Brief Introduction
Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.Course Summary
Learn how to use linear regression to analyze and predict real-world data in this Harvard online course. Gain practical skills and knowledge in statistical modeling and data analysis.Key Learning Points
- Understand the principles of linear regression and how to apply them to real-world data.
- Learn how to use Python and R for data analysis and modeling.
- Gain hands-on experience in statistical modeling and data analysis with real-world datasets.
Related Topics for further study
Learning Outcomes
- Understand the principles of linear regression and how they can be applied to real-world data
- Gain experience in using Python and R for data analysis and modeling
- Learn how to analyze and interpret data using statistical modeling techniques
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics and probability
- Familiarity with Python and R programming languages
Course Difficulty Level
IntermediateCourse Format
- Online, self-paced
- Video lectures
- Real-world datasets
- Hands-on exercises
Similar Courses
- Data Science: Machine Learning
- Data Science: Data Visualization
- Data Science: Probability
Related Education Paths
Related Books
Description
Course description
Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part of our Professional Certificate Program in Data Science, covers how to implement linear regression and adjust for confounding in practice using R.
In data science applications, it is very common to be interested in the relationship between two or more variables. The motivating case study we examine in this course relates to the data-driven approach used to construct baseball teams described in Moneyball. We will try to determine which measured outcomes best predict baseball runs by using linear regression.
We will also examine confounding, where extraneous variables affect the relationship between two or more other variables, leading to spurious associations. Linear regression is a powerful technique for removing confounders, but it is not a magical process. It is essential to understand when it is appropriate to use, and this course will teach you when to apply this technique.
Knowledge
- What you'll learn
- How linear regression was originally developed by Galton
- What is confounding and how to detect it
- How to examine the relationships between variables by implementing linear regression in R
Summary of User Reviews
Learn data science and linear regression with Harvard's online course. Users have praised the course for its comprehensive content, engaging instructors, and real-world examples. While some have noted the high price tag, many feel that the value is worth it.Key Aspect Users Liked About This Course
Real-world examplesPros from User Reviews
- Comprehensive content
- Engaging instructors
- Challenging assignments
- Interactive exercises
- Helpful community and support
Cons from User Reviews
- Expensive compared to other online courses
- Requires a significant time commitment
- May be too advanced for beginners
- Limited feedback on assignments
- Some technical difficulties with the online platform
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