R Programming & Statistical Analysis

Introduction:

The R Programming & Statistical Analysis course is designed for individuals looking to gain in-depth knowledge of R programming and its application in data analysis and statistical computing. As a popular tool in the fields of data science, business analytics, and research, R provides powerful capabilities for data manipulation, statistical modeling, and data visualization. This course will equip participants with essential skills to analyze large datasets, apply statistical techniques, and generate actionable insights using R.

Course Objective:

By the end of this course, participants will:

• Master the basics of R programming and key R packages for data analysis.

• Learn how to clean, manipulate, and transform datasets efficiently in R.

• Apply descriptive and inferential statistics to make data-driven decisions.

• Perform hypothesis testing, correlation, and regression analysis.

• Visualize data using ggplot2 and other popular R visualization tools.

• Execute real-world data analysis projects to solve business or research problems.

Course Outline:

Module 1: Introduction to R Programming

• Overview of R programming and its importance in data analysis.

• Setting up the R environment: Installing R and RStudio.

• Basic R programming concepts: Variables, data types, loops, and functions.

• Introduction to R packages: Using popular packages like tidyverse and ggplot2.

• Hands-On: Writing and running basic R scripts.

Module 2: Data Manipulation with R

• Working with Data Frames and Tibbles in R.

• Importing and exporting data (CSV, Excel, and databases).

• Cleaning data: Handling missing values, duplicates, and outliers.

• Using dplyr for data manipulation: Filter, Select, Mutate, and Summarize.

• Combining and reshaping data: join, gather, and spread functions.

• Hands-On: Cleaning and preparing datasets for analysis.

Module 3: Data Visualization with ggplot2

• Introduction to data visualization principles.

• Creating basic visualizations: Bar charts, line charts, scatter plots, and histograms.

• Customizing visualizations with ggplot2: Titles, labels, themes, and colors.

• Advanced visualizations: Box plots, heatmaps, and faceting.

• Visualizing distributions and relationships between variables.

• Hands-On: Creating effective visualizations using ggplot2.

Module 4: Descriptive Statistics

• Overview of descriptive statistics and its role in data analysis.

• Measures of central tendency: Mean, median, and mode.

• Measures of variability: Range, variance, standard deviation, and IQR.

• Summarizing data with summary statistics functions.

• Visualizing statistical summaries using charts and plots.

• Hands-On: Performing descriptive analysis on datasets.

Module 5: Inferential Statistics and Hypothesis Testing

• Introduction to inferential statistics: Samples vs. populations.

• Understanding probability distributions (Normal, Binomial, etc.).

• Hypothesis testing: t-tests, ANOVA, and Chi-Square tests.

• Confidence intervals and significance levels.

• P-values and decision-making in hypothesis testing.

• Hands-On: Conducting hypothesis tests and interpreting results.

Module 6: Correlation and Regression Analysis

• Understanding relationships between variables with correlation analysis.

• Pearson and Spearman correlation coefficients.

• Introduction to linear regression: Simple and multiple regression models.

• Model building and interpreting regression coefficients.

• Model diagnostics: Residual plots, R-squared, and p-values.

• Hands-On: Building and evaluating regression models using R.

Module 7: Advanced Statistical Techniques

• Introduction to logistic regression for binary outcomes.

• Time series analysis: Trends, seasonality, and forecasting models.

• Principal Component Analysis (PCA) for dimensionality reduction.

• Survival analysis for healthcare and business applications.

• Hands-On: Implementing advanced statistical models in R.

Module 8: Real-World Data Analysis Project

• End-to-end data analysis project using R.

• Problem definition, data collection, and cleaning.

• Exploratory Data Analysis (EDA) using R.

• Applying appropriate statistical techniques and visualizing results.

• Generating a report and presenting insights.

• Hands-On: Completing a capstone project on a real dataset.

Module 9: Reporting and Sharing Results

• Creating reports with RMarkdown.

• Automating analysis workflows with knitr.

• Exporting results and visualizations to Excel, PDF, or HTML.

• Hands-On: Generating professional reports and sharing results.

Module 10: Certification and Assessment

• Final project presentation and evaluation.

• Certification exam for R programming and statistical analysis.

• Preparation tips and study materials for certification.

Course Duration: 40-50 hours of instructor-led or self-paced learning.
Target Audience: Data analysts, researchers, business professionals, and anyone interested in learning R programming for data analysis and statistical computing