Multivariate statistics is a critical field in data science and statistics as it explores how more than one variable are related. Whether you are just starting out on your statistics journey or are delving into the intricacies of multivariate techniques, massive open online courses offer free resources to help you master these skills. This article highlights the best free multivariate statistics MOOCs available.
1. Statistical Learning – StanfordOnline
Stanford University’s course on Statistical Learning, available on EdX, provides a deep dive into the principles of statistical learning with a focus on statistical learning. The course is designed by leading experts in the field and introduces a broad range of topics, including regression, classification, resampling, and tree-based methods.
The course includes video lectures and hands-on programming experience. The course also relies on a free textbook which is made available to all learners.
There are two versions of this course offered, one in Python and another in R.
2. Exploratory Multivariate Data Analysis – France Universite Numerique
This intuition-based course on multivariate exploratory analysis is aimed at individuals who do not necessarily want to learn all the nuts and bolts of the mathematics behind statistics, but still need to be able to analyze data. The course topics include multidimensional visualization techniques, factor analysis, and factorial methods. The course includes programming exercises in R.
View the course landing page here.
3. Applied Multivariate Statistical Analysis – PennState
This graduate level course at Penn State covers a variety of multivariate statistical methods, including analysis of variance, dimension reduction, and classification methods. The course includes 14 modules and detailed notes are available for each. A unique feature of this course is that it includes detailed programming exercises in SAS, which is rare to find in free online courses.
The course notes are available here.
4. Statistical Learning Theory and Applications – MIT OpenCourseWare
This graduate level course from MIT OpenCourseWare includes lecture notes and problem sets to help solidify knowledge. The course covers a range of advanced topics, including multivariate approximation from sparse data, support vector machines, and feature selection. These topics are particularly beneficial for those working on computer vision, text classification, and bioinformatics as these fields are characterized by robust multivariate datasets.
The course materials are available here.
5. Multivariate Statistical Techniques – NPTEL
The National Programme on Technology Enhanced Learning (NPTEL) offers an advanced course on multivariate statistical techniques specifically aimed at engineering students. The video lectures for the course cover a wide range of topics, including estimation, inferential statistics, multivariate analysis of variance, and factor analysis.
The course is available to view here.
Summary
MOOCs are a great resource for anyone looking to learn more about statistics or polish up on previously learned skills. The courses discussed here cover a range of techniques in multivariate statistics at different levels. Exploring these resources can equip you with important skills applicable in a range of fields.
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