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

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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

About this Course

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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

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.

Coursera Labs

Includes hands on learning projects.

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Course 1 of 3 in the

Statistical Modeling for Data Science Applications Specialization

Intermediate Level

Calculus, linear algebra, and probability theory.

Approx. 45 hours to complete

English

Skills you will gain

Linear Model
R Programming
Statistical Model
regression

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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 3 in the

Statistical Modeling for Data Science Applications Specialization

Intermediate Level

Calculus, linear algebra, and probability theory.

Approx. 45 hours to complete

English

Instructor

Instructor rating

4.71/5 (7 Ratings)

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 course 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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Syllabus - What you will learn from this course

Week1

Week 1

8 hours to complete

Introduction to Statistical Models

8 hours to complete

8 videos (Total 82 min), 2 readings, 5 quizzes

8 videos

Frameworks and Goals of Statistical Modeling14mThe Assumption of Concept Validity7mThe Linear Regression Model11mMatrix Representation of the Linear Regression Model15mAssumptions of Linear Regression9mThe Appropriateness of Linear Regression11mInterpreting the Linear Regression Model I7mInterpreting the Linear Regression Model II5m

2 readings

Welcome and Where to Find Help 10mInformation on Peer Reviews10m

2 practice exercises

Introduction to Statistical Modeling30mThe Linear Regression Model30m

Week2

Week 2

8 hours to complete

Linear Regression Parameter Estimation

8 hours to complete

9 videos (Total 134 min)

9 videos

Introduction to Least Squares12mLinear Algebra for Least Squares9mDeriving the Least Squares Solution20mRegression Modeling in R: a First Pass19mJustifying Least Squares: the Gauss-Markov Theorem and Maximum Likelihood Estimation13mSums of Squares and Estimating the Error Variance19mThe Coefficient of Determination9mThe Problem of Non-identifiabiliity6mRegression Modeling in R: a Second Pass22m

2 practice exercises

Least Squares30mVariability and Identifiability in Regression Models30m

Week3

Week 3

9 hours to complete

Inference in Linear Regression

9 hours to complete

8 videos (Total 121 min), 1 reading, 5 quizzes

8 videos

Motivating Statistical Inference in the Linear Regression Context9mThe Sampling Distribution of the Least Squares Estimator23mT-Tests for Individual Regression Parameters14mT-Tests in R20mMotivating the F-Test: Multiple Statistical Comparisons8mThe F-Test22mThe F-Test in R10mConfidence Intervals in the Regression ContextConfidence Intervals in the Regression Context11m

1 reading

Ethics in Statistical Practice and Communication: Five Recommendations30m

2 practice exercises

Statistical Inference: Intro and T-Tests30mStatistical Inference: the F-tests and Confidence Intervals30m

Week4

Week 4

6 hours to complete

Prediction and Explanation in Linear Regression Analysis

6 hours to complete

6 videos (Total 82 min)

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.

More questions? Visit the Learner Help Center.

Modern Regression Analysis in R

About this Course

Skills you will gain