About this course: Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.
Created by: Johns Hopkins University
Taught by: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Hypothesis Testing
In this module, you'll get an introduction to hypothesis testing, a core concept in statistics. We'll cover hypothesis testing for basic one and two group settings as well as power. After you've watched the videos and tried the homework, take a stab at the quiz.
12 videos, 1 reading, 1 practice quiz
- Reading: Syllabus
- Video: Hypothesis Testing
- Video: More Hypothesis Testing
- Video: General Rules of Hypothesis Testing
- Video: Two-sided Tests
- Video: Confidence Intervals & P Values
- Video: Power
- Video: Calculating Power
- Video: T Tests & Monte Carlo
- Video: Two Sample Tests - Matched Data I
- Video: Two Sample Tests - Matched Data II
- Video: Two Sample Tests - Regression to the Mean
- Video: Two Sample Tests - Two Independent Groups
- Practice Quiz: Module 1 Homework (Not counted toward final grade)
Graded: Module 1 Quiz
WEEK 2
Two Binomials
In this module we'll be covering some methods for looking at two binomials. This includes the odds ratio, relative risk and risk difference. We'll discussing mostly confidence intervals in this module and will develop the delta method, the tool used to create these confidence intervals. After you've watched the videos and tried the homework, take a crack at the quiz!
8 videos, 1 practice quiz
- Video: Two Sample Binomial Tests - Score Statistic
- Video: Two Sample Binomial Tests - Exact Tests
- Video: Two Sample Binomial Tests - Comparing 2 Binomial Proportions
- Video: Relative Risks & Odds Ratios - Relative Measures
- Video: Relative Risks & Odds Ratios - The Relative Risk
- Video: Relative Risks & Odds Ratios - The Odds Ratio
- Video: Delta Method
- Video: Delta Method & Derivation
- Practice Quiz: Module 2 Homework
Graded: Module 2 Quiz
WEEK 3
Discrete Data Settings
In this module, we'll discuss testing in discrete data settings. This includes the famous Fisher's exact test, as well as the many forms of tests for contingency table data. You'll learn the famous observed minus expected squared over the expected formula, that is broadly applicable.
7 videos, 1 practice quiz
- Video: Fisher's Exact Test
- Video: Hyper-Geometric Distribution
- Video: Fisher's Exact Text in Practice & Monte Carlo
- Video: Chi Squared Testing
- Video: Testing Independence
- Video: Generalization
- Video: Goodness of Fit Testing
- Practice Quiz: Module 3 Homework
Graded: Module 3 Quiz
WEEK 4
Techniques
This module is a bit of a hodge podge of important techniques. It includes methods for discrete matched pairs data as well as some classical non-parametric methods.
16 videos, 1 practice quiz
- Video: Simpson's Paradox
- Video: Simpson's Paradox, more examples
- Video: Weighting
- Video: CMH test
- Video: Case Control Sampling
- Video: Exact inference for The Odds Ratio
- Video: Matched 2x2 Tables
- Video: Dependence and Marginal Homogeneity
- Video: Estimation of the Marginal Difference in Proportions
- Video: Odds and Ends for Matched 2x2 Tables
- Video: the sign test
- Video: the sign rank test
- Video: the rank sum test
- Video: Poisson distribution
- Video: Poisson likelihood
- Video: Poisson P-value calculation
- Practice Quiz: Module 4 Homework
Graded: Module 4 Quiz
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Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Ratings and Reviews
This course should be part of the Data Science specialization. Actually, you can supplement the Statistical Inference course with these two Boot camp courses really well!
A great revision of statistics, very rigorous and thorough cover of all distributions and hypothesis tests.
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Thankful that a course like this exists, as most MOOCs are quite basic. And thanks to Coursera for running the courses even though attendance seems to be low (darn, that pesky calculus pre-requisite). Lecture quality is varied--some quite good (such as the lectures in Boot Camp I) and others seem like he hadn't looked at his notes for a long time. It's great to hear a stats professor talk about the strengths and weaknesses of many approaches. It complements a mathematical statistics book quite well. It would have been nice to have had some problems that were more challenging. Overall, while the Johns Hopkins Data Science MOOCs are pretty good, they are a bit more basic than what's available through MIT and Stanford.