4.6

stars

561 ratings

Brian Caffo, PhD +2 more instructors

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Data Science: Statistics and Machine Learning SpecializationJohns Hopkins University

What you will learn

Perform regression analysis, least squares and inference using regression models.
Build and apply prediction functions
Develop public data products
Understand the process of drawing conclusions about populations or scientific truths from data

Skills you will gain

About this Specialization

9,849 recent views

Build models, make inferences, and deliver interactive data products. This specialization continues and develops on the material from the Data Science: Foundations using R specialization. It covers statistical inference, regression models, machine learning, and the development of data products. In the Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, learners will have a portfolio demonstrating their mastery of the material. The five courses in this specialization are the very same courses that make up the second half of the Data Science Specialization. This specialization is presented for learners who have already mastered the fundamentals and want to skip right to the more advanced courses.

Applied Learning Project

Each course in this Data Science: Statistics and Machine Learning Specialization includes a hands-on, peer-graded assignment. To earn the Specialization Certificate, you must successfully complete the hands-on, peer-graded assignment in each course, including the final Capstone Project.

Shareable Certificate

Earn a Certificate upon completion

100% online courses

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Approximately 6 months to complete

Suggested pace of 6 hours/week

English

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Shareable Certificate

Earn a Certificate upon completion

100% online courses

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Approximately 6 months to complete

Suggested pace of 6 hours/week

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

There are 5 Courses in this Specialization

Course1

Course 1

Statistical Inference

4.2

stars

4,392 ratings

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.

Course2

Course 2

Regression Models

4.4

stars

3,319 ratings

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Course3

Course 3

Practical Machine Learning

4.5

stars

3,219 ratings

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Course4

Course 4

Developing Data Products

4.6

stars

2,248 ratings

A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.

Instructors

Offered by

Johns Hopkins University

Industry Partners

Frequently Asked Questions

What is the refund policy?
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.
Can I just enroll in a single course?
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.
Is financial aid available?
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.
Can I take the course for free?
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.
Is this course really 100% online? Do I need to attend any classes in person?
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.
How long does it take to complete the Specialization?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 3-6 months.
What background knowledge is necessary?
Learners should complete the Data Science: Foundations using R specialization to gain the right foundation before starting this course.
Do I need to take the courses in a specific order?
After completing the Data Science: Foundations using R specialization, the courses in this specialization may be taken in any order, and in parallel if desired.
Will I earn university credit for completing the Specialization?
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.
What will I be able to do upon completing the Specialization?
You’ll have a foundational understanding of the field and be prepared to continue studying data science.
Can I sign up for the course without paying or applying for financial aid?
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.

More questions? Visit the Learner Help Center.

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Data Science: Statistics and Machine Learning Specialization

What you will learn

Skills you will gain

About this Specialization

Applied Learning Project