Data Analysis with R Specialization Course Syllabus

Overview: This specialization provides a comprehensive introduction to data analysis using R, designed for beginners and professionals looking to strengthen their statistical reasoning. Over approximately 7 weeks of content, learners will engage with hands-on data analysis techniques, covering descriptive statistics, inferential methods, and regression modeling. Each module combines theory with practical coding exercises using R, ensuring a solid foundation for real-world data analysis tasks. The course concludes with a capstone project that integrates all skills learned.

Module 1: Understanding and Visualizing Data with R

Estimated time: 14 hours

• Data types and structures in R
• Summary statistics and measures of central tendency
• Data visualization with base R and ggplot2
• Creating histograms, box plots, and scatter plots
• Introduction to probability distributions

Module 2: Exploratory Data Analysis in R

Estimated time: 10 hours

• Handling missing data and outliers
• Grouping and summarizing data with dplyr
• Reshaping data using tidyr
• Exploring relationships between variables
• Visualizing multivariate data

Module 3: Inferential Statistical Analysis with R

Estimated time: 14 hours

• Sampling distributions and the Central Limit Theorem
• Confidence intervals for means and proportions
• Hypothesis testing: t-tests and z-tests
• Interpreting p-values and significance levels
• Performing A/B testing in R

Module 4: Fitting Linear Models with R

Estimated time: 18 hours

• Simple linear regression with R
• Model assumptions and diagnostics
• Interpreting regression coefficients and R-squared
• Modeling with categorical predictors
• Analysis of Variance (ANOVA) in R

Module 5: Multiple Regression and Model Building

Estimated time: 16 hours

• Fitting multiple regression models
• Handling multicollinearity and interaction terms
• Model selection and stepwise regression
• Validating model performance
• Reporting results from regression analyses

Module 6: Final Project

Estimated time: 20 hours

• Perform exploratory data analysis on a real-world dataset
• Apply inferential statistics to test hypotheses
• Build and interpret a regression model using R

Prerequisites

• Familiarity with basic algebra and statistical concepts
• No prior R experience required, but comfort with computers is helpful
• Access to a computer with R and RStudio installed

What You'll Be Able to Do After

• Perform exploratory data analysis using R
• Apply statistical inference to real-world problems
• Construct confidence intervals and conduct hypothesis tests
• Build and interpret linear regression models
• Create publication-quality data visualizations in R