University of Colorado Boulder
Statistical Modeling for Data Science Applications Specialization
University of Colorado Boulder

Statistical Modeling for Data Science Applications Specialization

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

Taught in English

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Brian Zaharatos

Instructor: Brian Zaharatos

4,117 already enrolled

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Specialization - 3 course series

Get in-depth knowledge of a subject

4.2

(29 reviews)

Intermediate level

Recommended experience

3 months at 10 hours a week
Flexible schedule
Learn at your own pace
Progress towards a degree

What you'll 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.

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Specialization - 3 course series

Get in-depth knowledge of a subject

4.2

(29 reviews)

Intermediate level

Recommended experience

3 months at 10 hours a week
Flexible schedule
Learn at your own pace
Progress towards a degree

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Specialization - 3 course series

Modern Regression Analysis in R

Course 145 hours4.5 (24 ratings)

What you'll learn

  • Articulate some recommended practices for ethical behavior and communication in statistics and data science.

  • Interpret important components of the MLR model, including the “systematic” and “random” components of the model.

  • Describe and implement testing-based procedures for model selections and select a “best” model based on a given procedure.

Skills you'll gain

Category: Linear Model
Category: regression
Category: R Programming
Category: Statistical Model

ANOVA and Experimental Design

Course 240 hours3.9 (13 ratings)

What you'll learn

  • Identify and interpret the two-way ANOVA (and ANCOVA) model(s) as a linear regression model.

  • Use the two-way ANOVA and ANCOVA models to answer research questions using real data.

  • Define and apply the concepts of replication, repeated measures, and full factorial design in the context of two-way ANOVA.

Skills you'll gain

Category: Calculus
Category: and probability theory.
Category: Linear Algebra

Generalized Linear Models and Nonparametric Regression

Course 342 hours4.4 (14 ratings)

What you'll learn

  • Describe how to generalize the linear model framework to accommodate data that is not suitable for the standard linear regression model.

  • State some advantages and disadvantages of (generalized) additive models.

  • Describe how an additive model can be generalized to incorporate non-normal response variables (i.e., define a generalized additive model).

Skills you'll gain

Category: Calculus
Category: and probability theory.
Category: Linear Algebra

Instructor

Brian Zaharatos
University of Colorado Boulder
3 Courses10,466 learners

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