University of California, Irvine
Predictive Modeling, Model Fitting, and Regression Analysis
University of California, Irvine

Predictive Modeling, Model Fitting, and Regression Analysis

This course is part of Data Science Fundamentals Specialization

Taught in English

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Julie Pai

Instructor: Julie Pai

4,784 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.4

(59 reviews)

Intermediate level
Some related experience required
4 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • The application of predictive modeling to professional and academic work

  • Applications of classification analysis: decision trees

  • Applications of regression analysis (linear and logistic)

Details to know

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Assessments

2 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.4

(59 reviews)

Intermediate level
Some related experience required
4 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the Data Science Fundamentals Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

Welcome to Module 1, Predictive Modeling. In this module we will begin with a comparison of predictive and descriptive analytics, and discuss what can be learned from both. We will also discuss supervised and unsupervised modeling, two foundational models in analytics and machine learning.

What's included

1 video2 readings1 discussion prompt

Welcome to Module 2, Data Dimensionality and Classification Analysis. In this module we will explore how data can be classified and how decision trees can be leveraged as a fast, easy to use a model that is easy to interpret, explain, and visualize.

What's included

2 readings1 quiz

Welcome to Module 3, Model Fitting. In this module we will explore the concept of model fitting and how creating a generalized model that is able to fit both historical and future data is the ultimate goal. We will also review how a model can be trained or scored to apply to new and unlabeled data.

What's included

1 video2 readings1 discussion prompt

Welcome to Module 4, Regression Analysis. In this module we will begin with an explanation of regression analytics, a popular technique used by data science professionals to make predictions. We will also discuss how achieving model fit is not a guarantee that a model can help solve a business problem, and how even a good model can sometimes lead to unactionable outcomes.

What's included

2 readings1 quiz1 discussion prompt

Instructor

Instructor ratings
4.6 (17 ratings)
Julie Pai
University of California, Irvine
8 Courses20,362 learners

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4.4

59 reviews

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