Data Science Projects
PhD School at the Faculty of SCIENCE at University of Copenhagen
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). Seats will be allocated on a first-come, first-served basis and according to the applicable rules.
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.
Aim and Content
This Data Science Projects course provides a platform to perform research with the student’s own data using either Statistical data analysis, Machine Learning methods, or possibly a combination. A supervisor from the Data Science Lab is assigned to each student for this work.
The data may come from own experiments or observational data and be tables of numbers, surveys in text or digital formats, pictures, scans, or video. All related to some scientific investigation from the PhD student’s own work.
Depending on the primary scope of each of data analysis problems, an expert in either Statistics or Machine Learning will supervise the project. Typically, Statistics projects use R and Machine Learning projects will use Python. However, other software platforms are possible depending on the student’s qualifications.
Typical analysis within the scope of Statistics is modelling of experimental data to establish an associative or causal relation between an outcome of interest and some explanatory variables, e.g. application of different treatments. Subsequent, to quantify relations that cannot be explained by biological variation but must be attributed to real effects.
Typical analysis within the scope of Machine Learning could be automated quantification of objects of interests in the data (for example, image analysis), combining different types of data to address a common research question (like combining text and measurements), or building predictive models using Machine Learning.
One week prior to the course, the participants must submit a synopsis with a short description of their data and the desired outcome. This will allow us to plan the project supervisions.
The actual course opens with a plenum introduction lecture the first day. A week later, the students present their synopsis in plenum. Following this, the course is organized around individual supervision meetings. The students will submit an Introduction (similar to the Introduction section in a research paper) and a final Report (a draft version of a research paper). On the final course day, the students will present their results in plenum.
The entire course is framed as conducting the steps leading to an interdisciplinary data science research paper. The final report will be written as a draft of such a research paper – that may ideally be completed and submitted following the course.
Learning outcomes
Intended learning outcome for the students who complete the course:
Knowledge:
• Describe the analysis methods used by others for similar problems.
• Describe relevant, alternative approaches for solving the problem.
Skills:
• Develop/adapt/extend a computer-based software method for quantification and/or analysis of their own data.
Competences:
• Formulate scientific questions from their PhD project in terms of research hypotheses.
• Interpret the results of their computer-based analysis in relation to their PhD project.
Target Group
PhD students from the natural science departments (e.g. UCPH SCIENCE) with a clear element of data science in their research project.
Recommended Academic Qualifications
The students must have some Statistics and/or Machine Learning skills, corresponding to either the Statistical Methods for SCIENCE or Machine Learning for SCIENCE PhD toolbox courses offered by the Data Science Lab.
Further, the students must have relevant data from their research project that can be the foundation for their data analysis during the course
Research Area
All SCIENCE research fields, and secondarily other scientific fields with a clear data science element (e.g. health sciences).
Teaching and Learning Methods
The first two course days will include traditional lectures. Following this, most of the work will be organized during individual supervision meetings with the course researchers. During the course, the students will perform peer review. The project must result in a research article style report and be presented before the class at the concluding seminar. The projects may be done using software packages of the participant’s own choice.
Type of Assessment
The students need to hand in their reports at the end of the course. The report must be approved. The students are allowed to work in 2-person groups.
Literature
This depends on the individual project.
For potential background literature, see the course pages for the Python for SCIENCE, Statistical methods for SCIENCE, or Machine Learning for SCIENCE; all listed on the Data Science Lab homepage at datalab.science.ku.dk.
Course coordinator
Professor Susanne Ditlevsen, susanne@math.ku.dk
Dates
February 1: Introduction lecture
February 8: Student presentations
April 5: Student presentations
Expected frequency
The course runs twice a year, starting in February and September.
Course location
Nørre Campus, Universitetsparken, Copenhagen.
Registration
Standard registration through the PhD course database.
Deadline for registration
15 January 2026
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.
Course fee
• Participant fee: DKK
• PhD student enrolled at SCIENCE: DKK 0
• PhD student from Danish PhD school Open market: DKK 0
• PhD student from Danish PhD school not Open market: DKK 4.800
• PhD student from foreign university: DKK 4.800
• Master's student from Danish university: DKK 0
• Master's student from foreign university: DKK 4.800
• Non-PhD student employed at a university (e.g., postdocs): DKK 4.800
• Non-PhD student not employed at a university (e.g., from a private company): DKK 13.440
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