What is the format of this R: Advanced Data Analytics for Life Sciences & Research Careers course?

This is an Recorded Lectures (Self-Paced) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from DSTC (DSTC) that you can showcase on your CV and LinkedIn profile.

What are the prerequisites for this course?

No prior experience is required. This course is designed for beginners and takes you step by step from the basics to advanced topics.

How long will I have access to the course materials?

You will have access to all course materials for the duration of 3 Days 1.5 hr/day. The self-paced format allows you to learn according to your own schedule through our online learning management system.

Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to Data Science. Our mentors are industry experts and experienced professionals. Enroll in R: Advanced Data Analytics for Life Sciences & Research Careers today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering Data Science skills that matter.

📚 Syllabus & Course Curriculum

Data Science & Analytics

Module-by-module breakdown of R: Advanced Data Analytics for Life Sciences & Research Careers, from foundations to a certified capstone project.

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Foundations

• Tidy data structures for experimental designs with nested factors
• Reading instrument and plate-reader exports without silent coercion errors
• Reproducible project structure with renv and Quarto

Design

• Replication, pseudoreplication and the unit of analysis
• ANOVA, mixed models and repeated measures in R
• Power analysis before the experiment rather than after a null result

Bioconductor

• Bioconductor object model: SummarizedExperiment and friends
• Differential expression with DESeq2 or limma
• Annotation, enrichment and visualisation of results

Visualisation

• ggplot2 for multi-panel figures with consistent theming
• Showing distributions and individual points instead of bar-and-error-bar plots
• Export at journal-required dimensions and resolution

Career

• Version control and sharing analysis with collaborators
• Writing a supplementary methods section from your own code
• Building a portfolio of reproducible analyses for research roles

Earn government-registered certification in R: Advanced Data Analytics for Life Sciences & Research Careers

e-Certificate and e-Marksheet issued on successful completion.

View full course →

Scholar Registration

For scholars whose department, college or employer pays the fee. We raise a proforma invoice to your institution; you attach the signed processing letter or bank slip.

Academic Registry Hub

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R: Advanced Data Analytics for Life Sciences & Research Careers

📚 Syllabus & Course Curriculum