Advanced Statistics for Data Science

Overview

Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization.  It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression.  

These range from probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling.

This Advanced Statistics for Data Science Specialization course offered by Coursera in partnership with Johns Hopkins University also linear models for data science, starting from understanding least squares from a linear algebraic and mathematical perspective, to statistical linear models, including multivariate regression using the R programming language.  These courses will give learners a firm foundation in the linear algebraic treatment of regression modeling, which will greatly augment applied data scientists' general understanding of regression models.

This specialization requires a fair amount of mathematical sophistication. Basic calculus and linear algebra are required to engage in the content. 

Applied Learning Project

The Advanced Statistics for Data Science Specialization incorporates a series of rigorous graded quizzes to test the understanding of key concepts such as probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling.