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
Data Science covers both Statistics and Machine Learning. This toolbox 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 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.
The course report will be written like a draft of a research paper – that may ideally be completed and submitted following the course.
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 consider plenum lectures on some specific analysis methods and to plan the project supervisions.
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 all SCIENCE departments with a clear element of data science in their research project.
Recommended Academic Qualifications
The students must either have some Statistics or Machine Learning experience, corresponding to either the Statistical Methods for SCIENCE or Machine Learning for SCIENCE PhD toolbox courses.
The students should have passed the PhD Fundamentals course Module 2. In addition, the participants should have passed either the Statistical Methods for SCIENCE or Machine Learning for SCIENCE PhD toolbox courses, or have similar experience.
If in doubt, contact the course responsible
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 first few course days may include traditional lectures. Following this, most of the work will be organized during individual supervision meetings with the course lecturers. 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.
Course coordinator
Susanne Ditlevsen - susanne@math.ku.dk
Dates
02 February - 07 April 2026.
Course last for 2-3 months where the meetings are coordinated between student and supervisor.
Course location
Physically on campus for initial plenum session(s).
Most of the course is coordinated between the PhD student and the assigned project supervisor.
Course fee
• PhD student enrolled at SCIENCE: 0 DKK
• PhD student from Danish PhD school Open market: 0 DKK
• PhD student from Danish PhD school not Open market: 4800 DKK
• PhD student from foreign university: 4800 DKK
• Master's student from Danish university: 0 DKK
• Master's student from foreign university: 4800 DKK
• Non-PhD student employed at a university (e.g., postdocs): 4800 DKK
• Non-PhD student not employed at a university (e.g., from a private company): 12.000 DKK
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.