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DTZ2025
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Datascience in healthcare
Period 5: from 13-4-2026 to 5-6-2026 (maandag 13 april 2026 tot vrijdag 5 juni 2026)
Co-requisites:
None
Coordinator:
Urovi, V.
ECTS credits:
11
Language of instruction:
English
Publication dates timetable/results in the Student Portal
Deadline publication timetable
The date on which the timetable of this module is available:
vrijdag 20 maart 2026
Deadline publication final result
The date on which the final grade of this module is available: dinsdag 23 juni 2026
Resit booking
Exam booking for a test in current academic year (resit)
You will be booked automatically for the resit in one of our resit periods. You may check our calenders to find out which modules can be retaken and when: https://intranet.maastrichtuniversity.nl/nl/fhml-studenten/studieverloop/wanneer-wat
As of one week before the resit test takes place, you can check in Student Portal if you are booked correctly: Student Portal > My Courses > More actions. The test will also be visible in your time table.
Exam booking for a test from a previous academic year (exam only)
All students who have not passed the test for this module in a previous academic year, will be booked automatically for the test during the regular block period. You will be enrolled in the new course in Canvas but not scheduled for a tutorial group and other educational activities.
If you do not wish to participate in this test at the end of the regular block period please de-register via askFHML.
Though great care has been taken to assure the accuracy of the information on fhmlweb, the FHML cannot be held responsible for possible printing errors, incomplete information, or misinterpretations. Additionally, the FHML reserves the right to make changes to this information.
Course information
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Description:
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EN:
The ability to manipulate and understand health-data is increasingly critical to discoveriesand innovations in healthcare. Data science is an emerging field that focuses on theprocesses and systems that enable us to extract knowledge or insight from data invarious forms and to translate it into action. With techniques such as machine learningand artificial intelligence being used for prevention of diseases, defining patient profilesand treatment interventions, Data Science plays an increasingly important role in healthscience.The Data Science in this DTZ module covers novel tools, methods and best practices of adata science project. The module is designed around the data science life-cycle and thetechniques and challenges into handling data analysis and management in healthcare.Students will gain knowledge on how to formulate a data research question and identifythe right dataset and methods needed to answer it. Students will become familiar withbasic data analysis algorithms, and will be able to visualize and interpret the results withregard to the data question/hypothesis. The module teaches basic programming skillsand how to apply them to perform the data science lifecycle: namely select, clean,analyze, visualize and interpret healthcare data. Each week, practical sessions will enablestudents to gain hands-on experience with data in healthcare topics.
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Goals:
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EN:
Knowledge and insight- differentiating the steps of the data science life cycle;
- formulating data research questions and identifying the right dataset and methods needed to answer these;
- knowledge of how basic data analysis and machine learning algorithms work;
- knowleege of the ways in which algorithms are validated and conclusions are drawn with regard to questions/hypotheses.
Application of knowledge and understanding- interpret the validity of the findings;
- apply visualization techniques to gain insight into the data and the models;
- identify hypotheses in a dataset;
- use a programming language to define models which test questions and
- apply the right methods within each step of the data science life cycle
Forming opinions- critically evaluate data science applications for use in the healthcare domain;
- perform critical thinking by discussing given literature and case studies;
- learn to use and extend their knowledge with respect to realistic data science problems
Specific attention will be paid to the communication skills needed to form a bridge-building professionals. By hosting specific meetings to present their knowledge, students will improve their communication skills as well as their professional attitude.The module is designed to enable interaction, feedback, and teamwork. Communication
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Key words:
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EN:
Data science, data analysis, python, data mining, healthcare
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Literature:
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❏ Book:Fundamentals of Clinical Data Science, Editors: P.Kubben, M.Dumontier,
A.Dekker, (Downloadable for free here http://www.clinicaldatasciencebook.com/ ) ❏ Book: Jake Vanderplas Python Data Science Handbook | Python Data Science
Handbook (Downloadable for free here jakevdp.github.io/PythonDataScienceHandbook/) ❏ Book: Data Science from Scratch - Joel Grus, second edition, Publisher(s): O'Reilly
Media, Inc. SBN: 9781492041122 (we use very few chapters of this book) ❏ Additional state of the art readings are included and specified in the student portal ❏ The lecture slides and recordings will be shared via the student portal
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Teaching methods:
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- Assignment(s)
- Work in workgroup(s)
- Lecture(s)
- Problem Based Learning
- Presentation(s)
- Skills
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Assessments methods:
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- Assignment
- Attendance
- Computertest
- Final paper
- Participaion
- Presentation
- Written exam
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This page was last modified on:dinsdag 18 maart 2025
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