FavoriteLoadingBookmark this content

Statistical Inference No ratings yet.

Link to Content:
Statistical Inference

Created/Published/Taught by:
Johns Hopkins University
Brian Caffo
Roger D. Peng
Jeff Leek

Content Found Via:

Free? Partially: Some Free Content, Some Paid

Cost: $0.00

Tags: / / / / /
Content Type: /

Difficulty Rating:

No ratings yet.

This is course 6 of 10 in the Coursera Data Science Specialization.

From Coursera:

“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 explicitly 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.


  • Week 1: Probability & Expected Values
  • Week2: Variability, Distribution, & Asymptotics
  • Week 3: Intervals, Testing, & Pvalues
  • Week 4: Power, Bootstrapping, & Permutation Tests

Recommended Prerequisites: Some programming experience

Go to Content: Statistical Inference