R for Biologists (Online Hands-on Training)

17–21 April 2026 | 7:15 PM IST | Online| Certificate

About the Training

This online hands-on training introduces participants to R programming, starting from basics to advanced data visualization and statistical analysis. The course focuses on real-world biological datasets, helping learners build practical skills for research, data analysis, and publication-quality visualizations.

What You Will Learn

• Fundamentals of R and RStudio environment

• Data structures (vectors, matrices, data frames)

• Data import, export, and preprocessing

• Data manipulation using dplyr

• Data visualization using ggplot2

• Statistical analysis (t-test, ANOVA, regression)

• Heatmaps and clustering techniques

• Creating publication-quality plots

Course Features

• Live hands-on sessions

• e-Certificate upon completion

• Session recordings for revision

• Lecture PPTs and study materials

• Real-world biological datasets for practice
  

Learning Outcomes

• Perform data analysis using R programming

• Manipulate and clean biological datasets

• Create advanced visualizations and plots

• Apply statistical methods to experimental data

• Generate publication-ready figures

  
  

Who Can Join

• Undergraduate and postgraduate students in Life Sciences

• Research scholars and faculty members

• Professionals from Biotechnology, Biochemistry, Microbiology, and related domains

• Medical, Pharmacy, and Chemical Sciences background participants

Course Fee

Indian Participants: ₹1299 INR
International Participants: 85 USD

Course Module

Day 1: Introduction to R and Basic Programming

• Introduction to R and RStudio

• Installing R & RStudio

• Understanding the R interface

• Running basic commands

• Variables, lists, vectors, matrices, data frames

• Operators and calculations

• Indexing and subsetting data

• Importing and exporting data (read.csv, read.table)

  Hands-on: Manipulate biological datasets

Day 2: Data Manipulation with dplyr

• filter(), select(), mutate(), arrange(), summarize()

• Combining commands with pipes (%>%)

• Transforming datasets and creating variables

• Reshaping and organizing data

  Hands-on: Clean and organize biological data

Day 3: Data Visualization with ggplot2

• Aesthetic mappings & geoms

• Scatter, bar, box plots

• Themes, labels, colors

• Regression lines and faceting

  Hands-on: Create and customize biological visualizations

Day 4: Statistical Analysis in R

• t-tests, ANOVA

• Statistical workflows

• Adding p-values to plots

• Correlation & linear regression

  Hands-on: Apply statistical analysis to gene expression data

Day 5: Advanced Visualization & Heatmaps

• Heatmap creation (pheatmap, ComplexHeatmap)

• Clustering and large dataset visualization

• Multi-panel and complex plots

  Hands-on: Build advanced visualizations