SBRI19004U Big Data Analytics and Machine Learning I – Computational Biology in Translational Medicine

Volume 2022/2023

BRIDGE - Translational Excellence Programme



To exploit the full potential of big data for translational medicine, analytic techniques and machine learning methods are crucial. This course will include subjects like getting access to, harmonizing, and analyzing big data as well as concrete examples of how big data is used in translational medicine. It will include classical machine learning, deep learning, and text mining methods with both lectures and practical computer exercises.

Learning Outcome

On completion of the course, the participants should be able to:


  • Identify and describe different types of big data including molecular and registry data
  • Describe the procedure for applying for data access, capturing data and how data harmonization is necessary for data analysis
  • Discuss machine learning methods and provide examples of specific methods and their advantages and disadvantages



  • Use data analysis programs such as R/RStudio
  • Locate and apply for data access to registries
  • Apply machine learning algorithms on big data to predict relevant outcomes
  • Apply text mining for extracting information from clinical notes or biomedical literature



  • Discuss big data types and assess what such data can be used for in the context of translational medicine with specific focus on precision medicine
  • Benchmark and critically evaluate results of classical machine learning, deep learning and text mining methods for analyzing big data
  • Reflect on the central aspects of big data analytics and be able to discuss and communicate to other scientists, clinicians, and the public


Course literature is published on Absalon.

Participants must meet the admission criteria in BRIDGE - Translational Excellence Programme
Lectures and web-based and non-web-based, hands-on computer exercises.
  • Category
  • Hours
  • Lectures
  • 12
  • Theory exercises
  • 12
  • Total
  • 24
Continuous feedback during the course of the semester
Type of assessment
Continuous assessment
Course participation
Type of assessment details
Attendance and active participation
Exam registration requirements

Participants are automatically registered for the Examination upon course registration.

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

Active contribution and course participation according to the BRIDGE Guidelines.