Learn Biological Data Analysis with R | NPTEL Course by IIT Kharagpur
Course Details
| Exam Registration | 713 |
|---|---|
| Course Status | Ongoing |
| Course Type | Core |
| Language | English |
| Duration | 8 weeks |
| Categories | Biotechnology, Biological Sciences & Bioengineering, Computational Biology |
| Credit Points | 2 |
| Level | Undergraduate/Postgraduate |
| Start Date | 16 Feb 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 16 Feb 2026 |
| Exam Registration Ends | 27 Feb 2026 |
| Exam Date | 25 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Master Biological Data Analysis and Visualization with R: An Expert-Led NPTEL Course
In the era of big data biology, the ability to analyze and visualize complex genomic and transcriptomic datasets is a critical skill for modern biologists and biotechnologists. To bridge the gap between biological questions and computational answers, the National Programme on Technology Enhanced Learning (NPTEL) offers a comprehensive course: Biological Data Analysis and Visualization with R. This 8-week program, meticulously designed and taught by an expert from IIT Kharagpur, provides a powerful toolkit for anyone looking to derive meaningful insights from biological data.
Meet Your Instructor: Prof. Riddhiman Dhar
Leading this course is Prof. Riddhiman Dhar, an Assistant Professor in the Department of Biotechnology at IIT Kharagpur. With a research focus on genomic/transcriptomic data analysis and building predictive machine learning models, Prof. Dhar brings cutting-edge expertise directly to the virtual classroom. His academic journey includes a PhD in Evolutionary Systems Biology from the University of Zurich and B.Tech & M.Tech degrees from IIT Kharagpur itself. Since 2018, he has been teaching advanced courses in Bioinformatics and Epigenetics at IIT KGP, ensuring the course content is both rigorous and highly applicable.
Who Should Enroll?
This course is perfectly tailored for:
- Undergraduate (UG) & Postgraduate (PG) Students in Biotechnology, Biological Sciences, and Bioengineering.
- PhD Scholars embarking on research involving biological datasets.
- Professionals and researchers in Computational Biology and Bioinformatics seeking to strengthen their R skills.
- Anyone interested in applying statistical programming to solve biological problems.
Course Prerequisites & Industry Relevance
To ensure you get the most out of this course, a foundational knowledge of R software is required. Learners can prepare by taking the NPTEL course "Introduction to R Software" (Course ID: 111104100). The skills taught are in high demand, with strong industry support from companies operating in biological data science, pharmaceuticals, agri-biotech, and bioinformatics services.
What Will You Learn? A Week-by-Week Breakdown
The course is structured to take you from foundational concepts to advanced analytical techniques, all within the R environment.
| Week | Topic | Key Learning Outcomes |
|---|---|---|
| 1 | Introduction and Set Up | Configure your R environment for biological data analysis, understand workflows. |
| 2 | Basic Statistical Analysis & Visualization | Perform essential stats and create publication-quality graphs with ggplot2. |
| 3 | Bioconductor Packages | Explore the powerhouse of R bioinformatics: install and use Bioconductor for genomic data. |
| 4 | Gene Expression Analysis & Co-expression Networks | Analyze RNA-seq/microarray data, identify differentially expressed genes, and build networks. |
| 5 | Analysis of ChIP-seq Data in R | Process and visualize chromatin immunoprecipitation sequencing data to find protein-DNA interactions. |
| 6 | Regression Models on Biological Data | Apply linear and logistic regression to model biological relationships and outcomes. |
| 7 | Dimensionality Reduction Techniques | Use PCA, t-SNE, and other methods to visualize and interpret high-dimensional data. |
| 8 | Decision Trees and Random Forest | Implement machine learning algorithms for classification and feature importance in biological datasets. |
Why Choose This Course?
- Hands-On Learning: Move beyond theory with practical demonstrations on real biological datasets.
- R & Bioconductor Focus: Gain proficiency in the industry-standard tools for bioinformatics.
- Comprehensive Curriculum: Covers from basic visualization to advanced machine learning, all in context.
- Expert Instruction: Learn from an IIT professor actively engaged in cutting-edge research.
- Career-Oriented Skills: Acquire directly applicable skills that enhance research and employability in biotech and pharma.
Recommended Textbooks & Resources
To supplement your learning, consider these excellent resources:
- Introduction to Bioinformatics with R: A Practical Guide for Biologists (Chapman & Hall/CRC Series)
- R Programming for Bioinformatics (Chapman & Hall/CRC Series)
- A Little Book of R for Bioinformatics 2.0 (Freely available online)
Embark on your journey to becoming proficient in biological data science. Enroll in Biological Data Analysis and Visualization with R and transform raw data into groundbreaking biological insights.
Enroll Now →