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Data Science: Linear Regression is an introductory online course offered by the Harvard T.H. Chan School of Public Health and delivered through edX. The course teaches learners how to implement linear regression models using R, understand confounding, and analyze relationships between variables. Through practical case studies, including sports analytics inspired by Moneyball, participants gain hands-on experience applying statistical modeling techniques to real-world datasets.
This course enables learners to:
Understand the historical development of linear regression and its statistical foundations
Detect and interpret confounding variables in data relationships
Implement linear regression models in R to analyze variable relationships
Apply regression techniques to practical scenarios and predictive analytics
Evaluate when regression is appropriate and understand its limitations
The 100% online, self-paced course runs for 8 weeks with an estimated time commitment of 1- 2 hours per week. The course is available free of charge (with optional paid certification) and is designed for beginners exploring data science fundamentals.
Foundations of Linear Regression: Learn statistical reasoning behind regression modeling
Confounding and Bias Detection: Understand hidden variables affecting relationships
Regression Implementation in R: Apply models using practical datasets
Case Study Applications: Explore predictive analysis through sports data examples
Practical Interpretation: Learn how to evaluate results and limitations
No prior advanced statistics experience required
Basic familiarity with programming concepts helpful but not mandatory
Suitable for beginners, students, and professionals entering data science
This course combines statistical theory with hands-on implementation using R, helping learners move beyond formulas to practical understanding. Real-world case studies demonstrate how regression informs decision-making in analytics-driven industries, making concepts easier to apply professionally.
Data Science: Linear Regression provides a strong foundation for understanding relationships between variables and making data-driven decisions. By combining statistical reasoning with applied coding experience, learners gain essential analytical skills relevant across industries.
