Big Data Analytics and Machine Learning – Biostatistics and Epidemiology in Translational Medicine

Course content

Big data and machine learning can play a crucial role in advancing translational medicine and improving patient outcomes. While randomized controlled trials (RCTs) remain the gold standard for informing clinical practice, observational data such as healthcare register data can be useful for monitoring the real-world implementation of evidence and gain more insights into the underlying mechanisms of diseases and treatment. However, observational research presents numerous methodological pitfalls, and the analysis of complex large-scale datasets requires a modernization of biostatistical tools and careful guidance of machine learning techniques.


The course emphasizes state-of-the-art biostatistical machine learning based methods tailored to answer pressing research questions in medicine. The aim is to increase awareness of the potential of novel statistical methods, the availability of (big) data sources and the methodological limitations and challenges in analysing them.


By the end of the course, the participants should be able to communicate and collaborate effectively with subject matter experts and professional statisticians, in order to optimize their research in translational medicine.


BRIDGE - Translational Excellence Programme

Learning outcome

Upon completing the course, participants should be able to:



  • Describe the benefits and limitations of analyses based on experimental and non-experimental data sources; particularly, list and distinguish common biases and pitfalls in the analysis of observational studies.
  • Explain the concept of a "question-first" approach to statistical analysis, and particularly the use of a causal language to frame and communicate scientific questions and identify examples of research questions that could be addressed.
  • Summarize the advantages and limitations of using machine learning tools in medical research and explain the overall differences between prediction and inference for interpretable parameters.



  • Employ causal inference tools and target trial conceptualization to translate relevant scientific questions to well-defined statistical parameters in statistical collaborations.
  • Utilize causal diagrams to discuss current scientific knowledge, and to identify and evaluate potential sources of bias.
  • Apply critical thinking in evaluation of scientific literature and when engaging in scientific collaborations.


  • Evaluate the benefits and limitations of (causal) statistical analyses incorporating machine learning tools based on experimental and non-experimental big data sources.
  • Communicate and collaborate more effectively with subject matter experts and professional statisticians to answer pressing research questions in medicine based on big data and machine learning.
  • Recognize the ethical implications of big data and machine learning in translational medicine and be able to engage in discussions on responsible use and interpretation of these tools.

Five full days with lectures, group work, discussions, and computer exercises.

The course will end with an evaluation where participants must reflect on course learning outcomes and give feedback for course development.

Course literature will be published on Absalon.

Participants must meet the admission criteria of the BRIDGE - Translational Excellence Programme.

Continuous feedback during the course of the semester
Type of assessment
Continuous assessment
Requirement to attend classes
Type of assessment details
Attendance and active participation.
Exam registration requirements

Participants are automatically registered for the examination upon admission to the BRIDGE - Translational Excellence Programme.

All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
Criteria for exam assessment

Active contribution and course participation according to the BRIDGE Guidelines and Practicalities.

Part time Master and Diploma courses

  • Category
  • Hours
  • Lectures
  • 18
  • Preparation
  • 15
  • Exercises
  • 12
  • English
  • 45


Course number
Programme level
Part Time Master
See course dates and course programme in Absalon.
15 participants
Study Board for the Professionel Master´s Degree Programmes at The Faculty og Health and Medical Science
Contracting department
  • Department of Public Health
Contracting faculty
  • Faculty of Health and Medical Sciences
Course Coordinators
  • Helene Charlotte Wiese Rytgaard   (4-6b686f7c4376787167316e7831676e)
  • Thomas Alexander Gerds   (3-77646a43656c7276776477316e7831676e)
Saved on the 23-04-2024

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