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A teaching proposal for a short course on biomedical data science.

Davide Chicco1,2, Vasco Coelho1

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This study outlines a master's degree curriculum for biomedical data science, focusing on data analysis and interpretation. It emphasizes practical skills in data handling, machine learning, and open science principles for training future data scientists.

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Area of Science:

  • Biomedical Data Science
  • Computational Statistics
  • Machine Learning

Background:

  • Increasing volume of big biomedical data necessitates specialized training for university students.
  • Existing curricula may not adequately cover the practical aspects of analyzing and interpreting health data.

Purpose of the Study:

  • To propose and describe a master's degree course plan for biomedical data science.
  • To share practical experiences from implementing the course in the last academic year.

Main Methods:

  • Curriculum development focusing on data acquisition, cleaning, and preparation.
  • Instruction on exploratory data analysis (EDA), machine learning (supervised/unsupervised), and result validation.
  • Integration of open science principles, utilizing open-source tools and data.

Main Results:

  • Students were trained to identify, clean, and prepare open biomedical datasets.
  • Practical application of statistical and machine learning techniques with interpretation of outcomes.
  • Emphasis on using open-source programming languages (R, Python) and open-access resources.

Conclusions:

  • The proposed curriculum effectively equips students with essential biomedical data science skills.
  • The course promotes best practices in data analysis and interpretation within an open science framework.
  • This teaching proposal serves as a valuable resource for developing new biomedical data science programs.