Statistical Inference

Learn how to draw conclusions about populations or scientific truths from data. This is the sixth course in the Johns Hopkins Data Science Course Track.

About The Course

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

Frequently Asked Questions

Will I get a Statement of Accomplishment after completing this class?
Yes. Students who successfully complete the class will receive a Statement of Accomplishment signed by the instructor.

What resources will I need for this class?
Students must have the latest version of R and RStudio installed.

How does this course fit into the Data Science Course Track?
This is the sixth course in the track. Although it isn't a requirement, we recommend that you first take The Data Scientist's Toolbox and R Programming. A full list of course dependencies can be found here https://d396qusza40orc.cloudfront.net/rprog/doc/JHDSS_CourseDependencies.pdf.


Recommended Background

R programming, mathematical aptitude. As part of the Data Science specialization, students should refer to the set of course dependencies here https://d396qusza40orc.cloudfront.net/rprog/doc/JHDSS_CourseDependencies.pdf.