Curriculum
Data analysis in R and Ai (Urdu medium) Recorded Course
Module 1:What You Will Learn in This Course: Data Analysis in R & AI
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Module 2: Downloading and Installation of R & RStudio
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Module 3: What is the Difference Between R and RStudio?
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Module 4: Working Directory and Packages
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Module 5.1: Reading, Writing, and Editing R Scripts & Solving Basic Errors
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Module 5.2: Reading, Writing, and Editing R Scripts & Solving Basic Errors
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Module 6: Basic Plots Using ggplot2, Script Writing & Solving Basic Errors
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Module 7: Correlation Analysis and Correlation Plots in R
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Module 8: Use of AI for Script Writing and Data Visualization in R
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Module 9: Heatmap Visualization in R
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Module 11: Descriptive statistics in R
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Module 14:Time series analysis in R
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Module 11: t-Tests in R (One-Sample, Two-Sample & Paired t-Test)
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Module 12 : ANOVA , Onway anova , Two way ANOVA and Many more
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Module 13: Regression and its different types
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Module 1:What You Will Learn in This Course: Data Analysis in R & AI
📘 Course Contents
💡 Note: These topics are for practice only. After completing the course, you will be able to perform more than 100+ data analyses relevant to your field.
🔹 Module 1: Introduction to R & RStudio
Installing R & RStudio
Working Directory & File Management
🔹 Module 2: Data Visualization (Basic to Advanced)
Bar, histogram, boxplot, scatterplot
Violin, bubble, ridge, heatmap, pair plots
Facets & multi-panel layouts
Interactive plots (plotly, ggiraph, gganimate)
Geospatial mapping using sf, ggmap, leaflet
🔹 Module 3: Exploratory Data Analysis (EDA)
Summary statistics & distributions
🔹 Module 4: Statistical Analysis
Descriptive & inferential statistics
t-test, Z-test, Chi-square, F-test
Correlation & covariance
Probability distributions
Hypothesis testing
🔹 Module 5: Regression Analysis (Comprehensive)
Simple & multiple linear regression
Polynomial & logistic regression
Ridge, Lasso, Elastic Net
Stepwise (forward/backwards)
Non-linear & quantile regression
Robust regression
GLM & GAM
Survival models (Cox Regression)
Hierarchical/Mixed models
SEM (Structural Equation Modelling)
🔹 Module 6: Non-Parametric Tests
Mann–Whitney, Wilcoxon, Kruskal–Wallis
Friedman, Kolmogorov–Smirnov
Sign test, Spearman rank
🔹 Module 7: Multivariate Analysis
PCA, factor analysis, cluster analysis
LDA, QDA
MDS, CCA, correspondence analysis
🔹 Module 8: Time Series Analysis
Decomposition & smoothing
ACF/PACF
ARIMA/SARIMA
Forecasting (forecast, prophet)
Stationarity & seasonality
🔹 Module 9: Specialized Visualizations
Network analysis (igraph, graph)
Chord diagrams (circlize)
Sankey, radar charts, circular bar plots
Word clouds, treemaps, sunburst
Survey/Likert data visualization
🎯 What You Will Gain
✅ Practical skills in R for real-world data analysis
✅ Official E-Certificate
✅ Publication-ready visualizations
✅ Confidence in statistical modelling and analytics
🎉 Don’t Miss This Opportunity!
Whether you are a student, researcher, academic, or professional, this course is your pathway to mastering R, statistics, data analysis, and AI-driven workflows.
Dr. Syed Atiq Hussain
PyRlytics