Statistics is the backbone of data science and machine learning. Whether you’re a beginner or an advanced learner, these online courses and resources from renowned universities and platforms can help you build a strong foundation. Below is a curated list of links to statistics courses, programs, and related materials.
University Courses and Departments
- MIT Machine Learning: Offers courses on machine learning with statistical underpinnings.
- UC Berkeley Statistics: Department page with course listings and resources.
- Stanford Statistics Courses: A collection of statistics courses from Stanford University.
- Duke University Statistics: Department resources and course information.
- Rice University Statistics: Statistics department with course offerings.
- Carnegie Mellon Statistics: CMU’s statistics department page.
- Oxford Statistics: University of Oxford’s statistics resources.
- WU Vienna Statistics Courses: Courses from Vienna University of Economics and Business.
Specific Course Links
- Duke STA 320: Introduction to Statistical Methods: An introductory course on statistical methods.
- Penn State STAT 509: Applied Data Mining and Statistical Learning: Focuses on data mining and statistical learning.
- Penn State Statistics Program: Overview of Penn State’s online statistics programs.
- Duke STA 114: Probability and Statistics for Economists: Tailored for economics students.
- Stanford STATS 311: Applied Statistics: Applied statistics course.
- Texas A&M STAT 613: Statistical Methods for Research Workers: Advanced statistical methods.
Additional Resources
- J.G. Scott’s Teaching Page: Resources from a statistics educator.
- Data Science Books by WZ Chen: Recommended books on data science and statistics.
- Rafael Irizarry’s Data Science Book: A comprehensive book on data science, including statistical concepts.
Tips for Learning Statistics
- Start with foundational courses like introductory probability and statistics before diving into advanced topics.
- Combine theory with practical applications, such as using R or Python for data analysis.
- Many of these resources are free or offer audit options; check for enrollment details.
- For machine learning, focus on courses that cover regression, hypothesis testing, and Bayesian methods.
If you’re new to statistics, consider supplementing these with interactive platforms like Coursera or edX, which often host these university courses. Happy learning!