Graduate School of health and Medical Sciences

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Hands-on introduction to Pharmaceutical Data Science
Provider: Faculty of Health and Medical Sciences

Activity no.: 3958-26-00-00	There are 24 available seats
	Enrollment deadline: 10/08/2026

Date and time	07.09.2026, at: 09:00 - 23.09.2026, at: 17:00
Regular seats	25
Course fee	8,160.00 kr.
Lecturers	Albert Kooistra
ECTS credits	5.60

Contact person	Marianne W. Jørgensen E-mail address: marianne.joergensen@sund.ku.dk

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

Enrolment guidelines 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 The participants will learn core pharmaceutical data science elements to then apply during their own research. They will be able to: 1. Understand and apply basic scientific code using Python 2. Perform data cleaning, transformation, and statistical analyses 3. Create meaningful data visualizations for pharmaceutical data (e.g. for publications and presentations) 4. Apply and evaluate basic machine learning techniques 5. Design and carry out basic data-driven projects in the pharmaceutical research field Content This PhD course is the first in a series designed and offered by the Center for Pharmaceutical Data Science Education (CPDSE) to strengthen the data skills of researchers working in the field of pharmaceutical sciences. It is intended for researchers in pharmaceutical sciences and related disciplines who are interested in acquiring a foundational understanding and hands-on skills for scientific data handling, visualization, and activation. With a practice-driven approach, the course introduces participants to Python programming, data analysis, visualization techniques, and an overview of AI applications in pharmaceutical research. Through hands-on exercises, participants will learn key steps in accessing, processing, analyzing, and visualizing data. The course is structured to support understanding and practical applicability, so the participants will be equipped with skills and tools they can take home and apply directly to their own research. Students will progress from computational thinking and basic programming to working with real-world pharmaceutical data, coming together in a group project where they apply their skills to a custom research question. The course includes lectures and hands-on exercises on: • Foundations of Python programming for data science • Fundamentals of statistics and data structures • Use of large language models (LLMs) like ChatGPT and Copilot for coding support and data tasks • Data management principles, including data lifecycle and FAIR data practices • Hands-on data cleaning and wrangling with real-world pharmaceutical datasets • Design and implementation of data visualizations • Introduction to machine learning concepts • Application of ML models to real-world pharma data • Data-driven project work tackling domain-specific challenges Participants The course is primarily offered to PhD students who have completed undergraduate courses in pharmacy, medicine, pharmacology, biochemistry, biology, chemistry, molecular biology or similar topics. In addition, the course is offered to researchers within the pharmaceutical industry or biotech. Students enrolled in part-time master's programs at the University of Copenhagen may also participate in the course. 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 Pharmaceutical Sciences (Drug Research Academy) Language English Form The course is offered as a 3-week parttime workshop and includes: • 14 lectures • 5 hands-on workshop sessions • 2 project days including project presentations and wrapping up Participants will be provided with study materials, relevant datasets, example code, and instructions. No prior programming experience is required. Course director Albert J. Kooistra, Associate Professor, Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Email: albert.kooistra@sund.ku.dk Teachers Center for Pharmaceutical data Science (ILF) Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, UCPH • Albert J. Kooistra, Associate Professor • Icaro A. Simon, Postdoctoral researcher • Jake Jackson, PhD student • Jonas Verhellen, Postdoctoral researcher • Kasper Harpsøe, Data and Computing Facility Manager • Melika Keshavarzmirzamohammadi, PhD student • Trinh Trung Duong Nguyen, Postdoctoral researcher • CPDSE Data specialists Center for Health Data Science Department of Public Health, Faculty of Health and Medical Sciences, UCPH • Henrike Zschasch, Data scientist Computer Science (DIKU) Department of Computer Science, Faculty of Science, UCPH • Kasper Hornbæk, Professor External guest speakers • Johanna Tiemann, Novonesis • Tsonko Tsonkov, Novo Nordisk Dates 7 – 23 September 2026 (7 days on-site) Course location School of Pharmaceutical Sciences, Universitetsparken 2, DK-2100 Copenhagen Ø, Copenhagen – DK. Registration Please register before 10 August 2026 Expected frequency Yearly, in September 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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