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.


会期:
  • 2015年12月07日, 4 星期
  • 2015年11月02日, 4 星期
  • 2015年10月05日, 4 星期
  • 2015年9月07日, 4 星期
  • 2015年8月03日, 4 星期
  • 2015年7月06日, 4 星期
  • 2015年6月01日, 4 星期
  • 2015年5月04日, 4 星期
  • 2015年4月06日, 4 星期
  • 2015年3月02日, 4 星期
  • 2015年2月02日, 4 星期
  • 2015年1月05日, 4 星期
  • 2014年12月01日, 4 星期
  • 2014年11月03日, 4 星期
  • 2014年10月06日, 4 星期
  • 2014年9月01日, 4 星期
  • 2014年8月04日, 4 星期
  • 2014年7月07日, 4 星期
  • 2014年6月02日, 4 星期
  • 2014年5月05日, 4 星期
  • 2014年3月01日, 4 星期
  • 日期还没有被通知, 4 星期
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  • 细节问题没有被通知
  • 细节问题没有被通知
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  • 语言: 英语 Gb

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