Build Your Statistical Skills for Data Science. Master the Statistics Necessary for Data Science

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Brian Zaharatos

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Part of the Master of Science in Data Science degree
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Statistical Modeling for Data Science Applications SpecializationUniversity of Colorado Boulder

What you will learn

Correctly analyze and apply tools of regression analysis to model relationship between variables and make predictions given a set of input variables.
Successfully conduct experiments based on best practices in experimental design.
Use advanced statistical modeling techniques, such as generalized linear and additive models, to model wide range of real-world relationships.

Skills you will gain

About this Specialization

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Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language. This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash

Applied Learning Project

Learners will master the application and implementation of statistical models through auto-graded and peer reviewed Jupyter Notebook assignments. In these assignments, learners will use real-world data and advanced statistical modeling techniques to answer important scientific and business questions.

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 4 months to complete

Suggested pace of 9 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 4 months to complete

Suggested pace of 9 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

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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 3 Courses in this Specialization

Course1

Course 1

Modern Regression Analysis in R

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This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash

Course2

Course 2

ANOVA and Experimental Design

This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, will provide the mathematical basis for designing experiments for data science applications. Emphasis will be placed on important design-related concepts, such as randomization, blocking, factorial design, and causality. Some attention will also be given to ethical issues raised in experimentation.

Course3

Course 3

Generalized Linear Models and Nonparametric Regression

In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools. Attention will also be given to ethical issues raised by using complicated statistical models.

Instructor

Brian Zaharatos

Director, Professional Master’s Degree in Applied Mathematics

Department of Applied Mathematics

6,624 Learners

3 Courses

Offered by

University of Colorado Boulder

Start working towards your Master's degree

This specialization is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

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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?
Approximately 15 weeks.
What background knowledge is necessary?
Students should be familiar with differential and integral calculus, basic linear algebra (including matrix operations, properties, and vector norms) and probability theory (including random variables, probability distributions, expectation, variance, covariance, and conditional probability).
Do I need to take the courses in a specific order?
Learners should take the first course (Modern Regression Analysis in R) first. Learners can take the next two courses in either order.
Will I earn university credit for completing the Specialization?
Statistical Modeling for Data Science Application is part of CU Boulder's Master of Science in Data Science (MS-DS) program. Learners enrolled in the degree program will earn three credits for successful completion of the specialization.
What will I be able to do upon completing the Specialization?
Learners will be able to answer important questions that arise in science and business by applying statistical models to data and interpreting their results.

More questions? Visit the Learner Help Center.

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Statistical Modeling for Data Science Applications Specialization

What you will learn

Skills you will gain

About this Specialization

Applied Learning Project