Faculty of Science

Python for SCIENCE
Provider: Faculty of Science

Activity no.: 5197-26-00-00	There are 47 available seats
	Enrollment deadline: 17/12/2025

Place	Department of Computer Science Universitetsparken 1, 2100 København Ø
Date and time	21.01.2026, at: 09:00 - 30.01.2026, at: 16:00
Regular seats	50
Activity Prices:
- 1 Participant fee (all participants in add. to course fee)	0.00 kr.
- 2 Course fee PhD student enrolled at UCPH SCIENCE	0.00 kr.
- 3 Course fee PhD student at Danish Universities (except CBS)	0.00 kr.
- 4 Course fee PhD student at Copenhagen Business School	3,000.00 kr.
- 5 Course fee PhD student at foreign university	3,000.00 kr.
- 6 Course fee Master's student at Danish university	0.00 kr.
- 7 Course fee Master's student at foreign university	3,000.00 kr.
- 8 Course fee Employee at university (e.g., postdocs)	3,000.00 kr.
- 9 Course fee Others (e.g., from a private company)	8,400.00 kr.
Lecturers	Erik Bjørnager Dam
ECTS credits	2.50

Contact person	Erik Bjørnager Dam E-mail address: erikdam@di.ku.dk

Enrolment Handling/Course Organiser	PhD Administration SCIENCE E-mail address: phdcourses@science.ku.dk

Enrolment guidelines This is a toolbox course where 80% of the seats are reserved to PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% og the seats are reserved to PhD students from other Danish Universities/faculties (except CBS). Anyone can apply for the course, but if you are not a PhD student at a Danish university (except CBS), you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to applicants on the waiting list. The course is free of charge for PhD students at Danish universities (except CBS). All other participants must pay the course fee (except if you are a master’s student from a Danish University). Aim and Content The course introduces to the dominant programming language in data science, Python. Python is a general-purpose programming language that is currently being used in many active data science projects with open-source libraries available. The workshop will teach the basic programming constructs in Python and then provide data science examples, including data import, visualization, and analysis. We will introduce integrated development interfaces such as JupyterLab. We will introduce libraries from active open-source frameworks (numpy, pandas, matplotlib, sklearn, …). We will further discuss methods for securing reproducibility of research results (code architecture, versioning, open source). The course is aimed at PhD students, who need tools for data exploration, data analysis, and data visualization. Post Docs, Professors, and Master's thesis students from SCIENCE may register for participation and will be accepted if space permits. Learning outcomes Intended learning outcome for the students who complete the course: Knowledge • Understand computational thinking concepts. • Understand key programming elements (e.g. variables, objects, functions, modules). • Know useful open-source libraries (e.g. pandas, matplotlib, sklearn). Skills • Develop/adapt/extend a computer-based software program for analysis of relevant data. • Apply good practice co-development principles. Competences • Propose relevant analysis methods for scientific data science problems. • Consider cross-disciplinary data science methods in their research. Target Group PhD students from all SCIENCE departments with an element of data science in their research project. Recommended Academic Qualifications None. Research Area All SCIENCE research fields, and secondarily other scientific fields with a data science element (e.g. health sciences). Teaching and Learning Methods The course is composed of sessions combining lectures and exercises. For each topic, the students will get hands-on experience in applying, modifying, and creating elements of analysis methods. The programming examples will be implemented in JupyterLab notebooks and in pure Python source files. Type of Assessment The students need to be physically present and active during the course. Literature Course lecture slides and exercises. We will use data, examples, and other material from publicly available sources. Course coordinator Erik Dam, Professor, erikdam@di.ku.dk Dates Wed 21, Fri 23, Mon 26, Wed 28, Fri 30 January 2026, from 9 to 16 all days. Course location Physically on campus. Typically at Nørre Campus, alternatively at Frederiksberg Campus. 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 deadline for registration. Cancellation policy • Cancellations made up to two weeks before the course starts are free of charge. • Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000 • Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000 • No-show will result in a fee of DKK 5.000 • Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000 Course fee and participant fee PhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types. In addition to the course fee, there might also be a participant fee. If the course has a participant fee, this will apply to all participants regardless of participant type - and in addition to the course fee.

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