Graduate School of health and Medical Sciences

Python Part 2: Introduction to Data Analysis in Python
Provider: Faculty of Health and Medical Sciences

Activity no.: 3952-25-00-00	There are 24 available seats
	Enrollment deadline: 18/04/2025

Date and time	19.05.2025, at: 09:00 - 21.05.2025, at: 16:00
Regular seats	25
Course fee	4,440.00 kr.
Lecturers	Anders Krogh
ECTS credits	2.10

Contact person	HeaDS Administration E-mail address: heads-admin@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration E-mail address: phdkursus@sund.ku.dk

Aim and content This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to the waiting list. The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee. Learning objectives A student who has met the objectives of the course will be able to: 1. Understand and use advanced concepts of the python language such as user-defined functions, classes and modules. 2. Perform modelling with the data science library scikit-learn. 3. Explain the use of supervised and unsupervised learning techniques and how to choose a model. 4. Create customized visualizations with the seaborn and matplotlib libraries. 5. Test and document python code. Content The course will familiarize participants with the use of python as a basic data science tool, with a focus on the scikit-learn library. It combines exercises on data modelling tasks with brief theoretical introductions to the different types of models (supervised and unsupervised), as well as advanced visualization with the matplotlib and seaborn libraries. The course will further cover advanced features of the python language such as objects, classes and user-defined functions, which are elementary for expert use of python. Lastly, participants will be introduced to best practices for testing and documenting python code. This course builds on Python Part 1 and familiarity with the basic operations of python is a requirement Participants The course is intended for PhD students at SUND who have an elementary understanding of python and want to learn how to use it for a range of basic data science tasks, as well as getting more familiar with features of python that beginners typically do not use, such as classes and user-defined functions. As such it is an entry point to more advanced python programming, as well as data science anaylsis. Relevance to graduate programmes The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH: - All graduate programmes Language English Form Lectures and exercises Course director Anders Krogh, Professor, Head of Center for Health Data Science, Center for Health Data Science, anders.krogh@sund.ku.dk Teachers Henrike Zschach PhD, Data Scientist, Center for Health Data Science, henrike.zschach@sund.ku.dk Andreas Bjerregaard Jeppesen MSc, PhD Student, Department of Computer Science, anje@di.ku.dk Helene Scheel Wegener PhD, Data Scientist, Center for Health Data Science, helene.wegener@sund.ku.dk Dates 19 – 21 May 2025 Course location Room to be announced Faculty of Health and Medical Sciences, Panum, Blegdamsvej 3B, 2200 København. Registration Please register before 18. April 2025 Expected frequency Will run again in the autumn of 2025. Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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