This course is part of the Advanced Statistics for Data Science Specialization

Brian Caffo, PhD

Offered By

Advanced Statistics for Data Science SpecializationJohns Hopkins University

About this Course

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This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

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Course 1 of 4 in the

Advanced Statistics for Data Science Specialization

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Approx. 13 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish

Skills you will gain

StatisticsConfidence IntervalStatistical Hypothesis TestingBiostatistics

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 4 in the

Advanced Statistics for Data Science Specialization

Flexible deadlines

Reset deadlines in accordance to your schedule.

Approx. 13 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish

Instructor

Instructor rating

4.45/5 (25 Ratings)

Brian Caffo, PhD

Professor, Biostatistics

Bloomberg School of Public Health

1,290,378 Learners

21 Courses

Offered by

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.

Syllabus - What you will learn from this course

Content Rating94%(2,386 ratings)

Week

Week 1

4 hours to complete

Introduction, Probability, Expectations, and Random Vectors

You are about to undergo an intense and demanding immersion into the world of mathematical biostatistics. Over the next few weeks, you will learn about probability, expectations, conditional probabilities, distributions, confidence intervals, bootstrapping, binomial proportions, and much more. Module 1 covers experiments, probability, variables, mass functions, density functions, cumulative distribution functions, expectations, variations, and vectors.

4 hours to complete

13 videos (Total 179 min), 2 readings, 2 quizzes

13 videos

Welcome to the Course1m

Biostatistics and Experiments12m

Set Notation and Probability14m

Probability22m

Random Variables6m

PMFs and PDFs17m

CDFs, Survival Functions, and Quantiles11m

Expected Values12m

Rules About Expected Values10m

Variances and Chebyshev's Inequality18m

Random Vectors and Independence17m

Correlation10m

Variance Properties and Sample Variance22m

2 readings

Syllabus10m

Faculty10m

2 practice exercises

Module 1 Homework30m

Module 1 Quiz: Introduction, Probability, Expectations, and Random Vectors30m

Week

Week 2

4 hours to complete

Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics

This module covers Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics. These are the most fundamental core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.

4 hours to complete

7 videos (Total 157 min)

7 videos

Conditional Probabilities and Densities14m

Bayes' Rule and DLRs23m

Likelihood32m

Bernoulli Distribution and Binomial Trials15m

The Normal Distribution28m

Limits and LLN18m

CLT and Confidence Intervals23m

2 practice exercises

Module 2 Homework30m

Module 2 Quiz: Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics30m

Week

Week 3

3 hours to complete

Confidence Intervals, Bootstrapping, and Plotting

This module covers Confidence Intervals, Bootstrapping, and Plotting. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.

3 hours to complete

7 videos (Total 121 min)

7 videos

Confidence Intervals and CI for Normal Variance15m

Student's t Distribution and CI for Normal Means19m

Profile Likelihoods8m

T Confidence Intervals24m

Plotting22m

The Jackknife12m

Bootstrapping17m

2 practice exercises

Module 3 Homework30m

Module 3 Quiz: Confidence Intervals, Bootstrapping, and Plotting30m

Week

Week 4

2 hours to complete

Binomial Proportions and Logs

This module covers Binomial Proportions and Logs. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.

2 hours to complete

3 videos (Total 71 min)

3 videos

Binomial Proportions Part A6m

Binomial Proportions Part B36m

Logs27m

2 practice exercises

Module 4 Homework30m

Module 4 Quiz: Binomial Proportions and Logs30m

About the Advanced Statistics for Data Science Specialization

Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression.

This specialization starts with Mathematical Statistics bootcamps, specifically concepts and methods used in biostatistics applications. These range from probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling. This specialization also linear models for data science, starting from understanding least squares from a linear algebraic and mathematical perspective, to statistical linear models, including multivariate regression using the R programming language. These courses will give learners a firm foundation in the linear algebraic treatment of regression modeling, which will greatly augment applied data scientists' general understanding of regression models. This specialization requires a fair amount of mathematical sophistication. Basic calculus and linear algebra are required to engage in the content.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.

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