HarvardX: Data Science: Linear Regression course Syllabus

Overview: This course provides a rigorous introduction to linear regression, a foundational technique in data science and statistical modeling. Over approximately 8 weeks, learners will build a strong understanding of regression theory and practice, with hands-on applications using real-world datasets. The course balances mathematical rigor with practical implementation, making it ideal for those pursuing careers in data analysis, research, or machine learning. Estimated time commitment is 8–12 hours per week.

Module 1: Introduction to Linear Regression

Estimated time: 6 hours

• Understanding the role of linear regression in data science
• Identifying dependent and independent variables
• Exploring simple linear regression with intuitive examples
• Fitting and interpreting a basic regression line

Module 2: Multiple Linear Regression

Estimated time: 10 hours

• Extending regression to multiple predictors
• Interpreting coefficients in multivariable models
• Understanding confounding variables
• Modeling interactions between predictors

Module 3: Statistical Inference and Model Interpretation

Estimated time: 10 hours

• Hypothesis testing in regression contexts
• Constructing and interpreting confidence intervals
• Understanding p-values and R-squared
• Evaluating overall model fit and significance

Module 4: Model Diagnostics and Practical Applications

Estimated time: 10 hours

• Assessing regression assumptions (linearity, independence, normality, homoscedasticity)
• Diagnosing multicollinearity and heteroscedasticity
• Analyzing residuals to validate models
• Applying regression to real-world data analysis problems

Module 5: Strengthening Statistical Reasoning

Estimated time: 6 hours

• Interpreting regression outputs for decision-making
• Recognizing limitations and potential biases in models
• Communicating results effectively to non-technical audiences

Module 6: Final Project

Estimated time: 8 hours

• Apply linear regression to a real-world dataset
• Interpret coefficients, test hypotheses, and assess model assumptions
• Submit a report summarizing findings and practical implications

Prerequisites

• Basic understanding of high school algebra
• Familiarity with fundamental statistical concepts (mean, standard deviation, correlation)
• No prior programming experience required, but comfort with quantitative reasoning is recommended

What You'll Be Able to Do After

• Build and interpret simple and multiple linear regression models
• Perform statistical inference using regression output
• Diagnose and address common model assumption violations
• Apply regression techniques to real-world data analysis tasks
• Strengthen foundational skills for advanced data science and machine learning courses