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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.

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Data Science: Statistical Inference and Modeling

This course includes

8 Weeks

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

12,292

Audit For Free

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

Statistical Inference
Data Modeling
Bayesian Statistics
R Programming
Election Forecasting
Confidence Intervals
Predictive Analytics
Data Analysis

This course includes:

PreRecorded video

Graded assignments, exams

Access on Mobile, Tablet, Desktop

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

Senior Lecturer in Public Leadership at Harvard Kennedy School

Ronald Heifetz is among the world's foremost authorities on the practice and teaching of leadership. He speaks extensively and advises heads of governments, businesses, and nonprofit organizations across the globe. In 2016, President Juan Manuel Santos of Colombia highlighted Heifetz's advice in his Nobel Peace Prize Lecture. Heifetz founded the Center for Public Leadership at Harvard Kennedy School where he has taught for nearly four decades. He is the King Hussein bin Talal Senior Lecturer in Public Leadership. Heifetz played a pioneering role in establishing leadership as an area of study and education in the United States and at Harvard. His research addresses two challenges: developing a conceptual foundation for the analysis and practice of leadership; and developing transformative methods for leadership education, training, and consultation. Heifetz co-developed the adaptive leadership framework with Riley Sinder and Marty Linsky to provide a basis for leadership research and practice. His first book, Leadership Without Easy Answers (1994), is a classic in the field and one of the ten most assigned course books at Harvard and Duke Universities. Heifetz co-authored the best-selling Leadership on the Line: Staying Alive through the Dangers of Change with Marty Linsky, which serves as one of the primary go-to books for practitioners across sectors (2002, revised 2017). He then co-authored the field book, The Practice of Adaptive Leadership: Tools and Tactics for Changing your Organization and the World with Alexander Grashow and Marty Linsky (2009). Heifetz began his focus on transformative methods of leadership education and development in 1983. Drawing students from throughout Harvard's graduate schools and neighboring universities, his courses on leadership are legendary; his core course is considered the most influential in their career by Kennedy School alumni. His teaching methods have been studied extensively in doctoral dissertations and in Leadership Can Be Taught by Sharon Daloz Parks (2005). A graduate of Columbia University, Harvard Medical School, and the Kennedy School, Heifetz is both a physician and cellist. He trained initially in surgery before deciding to devote himself to the study of leadership in public affairs, business, and nonprofits. Heifetz completed his medical training in psychiatry, which provided a foundation to develop more powerful teaching methods and gave him a distinct perspective on the conceptual tools of political psychology and organizational behavior. As a cellist, he was privileged to study with the great Russian virtuoso, Gregor Piatigorsky.

Data Science: Statistical Inference and Modeling

This course includes

8 Weeks

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

12,292

Audit For Free

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