DATA ANALYSIS USING R PROGRAMMING

ABOUT:

This course is designed to introduce learners to data analysis using R which is a free and an incredibly powerful tool for data analysis, statistical modeling, and machine learning. This course is suitable for beginners or individuals looking to deepen their existing skills.

Course details:

Purpose

By the end of this training, the learner will be able to:

• Use R and R Studio proficiently.
•  Import, clean, and manipulate datasets.
•  Perform exploratory data analysis (EDA).
• Apply statistical analysis techniques.
•  Create compelling data visualizations.
•  Develop machine learning models.
•  Generate automated reports and dashboards.
•  Work with real-world datasets.

TOPICS:

Module 1: Introduction to R and R Studio 

• Setting up R and R Studio
• Understanding R syntax and basics
•  R Data Types
• Vectors: Creation, Naming, and Manipulation
•  R packages and libraries (tidy verse, ggplot2, dplyr, etc.)

Module 2: Introduction to Data Preparation 

• R Matrix: Creation, Printing, Adding Column, and Slicing
• Factors in R: Categorical and Continuous Variables
• R Data Frame: Creation, Appending, Selecting, and Sub setting

Module 3: Data and introduction to Functions. 

• Lists in R: Creation and Selecting Elements
• Sorting a Data Frame using Order()
• Functions in R

Module 4 Data Manipulation and Transformation 

• Filtering, selecting, and grouping data
•  IF, ELSE, ELSE IF Statement in R
• Using pipes (%>%) for efficient data processing

Module 5: Data Importing and Cleaning 

•  Review of previous class assignments
• Handling missing values and data inconsistencies
• Reshaping data with tidyr

Module 6: Exploratory Data Analysis (EDA) 

• Summary statistics (mean (), median (), summary ()
• Correlations in Pearson &spearman’s

Module 7: Introduction to Visualizations

• Detecting outliers and data distributions
• Data visualization with ggplot2
• Creating histograms, scatter plots, and boxplots

Module 8: Statistical Analysis in R 

• Descriptive vs. inferential statistics
• Hypothesis testing (t-tests, chi-square tests)
• ANOVA and other statistical techniques

Module 9: Regression Analysis 

• Simple linear regression
• Multiple linear regression
• Stepwise regression
• Generalized Linear Model (logistic regression and Poisson regression)

Module 10: R Markdown & Notebooks 

• R Markdown Reports
• Introduction to R Markdown
• Formatting text using Markdown syntax
• Embedding R code using Knitr

      R Notebook

• Creating a notebook
• Inserting code chunks
• Managing the execution queue
• Configuring the execution environment

Module 11: Project & Real-World Applications

• Working on a real-world dataset
• Developing insights and recommendations
• Presenting findings using R Markdown or Shiny dashboards

Module 12: Introduction to Machine Learning with R 

• Understanding supervised vs. unsupervised learning

Deliverables:

• Completed R Programming Projects
• Data Analysis Reports
• Interactive Dashboards
• Machine Learning Model Implementations.
• Data Visualizations.
• Certificate of completion