About this Specialization

82,588 recent views
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
Learner Career Outcomes
33%
Started a new career after completing this specialization.
17%
Got a pay increase or promotion.
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
Approx. 7 months to complete
Suggested 3 hours/week
English
Subtitles: English, Korean
Learner Career Outcomes
33%
Started a new career after completing this specialization.
17%
Got a pay increase or promotion.
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
Approx. 7 months to complete
Suggested 3 hours/week
English
Subtitles: English, Korean

There are 5 Courses in this Specialization

Course1

Course 1

Introduction to Probability and Data with R

4.7
stars
4,215 ratings
988 reviews
Course2

Course 2

Inferential Statistics

4.8
stars
1,846 ratings
345 reviews
Course3

Course 3

Linear Regression and Modeling

4.7
stars
1,345 ratings
238 reviews
Course4

Course 4

Bayesian Statistics

3.9
stars
696 ratings
220 reviews

Offered by

Duke University logo

Duke University

Frequently Asked Questions

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

  • 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.

  • 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. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • Basic math, no programming experience required. A genuine interest in data analysis is a plus!

    In the later courses in the Specialization, we assume knowledge and skills equivalent to those which would have been gained in the prior courses (for example: if you decide to take course four, Bayesian Statistics, without taking the prior three courses we assume you have knowledge of frequentist statistics and R equivalent to what is taught in the first three courses).

  • Yes.

  • Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

  • In this specialization, R is a requirement, and the labs have been enhanced and revised from the previous course. Success in the fourth course and the capstone project will depend heavily on successfully completing the first three courses in this specialization. Therefore, we require all students complete all courses to obtain the certificate.

  • Yes. You will need R and RStudio. Both are free and publicly available. You will need administrator access to your computer to install this software.

More questions? Visit the Learner Help Center.