Developing and validating an accelerated online Canadian training in data science for the health sciences community

Area of Science:

• Digital Health
• Data Science Education
• Clinical Research

Background:

• Digital health necessitates data science proficiency, yet university curricula often lack comprehensive training.
• Accessible platforms are crucial to bridge the gap in data analysis and programming education for healthcare professionals.

Purpose of the Study:

• To develop and evaluate a free, accelerated R programming course for healthcare trainees with no prior programming experience.
• To assess the impact of the R programming course on participants' data science skills for clinical research.

Main Methods:

• A 10-day, seven-video capsule R programming course was delivered to healthcare trainees.
• A pretest-posttest design assessed participants' skills immediately after training and at a three-month follow-up.
• 100 healthcare trainees from Montreal's academic community participated; skills were evaluated using mixed-effects models.

Main Results:

• 84% of participants completed the course within the 10-day timeframe.
• Mean test scores significantly increased from 4.5/10 pretest to 8.4/10 posttraining (p < .001).
• Skills remained significantly elevated at three months (6.8/10) compared to baseline (p < .001).

Conclusions:

• The accelerated R programming course effectively enhances data science skills in healthcare trainees.
• This training addresses critical gaps in formal data science education.
• The course has the potential to improve independent research and data analysis capabilities for healthcare professionals.