Statistical Inference

Johns Hopkins University

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.

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.

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 https://d396qusza40orc.cloudfront.net/rprog/doc/JHDSS_CourseDependencies.pdf.

Suggested Readings

There's a LeanPub book for the course here: https://leanpub.com/LittleInferenceBook that can be read for free here https://leanpub.com/LittleInferenceBook/read

In addition 

Course Format

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

FAQ

Will there be more Data Science Specialization sessions after December 2015?
Yes, the specialization is moving to the new Coursera platform in January 2016.

Will my current Data Science Specialization progress carry over to the new platform?

Yes, the certificates you earned in the current platform will still be valid after the move to the new platform in January 2016.

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 https://d396qusza40orc.cloudfront.net/rprog/doc/JHDSS_CourseDependencies.pdf.


Dates:
  • 7 December 2015, 4 weeks
  • 2 November 2015, 4 weeks
  • 5 October 2015, 4 weeks
  • 7 September 2015, 4 weeks
  • 3 August 2015, 4 weeks
  • 6 July 2015, 4 weeks
  • 1 June 2015, 4 weeks
  • 4 May 2015, 4 weeks
  • 6 April 2015, 4 weeks
  • 2 March 2015, 4 weeks
  • 2 February 2015, 4 weeks
  • 5 January 2015, 4 weeks
  • 1 December 2014, 4 weeks
  • 3 November 2014, 4 weeks
  • 6 October 2014, 4 weeks
  • 1 September 2014, 4 weeks
  • 4 August 2014, 4 weeks
  • 7 July 2014, 4 weeks
  • 2 June 2014, 4 weeks
  • 5 May 2014, 4 weeks
  • 1 March 2014, 4 weeks
  • Date to be announced, 4 weeks
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Course properties:
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  • Paid:
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  • MOOC:
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  • Language: English Gb

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