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Statistical Modeling for Data Science Applications

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

Status: Research Design
Status: Statistical Hypothesis Testing
IntermediateSpecialization

Top reviews across Statistical Modeling for Data Science Applications

DK

Reviewed Apr 29, 2024

A lot of work with several peer reviews, but it get you into R for Regression Analysis. Well laid out course. need knowledge of Linear algrebra for this course.

ZC

Reviewed Jul 30, 2022

Great course. Really useful and practical, and the exercise is not too difficult.

LB

Reviewed Jan 23, 2026

Can speak highly enough of this professor. He is extremely knowledgeable and can convey concepts in one of the clearest ways I have ever seen in my academic career.

CT

Reviewed Jun 27, 2023

The pace of instruction is excellent and the assignments make it easy to translate theory to practice.

Learner reviews across Statistical Modeling for Data Science Applications

Showing: 10 of 10

David
Course: Modern Regression Analysis in R
1.0
Reviewed Apr 12, 2022Course: Modern Regression Analysis in R
Michael
Course: Modern Regression Analysis in R
5.0
Reviewed Aug 16, 2021Course: Modern Regression Analysis in R
Derek
Course: Modern Regression Analysis in R
5.0
Reviewed Apr 30, 2024Course: Modern Regression Analysis in R
Steve
Course: Modern Regression Analysis in R
2.0
Reviewed Feb 13, 2022Course: Modern Regression Analysis in R
matt
Course: Modern Regression Analysis in R
1.0
Reviewed Mar 4, 2026Course: Modern Regression Analysis in R
Najib
Course: Modern Regression Analysis in R
5.0
Reviewed Oct 1, 2021Course: Modern Regression Analysis in R
Edgar
Course: Modern Regression Analysis in R
5.0
Reviewed Jun 3, 2023Course: Modern Regression Analysis in R
Hidetake
Course: Modern Regression Analysis in R
5.0
Reviewed Sep 25, 2022Course: Modern Regression Analysis in R
sina
Course: Modern Regression Analysis in R
5.0
Reviewed Aug 12, 2022Course: Modern Regression Analysis in R
Patrick
Course: Modern Regression Analysis in R
4.0
Reviewed Aug 17, 2022Course: Modern Regression Analysis in R