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#### English

Subtitles: English, Vietnamese

### 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

#### 100% online

Start instantly and learn at your own schedule.

#### English

Subtitles: English, Vietnamese

### Syllabus - What you will learn from this course

Week
1
18 hours to complete

## Week 1: Probability & Expected Values

10 videos (Total 64 min), 11 readings, 6 quizzes
10 videos
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
Welcome to Statistical Inference10m
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
2
11 hours to complete

## Week 2: Variability, Distribution, & Asymptotics

10 videos (Total 76 min), 4 readings, 4 quizzes
10 videos
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
Variability10m
Distributions10m
Asymptotics10m
Practical R Exercises in swirl Part 210m
1 practice exercise
Quiz 216m
Week
3
11 hours to complete

## Week: Intervals, Testing, & Pvalues

11 videos (Total 83 min), 5 readings, 4 quizzes
11 videos
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
Confidence intervals10m
Hypothesis testing10m
P-values10m
Knitr10m
Practical R Exercises in swirl Part 310m
1 practice exercise
Quiz 314m
Week
4
13 hours to complete

## Week 4: Power, Bootstrapping, & Permutation Tests

9 videos (Total 86 min), 4 readings, 5 quizzes
9 videos
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
Power10m
Resampling10m
Practical R Exercises in swirl Part 410m
Post-Course Survey10m
1 practice exercise
Quiz 418m
4.2
649 Reviews

## 45%

started a new career after completing these courses

## 40%

got a tangible career benefit from this course

## 12%

got a pay increase or promotion

### Top reviews from Statistical Inference

By JAOct 26th 2018

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 .

By APMar 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.

## 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

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....