Workshops

Introduction to R

Appreciating AI

Our "sister Data Lab" at SUND, Center for Health Data Science (HeaDS), organizes courses on R and Python with room for a small number of participants from SCIENCE. More info at the website for HeaDS.

PhD Courses

The PhD courses are aimed at PhD students from all departments at SCIENCE, UCPH. The focus is to learn to apply Statistics and Machine Learning on a wide variety of data. Post Docs, Master's thesis students, and PhDs from outside SCIENCE may participate, if space permits. 

 

  • Python for SCIENCE is a PhD course that introduces 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 course  will teach the basic programming constructs in Python and provide data science examples, including data import, visualization, and analysis. We will introduce integrated development interfaces such as jupyter. We will introduce libraries from active open-source frameworks (numpy, pandas, matplotlib, sklearn, …).

  • 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.

  • Five full days of interactive, hands-on lectures and exercises. 

  • The course will take place on Wed 22, Fri 24, Mon 27, Wed 29, Fri 31 January 2025, from 9 to 16 all days. 

  • 3 ECTS
  • The course is a SCIENCE Toolbox course. 
  • The course is a "generic" PhD course. For participants not from the Faculty of SCIENCE, UCPH, there is a participation fee of 3600 kr. 
  • Location: TBD. 
  • Registration (mandatory) and more info: in the SCIENCE PhD course base
  • For questions: Julius Bier Kierkegaard  juki@di.ku.dk

 

 

  • Machine Learning for SCIENCE (MLS) is a PhD course where the students are introduced to machine learning methods that they could apply in their own research field.  
  • Data types include images, physical measurements, questionnaires, scans, Internet searches, and biochemical outcomes.
  • Methods include feature extraction, machine learning basics, key machine learning and image analysis methods based on both unsupervised and supervised learning, and visualization. From Linear Discriminant Analysis to Deep Learning.
  • Five full days of interactive, hands-on lectures and exercises. 
  • 3 ECTS
  • The course is a SCIENCE Toolbox course. 
  • The course is a "generic" PhD course. For participants not from the Faculty of SCIENCE, UCPH, there is a participation fee of 3600 kr. 
  • Dates: Five full Tuesdays, starting April 29, 2025 and ending May 27.  
  • Location: TBD. 
  • Registration (mandatory) and more info: in the SCIENCE PhD course base
  • For questions: Raghav Selvan raghav@di.ku.dk)

 

 

 

 

  • The course enables the student to identify basic statistical problems in the natural sciences and to carry out the appropriate statistical analysis. The open-source program R is used for the computations.
  • Teaching on 5 full days within two consecutive weeks: October 30 + 31 and November 6 +7 + 8.
  • 2.5 ECTS.
  • Price for participants who are not from the SCIENCE faculty at UCPH: 3000 DKK (excl. moms).
  • Maximum number of participants: 50
  • Registration: Course description and registration form from the PhD course database.
  • For more info write to Bo Markussen (bomar@math.ku.dk)

 

 

  • Data Science Projects is a project course where the PhD students work with their own data using statistical and/or machine learning methods. The focus can be hypothesis testing, classification, regression, segmentation, quantification, explanation, etc.
  • After a few plenum sessions, the projects run as individual supervision of each participant over 2 approximately months. The participants  write an article style report. It is mandatory that the participant has a suitable dataset for the project. 
  • Topics, methods, and tools: As appropriate for the data, the research question, and the student qualifications. 
  • Prerequisites for participation is SmS, MLS or a similar course on applied statistics and/or machine learning.
  • 4 ECTS
  • Participants: Max 20
  • The course is an updated and merged version of two previous courses, one with a focus on statistics and another focusing on machine learning. The course is given twice per year (following the SmS and MLS courses, see above) starting in February and September, respectively.
  • The next course will run from Thursday, February 6 to Thursday, April 3, 2025. Registration from the PhD course database is available here. 
  • Price for participants who are not from the SCIENCE faculty at UCPH: 4800 DKK (excl. moms).
  • For questions regarding the February version: Bo Markussen (bomar@math.ku.dk) 
  • For questions regarding the September version: Erik Dam (erikdam@di.ku.dk) 

 

Three-stage Rockets

The Data Science Lab offers workshops/courses in our two focus areas: dedicated Statistics and broader Data/Computer Science. Within each of the areas we offer a triple of workshops/courses. First, a workshop/course introducing computer programs for data handling and data analyses for students and researchers. Secondly, we have a PhD methods course that presents key concepts and methodologies. Finally, we have a projects PhD course where the PhD students perform supervised research on the own data using the tools and methods from the previous workshops/courses. 

Particularly for a PhD student,  this setup facilitates cross-disciplinary Data Science research, ideally delivering a research paper at the end of the projects course. 

Smaller tailor-made workshops

  • If you are a group of researchers/PhD students who often encounter the same type of problems, then it is also possible to arrange a smaller tailor-made workshop. Contact us and let's talk about the possibilities.