About this Course
4.2
2,639 ratings
•
549 reviews
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....
Stacks

Course 6 of 10 in the

Data Science Specialization
Globe

100% online courses

Start instantly and learn at your own schedule.
Calendar

Flexible deadlines

Reset deadlines in accordance to your schedule.
Clock

Suggested: 5 hours/week

Approx. 16 hours to complete
Comment Dots

English

Subtitles: English

What you will learn

  • Check
    Describe variability, distributions, limits, and confidence intervals
  • Check
    Make informed data analysis decisions
  • Check
    Understand the process of drawing conclusions about populations or scientific truths from data
  • Check
    Use p-values, confidence intervals, and permutation tests

Skills you will gain

Statistical Hypothesis TestingStatisticsStatistical InferenceHypothesis Testing
Stacks

Course 6 of 10 in the

Data Science Specialization
Globe

100% online courses

Start instantly and learn at your own schedule.
Calendar

Flexible deadlines

Reset deadlines in accordance to your schedule.
Clock

Suggested: 5 hours/week

Approx. 16 hours to complete
Comment Dots

English

Subtitles: English

Syllabus - What you will learn from this course

1

Section
Clock
18 hours to complete

Week 1: Probability & Expected Values

This week, we'll focus on the fundamentals including probability, random variables, expectations and more. ...
Reading
10 videos (Total 64 min), 11 readings, 6 quizzes
Video10 videos
Introductory video7m
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
Reading11 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
Quiz1 practice exercise
Quiz 112m

2

Section
Clock
11 hours to complete

Week 2: Variability, Distribution, & Asymptotics

We're going to tackle variability, distributions, limits, and confidence intervals....
Reading
10 videos (Total 76 min), 4 readings, 4 quizzes
Video10 videos
05 01 Introduction to variability4m
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
Reading4 readings
Variability10m
Distributions10m
Asymptotics10m
Practical R Exercises in swirl Part 210m
Quiz1 practice exercise
Quiz 216m

3

Section
Clock
11 hours to complete

Week: Intervals, Testing, & Pvalues

We will be taking a look at intervals, testing, and pvalues in this lesson....
Reading
11 videos (Total 83 min), 5 readings, 4 quizzes
Video11 videos
08 01 T confidence intervals9m
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
Reading5 readings
Confidence intervals10m
Hypothesis testing10m
P-values10m
Knitr10m
Practical R Exercises in swirl Part 310m
Quiz1 practice exercise
Quiz 314m

4

Section
Clock
13 hours to complete

Week 4: Power, Bootstrapping, & Permutation Tests

We will begin looking into power, bootstrapping, and permutation tests....
Reading
9 videos (Total 86 min), 4 readings, 5 quizzes
Video9 videos
11 01 Power4m
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
Reading4 readings
Power10m
Resampling10m
Practical R Exercises in swirl Part 410m
Post-Course Survey10m
Quiz1 practice exercise
Quiz 418m
4.2
Direction Signs

50%

started a new career after completing these courses
Briefcase

83%

got a tangible career benefit from this course

Top Reviews

By AP•Mar 22nd 2017

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.

By LH•Jan 31st 2016

I found this course really good introduction to statistical inference. I did find it quite challenging but I can go away from this course having a greater understanding of Statistical Inference

Instructors

Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Jeff Leek, PhD

Associate Professor, Biostatistics
Bloomberg 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....
Data Science

Frequently Asked Questions

  • 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.

  • 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.

More questions? Visit the Learner Help Center.

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