Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
R is one of the most widely used programming languages in biological research due to its powerful statistical and visualization capabilities. From genomic data analysis to ecology, R enables scientists to perform data manipulation, statistical modeling, and complex visualizations to interpret and communicate results effectively. This course provides a hands-on approach to mastering R, focusing on its application in biological sciences, genomics, biostatistics, and bioinformatics. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
R is one of the most widely used programming languages in biological research due to its powerful statistical and visualization capabilities. From genomic data analysis to ecology, R enables scientists to perform data manipulation, statistical modeling, and complex visualizations to interpret and communicate results effectively. This course provides a hands-on approach to mastering R, focusing on its application in biological sciences, genomics, biostatistics, and bioinformatics.
📋 Course Objectives
1. Get comfortable working with application in biological sciences.
2. Put biotechnology techniques to work on real datasets and case studies.
3. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into application in biological sciences
🚀 Key Learning Outcomes
• Confidence to implement application in biological sciences in real projects.
• A portfolio-grade biotechnology deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
R for Experimental Data
• 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
Statistics That Match the Experiment
• 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
Omics Workflows in R
• Bioconductor object model: SummarizedExperiment and friends
• Differential expression with DESeq2 or limma
• Annotation, enrichment and visualisation of results
Figures for Publication
• 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
Reproducibility and Communication
• 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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | Seaborn |
| Covered Tool / Platform | Tableau |
| Covered Tool / Platform | SQL |
Frequently Asked Questions
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