October 2026 batch · Registration closed
Environmental Data Analysis Using R
Turn environmental data into research insights. Learn statistics, hypothesis testing, regression, PCA, and geospatial analysis in R with real datasets and guided practice.
Created and taught by Dr. Madhuraj P K
What you’ll learn
- Set up R and build a practical workflow for environmental datasets.
- Explore data distributions, descriptive statistics, and visualisation.
- Choose and interpret hypothesis tests, regression, correlation, and PCA.
- Work with raster and vector data to create environmental maps in R.
October 2026 live batch · Registration closed
8, 9, 12 & 13 October 2026
Four live sessions on Google Meet, two hours each. Class times across time zones:
Course curriculum
Four focused modules move from the R basics to statistical and geospatial analysis. Select a module to see the topics.
MODULE 1R Setup, Workflow, and AI-Assisted Coding
Introduction to R for environmental data analysis; working in Positron; understanding R data objects, vectors, functions, and packages; installing and managing packages; using AI-powered coding assistance; and building a beginner-friendly coding workflow for environmental datasets.
MODULE 2Fundamentals of Statistics in R
Understanding variability, normal distribution, and standard deviation; checking data distribution using density plots and Q-Q plots; normality testing using Shapiro-Wilk and Anderson-Darling tests; descriptive statistics for environmental datasets; boxplots, interquartile range, variance, and spread; and homogeneity of variance with Levene’s test.
MODULE 3Hypothesis Testing, Correlation, Regression, and PCA
Understanding null and alternative hypotheses; selecting the right statistical test for environmental data; parametric and non-parametric approaches; independent t-test, Welch’s test, ANOVA, Mann-Whitney U, and Kruskal-Wallis concepts; post-hoc interpretation; correlation analysis; simple linear regression; and Principal Component Analysis for multivariate environmental datasets.
MODULE 4Geospatial Data Analysis in R
Fundamentals of GIS and spatial thinking; datum, ellipsoid, geoid, and reference frames; geographic and projected coordinate systems; map projections, CRS, and EPSG codes; raster and vector data structures; working with DEM, temperature, and land use/land cover datasets; cropping and masking geospatial datasets; and environmental geospatial visualization including wind roses.
Included with the live course
- Live interactive sessions with practical demonstrations
- Envischolar e-certificate upon course completion
- Lifetime access to the recorded sessions from your program
- Course resources and practice code
Meet your instructor
Dr. Madhuraj P K
Environmental Data Scientist, researcher, and educator. Madhuraj has trained more than 500 learners across academia and industry. Learn more about the instructor ↗
Who can participate?
Students, researchers, faculty members, environmental consultants, and anyone interested in environmental data science. No prior coding experience is needed.
Real learner experiences
What participants say
Practical learning, in their own words.
★★★★★I am very much new to this field, though you made it very simple. Thank you so much sir.
★★★★★Thank you for developing this course content.
★★★★★Thank you very much for the course. It was very helpful and very well designed. Looking forward for many R courses like this.
★★★★★Thanks a lot Dr. Madhuraj for the support and the way you explained things to a learner is truly appreciable.