Statistical Models

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Designing, Running, and Analyzing Experiments (Coursera)

You may never be sure whether you have an effective user experience until you have tested it with users. In this course, you’ll learn how to design user-centered experiments, how to run such experiments, and how to analyze data from these experiments in order to evaluate and validate user [...]

Modern Regression Analysis in R (Coursera)

This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. [...]

Fitting Statistical Models to Data with Python (Coursera)

In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our [...]

Bayesian Statistics: Mixture Models (Coursera)

Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a [...]

Advanced Machine Learning (FutureLearn)

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Advanced Machine Learning (FutureLearn)
Course Auditing
Improve your understanding of machine learning. Explore advanced techniques and how to use them in your data science projects. Discover and apply advanced statistical machine learning techniques. This online course explores advanced statistical machine learning.

Distributed Machine Learning with Apache Spark (edX)

Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark. Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability and optimization.