Data Science: Statistical Inference and Modeling
Master statistical inference and modeling through practical applications in election forecasting using R programming.
Master statistical inference and modeling through practical applications in election forecasting using R programming.
This comprehensive course explores statistical inference and modeling, essential tools for data scientists analyzing chance-affected data. Using election forecasting as a motivating case study, students learn to develop effective statistical approaches for polling and predictions. The curriculum covers fundamental concepts including estimates, margins of error, confidence intervals, and p-values, with practical implementation in R. Students explore Bayesian modeling for probability calculations and culminate their learning by recreating a simplified election forecast model based on the 2016 election.
4.4
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What you'll learn
Define and calculate estimates and margins of error for populations
Develop models to aggregate data from different sources
Understand and apply basic Bayesian statistics principles
Create predictive models using real-world election data
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
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Limited Access access
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Module Description
This introductory course provides a practical foundation in statistical inference and modeling for data science. Through a compelling case study of election forecasting, students learn how to develop and apply statistical approaches to real-world data analysis. The curriculum covers essential concepts including population parameters, estimates, margins of error, and standard errors. Students learn to aggregate data from multiple sources, understand Bayesian statistics basics, and develop predictive models. The course culminates in applying these skills to recreate a simplified election forecast model.
Fee Structure
Instructor
32 Courses
Harvard Biostatistics Professor and Genomics Data Analysis Pioneer
Rafael Irizarry is a distinguished Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and Professor of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute. His expertise spans genomics, data analysis, and the R programming language. Irizarry's career has been marked by significant contributions to the field of genomics data analysis over the past two decades
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4.4 course rating
41 ratings
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