About This Course
Learn R programming for environmental data analysis, from fundamentals to exploratory data analysis, statistical testing, visualization, multivariate analysis, and geospatial mapping using real-world datasets, with AI-assisted coding using GitHub Copilot.
Program Details
Course Benefits
- No prior programming experience required
- E-certificate provided upon course completion
- Lifetime access to recorded sessions
Who Can Participate
- Students
- Researchers
- Faculty members
- Environmental consultants
- Anyone interested in environmental data science
Course Syllabus ▼
MODULE 1: Statistics Theory & R Fundamentals
Statistical fundamentals, measures of central tendency and dispersion, normal distribution, assumptions, parametric and non-parametric tests, correlation, regression, p-values, hypothesis testing, introduction to R programming, Positron IDE, variables, vectors, data frames, functions, loops, data import, tidyverse workflow, data cleaning, mutate, filtering, grouping, and summarising.
MODULE 2: Data Visualization – Static & Interactive
Grammar of graphics using ggplot2, time series plots, histograms, scatter plots, density plots, publication-quality figure customization, interactive visualizations using Plotly, interactive mapping using Leaflet, and administrative boundary visualization using geospatial vector data.
MODULE 3: Geospatial Environmental Data Analysis
Raster and vector geospatial analysis using terra, downloading and processing global environmental datasets for Delhi using geodata, land cover analysis including built-up area, tree cover, and water bodies, cropland and agricultural datasets, soil pH analysis, raster cropping and masking, environmental data visualization, spatial environmental interpretation, downloading global meteorological station data using worldmet, identifying nearby meteorological stations, accessing NOAA ISD datasets, meteorological data handling, and wind rose visualization using openair.
MODULE 4: AI-assisted Coding & Modern Data Science Workflows
Introduction to AI-assisted development environments, GitHub Copilot, GPT models, Claude models, intelligent code completion, code explanation, debugging, refactoring, workflow optimization, and AI-assisted environmental data analysis.
MODULE 5: Statistical Data Analysis & Multivariate Techniques
Exploratory data analysis, descriptive statistics by groups, seasonal pattern analysis, normality testing, homogeneity testing, non-parametric statistical tests, automated significance testing, correlation analysis, simple linear regression, interpretation of statistical outputs, correlation matrices, correlation visualization, multivariate environmental datasets, Principal Component Analysis, PCA computation, PCA biplots, and interpretation of PCA outputs.