs, exercises and exam will be partially given in English, and good English skills are required.
Compulsory assignments
- Excerises and participation in teaching periods
Further on evaluation
Portfolio assessment forms the basis for the final grade in the course, which is graded as pass/fail. The assessment is based on an individually completed project.
The portfolio consists of:
Project report (60%)
Code (40%)
The complete portfolio must be submitted in Inspera
Guidance takes place during gatherings and digitally on Piazza.
In the case of voluntary repetition, failure, or valid absence, the entire portfolio must be retaken in a semester with teaching.
Mandatory activities (exam requirements): exercises and participation in common gatherings.
Specific conditions
Admission to a programme of study is required:
Continuing Education in Technology (TKIMEEVU)
Recommended previous knowledge
- Basic programming skills in Python
- Statistics
- Clinical data and health records
Review your programming skills with these two online courses:
- Get started with Python, if you have no coding experience: https://www.kaggle.com/learn/intro-to-programming/
- Learn the most important language for data science: https://www.kaggle.com/learn/python/
Required previous knowledge
- IT6116 Health Data Analysis - Introduction, or IT6118 Health Data analysis with Python.
- Three years of education from a college/university, and
- At least two years of relevant professional practice within health sciences, science and/or informatics.
Other relevant education may provide grounds for education after individual assessment.
Course materials
Suggested textbooks:
- Yu, B., & Barter, R. L. (2024). Veridical data science: The practice of responsible data analysis and decision making. The MIT Press.
- McKinney, W. (2022). Python for data analysis: Data wrangling with pandas, NumPy, and Jupyter (Third edition). O’Reilly.
Supplementary material may be distributed during the course.
Access to your own portable computer is required.
Subject areas
- Build Acoustics
- Computer and Information Science
- Medical Computer Science
- Archival knowledge
Contact information
Course coordinator
Lecturers
Department with academic responsibility
Department of Computer Science