RiseUpp Logo
Educator Logo

Linear Regression for Data Science: Implementation with R

Master linear regression techniques for data analysis using R, with practical applications in statistical modeling and confounding adjustment.

Master linear regression techniques for data analysis using R, with practical applications in statistical modeling and confounding adjustment.

This course provides a comprehensive introduction to linear regression in data science, focusing on implementing regression analysis using R programming. Students learn to quantify relationships between variables and adjust for confounding effects through practical examples, including a case study on baseball analytics inspired by Moneyball. The course covers the historical development of linear regression by Galton, techniques for detecting and managing confounding variables, and practical implementation strategies in R for data analysis.

4.1

(25 ratings)

29,590 already enrolled

Instructors:

English

Arabic, German, English, 9 more

Powered by

Provider Logo
Linear Regression for Data Science: Implementation with R

This course includes

8 Weeks

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

12,275

Audit For Free

What you'll learn

  • Understand the historical development of linear regression through Galton's work

  • Master the identification and management of confounding variables

  • Implement linear regression analysis using R programming

  • Apply statistical modeling techniques to real-world datasets

  • Interpret regression results and assess model validity

  • Develop practical data analysis skills for research and business applications

Skills you'll gain

Linear Regression
R Programming
Statistical Modeling
Data Analysis
Confounding Variables
Statistical Methods
Data Science
Research Methods

This course includes:

PreRecorded video

Graded assignments, exams

Access on Mobile, Tablet, Desktop

Limited Access access

Shareable certificate

Closed caption

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

Provided by

Certificate

Top companies offer this course to their employees

Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

icon-0icon-1icon-2icon-3icon-4

Module Description

This course offers a practical approach to understanding and implementing linear regression in data science applications. The curriculum covers fundamental concepts of linear regression, from its historical development to modern applications in data analysis. Students learn to identify and handle confounding variables, implement regression analysis in R, and interpret results effectively. Using real-world examples like baseball analytics, the course demonstrates how to apply linear regression techniques to solve practical problems and make data-driven decisions.

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.

Linear Regression for Data Science: Implementation with R

This course includes

8 Weeks

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

12,275

Audit For Free

Testimonials

Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.

Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.