#1 Specialization

Launch Your Career in Data Science. A nine-course introduction to data science, developed and taught by leading professors.

Johns Hopkins University

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.

- 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

StatisticsR ProgrammingData AnalysisType I And Type Ii Errors

Section

This week, we'll focus on the fundamentals including probability, random variables, expectations and more. ...

10 videos (Total 64 min), 11 readings, 6 quizzes

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

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

Quiz 112m

Section

We're going to tackle variability, distributions, limits, and confidence intervals....

10 videos (Total 76 min), 4 readings, 4 quizzes

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

Variability10m

Distributions10m

Asymptotics10m

Practical R Exercises in swirl Part 210m

Quiz 216m

Section

We will be taking a look at intervals, testing, and pvalues in this lesson....

11 videos (Total 83 min), 5 readings, 4 quizzes

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

Confidence intervals10m

Hypothesis testing10m

P-values10m

Knitr10m

Practical R Exercises in swirl Part 310m

Quiz 314m

Section

We will begin looking into power, bootstrapping, and permutation tests....

9 videos (Total 86 min), 4 readings, 5 quizzes

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

Power10m

Resampling10m

Practical R Exercises in swirl Part 410m

Post-Course Survey10m

Quiz 418m

4.1

started a new career

got a tangible career benefit from this course

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

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....

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