This course is part of the Statistical Modeling for Data Science Applications Specialization

Brian Zaharatos

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You can earn credits from the University of Colorado Boulder that count towards a Master of Science in Data Science

Statistical Modeling for Data Science Applications SpecializationUniversity of Colorado Boulder

About this Course

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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.
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

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Course 2 of 3 in the

Statistical Modeling for Data Science Applications Specialization

Intermediate Level

Calculus, linear algebra, and probability theory.

Approx. 40 hours to complete

English

Skills you will gain

Calculus
and probability theory.
Linear Algebra

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 3 in the

Statistical Modeling for Data Science Applications Specialization

Intermediate Level

Calculus, linear algebra, and probability theory.

Approx. 40 hours to complete

English

Instructor

Brian Zaharatos

Director, Professional Master’s Degree in Applied Mathematics

Department of Applied Mathematics

6,122 Learners

3 Courses

Offered by

University of Colorado Boulder

Start working towards your degree

This Course is part of an online degree program offered by the University of Colorado Boulder. When you enroll in a for-credit non-degree course through the university and complete it online, it counts as credit hours towards a degree at CU-Boulder. All you have to do is apply through the university.

Master of Science in Data Science

University of Colorado Boulder

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Syllabus - What you will learn from this course

Week

Week 1

11 hours to complete

Introduction to ANOVA and Experimental Design

11 hours to complete

9 videos (Total 87 min), 2 readings, 12 quizzes

9 videos

Introduction to Experimental Design10m

The One-Way ANOVA and ANCOVA Models6m

ANOVA Variance Decomposition8m

ANOVA Sums of Squares and the F-test14m

ANOVA and ANCOVA as Regression Models10m

One-Way ANOVA Interpretation in the Regression Context10m

The ANCOVA Model15m

ANCOVA with Interactions7m

ANCOVA with Interactions in R4m

2 readings

Welcome and Where to Find Help 10m

Information on Peer Reviews10m

9 practice exercises

Introduction to ANOVA and Experimental Design30m

The One-Way ANOVA and ANCOVA Models30m

ANOVA Variance Decomposition30m

ANOVA Sums of Squares and the F-Test30m

ANOVA and ANCOVA as Regression Models30m

One-Way ANOVA Interpretation in the Regression Context30m

The ANCOVA Model30m

ANCOVA with Interactions30m

ANCOVA with Interactions in R30m

Week

Week 2

9 hours to complete

Hypothesis Testing in the ANOVA Context

9 hours to complete

6 videos (Total 91 min), 2 readings, 6 quizzes

6 videos

Beyond the Full F-test12m

Planned Comparisons: Defining Contrasts16m

Planned Comparisons: Hypothesis Testing with Contrasts14m

Post Hoc Comparisons13m

Post Hoc Comparisons in R16m

Type II Error and Power in the ANOVA Context18m

2 readings

Patrizio E. Tressoldi and David Giofré: "The pervasive avoidance of prospective statistical power: major consequences and practical solutions"10m

Optional: Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors10m

4 practice exercises

Beyond the Full F-test30m

Planned Comparisons: Defining Contrasts30m

Planned and Unplanned Comparisons30m

Type II Error and Power in the ANOVA Context30m

Week

Week 3

9 hours to complete

Two-Way ANOVA and Interactions

9 hours to complete

7 videos (Total 79 min)

7 videos

Motivating the Two-way ANOVA Model10m

The two-way ANOVA model9m

The Two-way ANOVA Model as a Regression Model9m

Interaction Terms in the Two-way ANOVA Model: Definitions and Visualizations13m

Interactions in the Two-way ANOVA Model: Formal Tests15m

Two-way ANOVA Hypothesis Testing (no interaction)14m

Looking Ahead: Two-Way ANOVA and Experimental Design5m

6 practice exercises

Motivating the Two-way ANOVA Model30m

The Two-way ANOVA Model30m

The Two-way ANOVA Model as a Regression Model30m

Interaction Terms in the Two-way ANOVA Model: Definitions and Visualizations30m

Interactions in the Two-way ANOVA Model: Formal Tests30m

Two-way ANOVA Hypothesis Testing (no interaction)30m

Week

Week 4

10 hours to complete

Experimental Design: Basic Concepts and Designs

10 hours to complete

7 videos (Total 79 min), 2 readings, 7 quizzes

7 videos

The Conceptual Framework of Experimental Design19m

The Completely Randomized Design12m

The Randomized Complete Block Design (RCBD)8m

The Randomized Complete Block Design (RCBD): Hypothesis Testing8m

The Factorial Design10m

Further Issues in Experimental Design7m

Ethical Issues in Experimental Design12m

2 readings

Causation and Experimental Design10m

Resources on Ethics 10m

5 practice exercises

The Conceptual Framework of Experimental Design30m

The Completely Randomized Design30m

The Randomized Complete Block Design (RCBD)30m

The Factorial Design30m

Further Issues in Experimental Design30m

About the Statistical Modeling for Data Science Applications Specialization

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

Statistical Modeling for Data Science Applications

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. 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.

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ANOVA and Experimental Design

About this Course

Skills you will gain