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.
This course is part of the Data Science Specialization
Statistical Inference
Offered By
About this Course
4.2
3,142 ratings
What you will learn
Describe variability, distributions, limits, and confidence intervals
Make informed data analysis decisions
Understand the process of drawing conclusions about populations or scientific truths from data
Use p-values, confidence intervals, and permutation tests
Skills you will gain
StatisticsStatistical InferenceStatistical Hypothesis Testing
Syllabus - What you will learn from this course
Week
118 hours to complete
Week 1: Probability & Expected Values
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
10 videos (Total 64 min), 11 readings, 6 quizzes
10 videos
02 01 Introduction to probability6m
02 02 Probability mass functions7m
02 03 Probability density functions13m
03 01 Conditional Probability3m
03 02 Bayes' rule7m
03 03 Independence3m
04 01 Expected values5m
04 02 Expected values, simple examples2m
04 03 Expected values for PDFs7m
11 readings
Welcome to Statistical Inference10m
Some introductory comments10m
Pre-Course Survey10m
Syllabus10m
Course Book: Statistical Inference for Data Science10m
Data Science Specialization Community Site10m
Homework Problems10m
Probability10m
Conditional probability10m
Expected values10m
Practical R Exercises in swirl 110m
1 practice exercise
Quiz 112m
Week
211 hours to complete
Week 2: Variability, Distribution, & Asymptotics
We're going to tackle variability, distributions, limits, and confidence intervals.
10 videos (Total 76 min), 4 readings, 4 quizzes
10 videos
05 02 Variance simulation examples2m
05 03 Standard error of the mean7m
05 04 Variance data example3m
06 01 Binomial distrubtion3m
06 02 Normal distribution15m
06 03 Poisson6m
07 01 Asymptotics and LLN4m
07 02 Asymptotics and the CLT8m
07 03 Asymptotics and confidence intervals20m
4 readings
Variability10m
Distributions10m
Asymptotics10m
Practical R Exercises in swirl Part 210m
1 practice exercise
Quiz 216m
Week
311 hours to complete
Week: Intervals, Testing, & Pvalues
We will be taking a look at intervals, testing, and pvalues in this lesson.
11 videos (Total 83 min), 5 readings, 4 quizzes
11 videos
08 02 T confidence intervals example4m
08 03 Independent group T intervals14m
08 04 A note on unequal variance3m
09 01 Hypothesis testing4m
09 02 Example of choosing a rejection region5m
09 03 T tests7m
09 04 Two group testing17m
10 01 Pvalues7m
10 02 Pvalue further examples5m
Just enough knitr to do the project3m
5 readings
Confidence intervals10m
Hypothesis testing10m
P-values10m
Knitr10m
Practical R Exercises in swirl Part 310m
1 practice exercise
Quiz 314m
Week
413 hours to complete
Week 4: Power, Bootstrapping, & Permutation Tests
We will begin looking into power, bootstrapping, and permutation tests.
9 videos (Total 86 min), 4 readings, 5 quizzes
9 videos
11 02 Calculating Power12m
11 03 Notes on power4m
11 04 T test power8m
12 01 Multiple Comparisons25m
13 01 Bootstrapping7m
13 02 Bootstrapping example3m
13 03 Notes on the bootstrap10m
13 04 Permutation tests9m
4 readings
Power10m
Resampling10m
Practical R Exercises in swirl Part 410m
Post-Course Survey10m
1 practice exercise
Quiz 418m
4.2
626 Reviews42%
started a new career after completing these courses
38%
got a tangible career benefit from this course
10%
got a pay increase or promotion
Top Reviews
Course is compressed with lots of statistical concepts. Which is very good as most must know concepts are imparted. Lots of extra reading is required to gain all insights. Very good motivating start .
The strategy for model selection in multivariate environment should have been explained with an example. This will make the model selection process, interaction and its interpretation more clear.
Instructors
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
About 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.
About the Data Science Specialization
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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