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Statistical Inference

Part of the Data Science Specialization »

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.


Eligible for

Data Science Specialization
Course Certificate

Course at a Glance

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.

Course Syllabus

In this class students will learn the fundamentals of statistical inference. Students will receive a broad overview of the goals, assumptions and modes of performing statistical inference. Students will be able to perform inferential tasks in highly targeted settings and will be able to use  the skills developed as a roadmap for more complex inferential challenges.

Recommended Background

R programming, mathematical aptitude. As part of the Data Science specialization, students should refer to the set of course dependencies here

Suggested Readings

There's a LeanPub book for the course here: that can be read for free here

In addition 

Course Format

Weekly lecture videos and quizzes and a final peer-assessed project.


Will I get a Statement of Accomplishment after completing this class?
Free statements of accomplishment are not offered in this course. If you are not enrolled in Signature Track, participation and performance documentation will be reported on your Accomplishments page, but you will not receive a signed statement of accomplishment.

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