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 qui...
12 videos, 1 reading, 1 practice quiz
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 ...
8 videos, 1 practice quiz
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, tha...
7 videos, 1 practice quiz
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
Graded: Module 4 Quiz
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Coursework
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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.