Making Data Science Work for Clinical Reporting
Learn to apply data science principles and tools effectively in clinical trial reporting while maintaining quality and compliance.
Learn to apply data science principles and tools effectively in clinical trial reporting while maintaining quality and compliance.
This comprehensive course demonstrates how to integrate data science methodologies into clinical reporting workflows. Students learn to balance efficient data analysis with regulatory requirements, quality standards, and transparency needs. The curriculum covers version control, reproducible research practices, code reusability, and risk management in clinical trials. Through practical examples and hands-on exercises, participants master the application of modern development practices while ensuring compliance with clinical reporting standards.
Instructors:
English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Master data science applications in clinical reporting
Implement version control and Git workflows effectively
Develop robust and reusable code for clinical analysis
Understand quality assurance and validation requirements
Apply DevOps practices in clinical settings
Assess and manage risks in clinical coding
Skills you'll gain
This course includes:
289 Minutes PreRecorded video
7 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 7 modules in this course
This course bridges the gap between data science and clinical reporting requirements. Through seven comprehensive modules, students learn to apply data science principles while maintaining compliance with clinical trial standards. The curriculum covers essential topics including version control, code quality, risk management, and reproducible research practices. Students gain practical experience with tools like Git and R, while learning to implement DevOps and agile methodologies in a clinical context.
Making Data Science work for clinical reporting
Module 1 · 54 Minutes to complete
The burden of being faultless and transparent
Module 2 · 1 Hours to complete
Bringing DevOps practices and agile mindset to clinical reporting
Module 3 · 2 Hours to complete
Version control and git flows for reproducible clinical reporting
Module 4 · 1 Hours to complete
Making code reusable and robust in clinical reporting — a call for InnerSourcing
Module 5 · 2 Hours to complete
Assessing and managing risk
Module 6 · 2 Hours to complete
Conclusion
Module 7 · 4 Minutes to complete
Fee Structure
Payment options
Financial Aid
Instructors
Developer Tooling Lead and Clinical Data Science Expert
Dinakar Kulkarni has established himself as a leader in developer tooling and clinical data science at Genentech. As Developer Tooling Lead, he specializes in implementing data science principles in clinical reporting environments. His expertise is demonstrated through his role as instructor for the course "Making Data Science Work for Clinical Reporting," where he teaches intermediate-level concepts combining data science tools with clinical reporting practices. His course covers crucial areas including version control, risk assessment in codebases, and the implementation of data science principles in clinical settings. His work focuses on bridging the gap between traditional clinical reporting and modern data science methodologies, helping professionals understand how to effectively apply tools like R and Python in clinical contexts. His expertise in DevOps practices and agile methodologies contributes to Genentech's mission of advancing healthcare through innovative data science applications.
Pharma Biometrics Specialist
Kamila Duniec is a Data Scientist at Genentech with more than fifteen years of experience in pharmaceutical drug development biometrics. Known for an enthusiastic embrace of Agile practices, Kamila has taken on roles as both Scrum Master and Product Owner, in addition to serving as Statistical Programmer and Subject Matter Expert for systems and processes. Their expertise bridges clinical data science and agile methodologies, focusing on optimizing workflows and innovation within biometrics teams. Kamila offers the course “Making Data Science Work for Clinical Reporting,” empowering learners with practical knowledge in data-driven pharmaceutical reporting and Agile teamwork.
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Frequently asked questions
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