A comprehensive understanding of statistics and data analysis is paramount for the fields of data science, data analytics, and machine learning. In the first course of this specialization, you will learn traditional and applied statistics (descriptive statistics, probability, and discrete and continuous probability distributions) from ground zero (i.e., beginner level).
The second course is all about inferential statistics and making decisions (sampling distributions, one- and two-sample hypothesis tests, analysis of variance) and creating predictive mathematical models (linear and nonlinear regression). Throughout both of the first two courses, you will learn how to visualize data and solve various statistical problems using Microsoft Excel.
In the final course of the specialization, you will use the statistical computing software R (using RStudio) for statistical hypothesis tests, data visualization, and analysis of variance (ANOVA).
Applied Learning Project
Parts 1 and 2 of the specialization have “guided” workshop quizzes during each week of the course for learners to work along with the instructor through relevant and exciting statistics related problems. In Part 3 of the course, assignments are in-application (i.e., in the programming language R) and are submitted online.
