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February 7, 2025
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Launch Your Career in Data Science. A ten-course introduction to data science, developed and taught by leading professors.
Instructors: Roger D. Peng, PhD
495,798 already enrolled
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(38,771 reviews)
Recommended experience
Beginner level
You should have beginner level experience in Python. Familiarity with regression is recommended
(38,771 reviews)
Recommended experience
Beginner level
You should have beginner level experience in Python. Familiarity with regression is recommended
Use R to clean, analyze, and visualize data.
Navigate the entire data science pipeline from data acquisition to publication.
Use GitHub to manage data science projects.
Perform regression analysis, least squares and inference using regression models.
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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.
Set up R, R-Studio, Github and other useful tools
Understand the data, problems, and tools that data analysts use
Explain essential study design concepts
Create a Github repository
Understand critical programming language concepts
Configure statistical programming software
Make use of R loop functions and debugging tools
Collect detailed information using R profiler
Understand common data storage systems
Apply data cleaning basics to make data "tidy"
Use R for text and date manipulation
Obtain usable data from the web, APIs, and databases
Understand analytic graphics and the base plotting system in R
Use advanced graphing systems such as the Lattice system
Make graphical displays of very high dimensional data
Apply cluster analysis techniques to locate patterns in data
Organize data analysis to help make it more reproducible
Write up a reproducible data analysis using knitr
Determine the reproducibility of analysis project
Publish reproducible web documents using Markdown
Understand the process of drawing conclusions about populations or scientific truths from data
Describe variability, distributions, limits, and confidence intervals
Use p-values, confidence intervals, and permutation tests
Make informed data analysis decisions
Use regression analysis, least squares and inference
Understand ANOVA and ANCOVA model cases
Investigate analysis of residuals and variability
Describe novel uses of regression models such as scatterplot smoothing
Use the basic components of building and applying prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Develop basic applications and interactive graphics using GoogleVis
Use Leaflet to create interactive annotated maps
Build an R Markdown presentation that includes a data visualization
Create a data product that tells a story to a mass audience
Create a useful data product for the public
Apply your exploratory data analysis skills
Build an efficient and accurate prediction model
Produce a presentation deck to showcase your findings
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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Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 3-6 months.
Each course in the Specialization is offered monthly.
Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required).
Begin by taking The Data Scientist's Toolbox and Introduction to R Programming, in order. The other courses may be taken in any order, and in parallel if desired.
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.
You’ll have a foundational understanding of the field and be prepared to continue studying data science.
Yes, you can access the course for free via www.coursera.org/jhu. This will allow you to explore the course, watch lectures, and participate in discussions for free. To be eligible to earn a certificate, you must either pay for enrollment or qualify 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.
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. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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.
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