Data analysis in R and Ai (Urdu medium) Recorded Course

Module Description

This module introduces participants to the R programming environment and provides step-by-step guidance on downloading and installing R and RStudio on different operating systems. The session is designed for complete beginners and ensures that every participant successfully sets up a fully functional R working environment before starting data analysis.

Participants will learn the difference between R (the programming language) and RStudio (the integrated development environment), understand system requirements, and configure essential settings for smooth workflow. By the end of this module, learners will be ready to write and run their first R scripts confidently.

Learn how to set and manage the working directory in R and understand the role of packages. This module covers installing, loading, and updating packages to extend R’s functionality for efficient data analysis

Module Description

This module focuses on developing core programming skills in R by teaching participants how to read, write, and edit R scripts effectively. Learners will understand how R scripts work, how to structure code for clarity and reproducibility, and how to use RStudio’s script editor efficiently.

The module also introduces common beginner-level errors in R, explains why they occur, and demonstrates practical strategies to identify, debug, and fix them. By the end of this module, participants will gain confidence in writing clean R code and independently solving basic coding errors.

Understand the difference between R and RStudio, where R is the programming language and RStudio is the integrated development environment (IDE). This module explains how they work together to make coding, data analysis, and project management easier and more efficient.

Module Description

This module focuses on developing core programming skills in R by teaching participants how to read, write, and edit R scripts effectively. Learners will understand how R scripts work, how to structure code for clarity and reproducibility, and how to use RStudio’s script editor efficiently.

The module also introduces common beginner-level errors in R, explains why they occur, and demonstrates practical strategies to identify, debug, and fix them. By the end of this module, participants will gain confidence in writing clean R code and independently solving basic coding errors.

Module Description

This module introduces learners to data visualization in R using the ggplot2 package. Participants will learn how to create basic plots by writing and editing R scripts, understand the core grammar of graphics, and customize plots for clear and effective data presentation.

The module also covers common plotting and scripting errors, helping learners understand error messages related to ggplot2 and how to fix them efficiently. By the end of this module, participants will be able to generate clean, informative plots and troubleshoot basic visualization-related errors with confidence.

Module Description

This module introduces learners to correlation analysis and visualization in R using the psych and corrplot framework. Participants will learn how to calculate correlation coefficients and visualize relationships between variables through clear and interpretable correlation plots.

The module emphasizes reading, writing, and editing R scripts for correlation analysis, along with understanding and resolving common errors related to data types, missing values, and plotting functions. By the end of this module, learners will be able to create publication-ready correlation visualizations and troubleshoot basic scripting and plotting errors.

This module introduces learners to the use of Artificial Intelligence (AI) tools to assist with R script writing, data analysis, and data visualization. Participants will learn how AI can be effectively used to generate R code, improve script structure, debug errors, and accelerate the creation of high-quality visualizations.

The module emphasizes responsible and research-oriented use of AI, showing how to validate AI-generated code, customize it for specific datasets, and convert it into publication-ready R scripts and figures. By the end of this module, learners will be able to integrate AI tools into their R workflow to improve efficiency without compromising scientific rigor.

This module introduces participants to heatmap visualization in R, a powerful technique for exploring patterns, relationships, and variations in multivariate data. Learners will understand when and why heatmaps are used, and how to create clear, informative heatmaps using R.

The module covers reading, writing, and editing R scripts for heatmap generation, along with common errors and troubleshooting techniques. Emphasis is placed on producing publication-quality heatmaps commonly used in biological, agricultural, and statistical research.

This module introduces learners to T-tests, one of the most commonly used statistical methods for comparing means in research. Participants will learn the conceptual foundation of T-tests, when and why they are used, and how to perform different types of T-tests in R.

The module covers one-sample, two-sample (independent), and paired T-tests, with step-by-step R scripts for data preparation, analysis, and interpretation. Learners will also understand statistical assumptions, interpretation of p-values and confidence intervals, and how to report results in a publication-ready format. Common errors and troubleshooting techniques in R are also discussed.

This module introduces learners to descriptive statistics, which form the foundation of all data analysis and statistical modeling. Participants will learn how to summarize, explore, and understand datasets using numerical and graphical descriptive methods in R.

The module covers key summary measures such as mean, median, mode, variance, standard deviation, range, and percentiles, along with practical R scripts for implementation. Learners will also understand how to interpret descriptive statistics, check data distribution, and present results in a clear and publication-ready format. Common coding errors and data-handling issues in R are addressed to build confidence and accuracy.

This module introduces learners to Analysis of Variance (ANOVA), a powerful statistical method used to compare means across multiple groups. Participants will learn the theoretical concepts of ANOVA, understand when and why it is applied, and perform different types of ANOVA using R.

The module covers one-way ANOVA, two-way ANOVA, and extended ANOVA models commonly used in research. Emphasis is placed on checking assumptions, interpreting results, performing post-hoc tests, and presenting findings in a publication-ready format. Common errors and troubleshooting strategies in R are also discussed.

This module introduces learners to regression analysis, one of the most important statistical techniques for modeling relationships between variables and making predictions. Participants will learn the conceptual foundations of regression, understand when and why different regression models are used, and implement them using R.

The module covers simple and multiple linear regression as well as commonly used advanced regression models. Emphasis is placed on model assumptions, interpretation of coefficients, diagnostic checking, and result reporting in a publication-ready format. Learners will also practice reading, writing, and editing R scripts and resolving common regression-related errors.

This module introduces learners to time series analysis, a statistical approach used to analyze data collected over time to identify trends, seasonal patterns, and temporal dependencies. Participants will learn how to prepare, visualize, model, and forecast time-dependent data using R.

The module covers fundamental time series concepts, classical decomposition methods, and widely used forecasting models. Emphasis is placed on hands-on implementation in R, interpretation of results, model diagnostics, and presentation of findings in a clear and publication-ready format. Common errors and troubleshooting techniques in time series analysis are also discussed.