Statistical modeling is a critical skill for any analyst and is applicable across a range of fields from business and healthcare to the social sciences and engineering. There are many free courses available online to provide a comprehensive education in statistical modeling and help anyone from beginner to advanced refine their techniques. Here are five free courses offering a mix of theoretical knowledge and practical skills to learn statistical modeling.
Data Science Inference and Modeling – Harvard University
Harvard provides a Professional Certificate in Data Science and offers access to some of the courses for free. One of these courses is Inference and Modeling which explores these topics with real-world applications. It includes discussions of confidence intervals, p-values, Bayesian statistics, and estimation. The course is taught in R and includes a project to build an election forecast model.
Learn more about the course here.
Statistical Modeling for Data Science – University of Colorado Boulder
This data science specialization program actually includes three courses: Modern Regression Analysis in R, ANOVA and Experimental Design, and Generalized Linear Models and Nonparametric Regression. Together, the courses offer a comprehensive understanding of intermediate and advanced statistical modeling techniques. The courses utilize real-world data and program exclusively in R.
View the course page here.
Predictive Modeling, Model Fitting, and Regression Analysis – University of California, Irvine
This course provides a survey of different predictive modeling techniques, including both supervised and unsupervised learning. Topics include model fitting, training, and classification, and the course ends with a linear regression model project. It provides a basic, entry-level understanding of these topics and is well suited for someone with some prior knowledge of statistics.
Learn more about the course here.
Statistical Learning with Python – Stanford Online
This introductory-level course focuses on supervised learning, including regression and classification model development. Topics include linear and polynomial regression, logistic regression, tree-based model development, support vector machines, and neural networks. The course is focused on programming Python, but edX also provides a version in R. The course does not rely on complex mathematics, making it a good choice for anyone wanting to learn modern statistical techniques at the entry level.
For more details, visit the course page here.
Statistical Inference and Modeling for High-throughout Experiments – Harvard University
Some fields, like bioinformatics, generate very large volumes of data from thousands or even millions of experiments conducted simultaneously. This scale of data generation requires specialized statistical techniques to analyze and interpret the results effectively. This course from Harvard University explores these unique methods, including multiple testing, false discovery rate, and q-values. It is best suited for those with a background in statistical modeling, and the course is conducted entirely in R.
View more course information here.
Summary
Each of these courses covers a unique set of skills and techniques used for statistical modeling. Taking advantage of these free resources can help you build a solid foundation in statistical modeling and stay ahead in the rapidly changing landscape of data analytics and decision-making.
