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

Predictive Modeling, Model Fitting, and Regression Analysis

Julie Pai

Instructor: Julie Pai

6,060 already enrolled

Included with Coursera Plus

Gain insight into a topic and learn the fundamentals.
4.4

(61 reviews)

Intermediate level
Some related experience required
4 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.4

(61 reviews)

Intermediate level
Some related experience required
4 hours to complete
3 weeks at 1 hour a week
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 assignments

Taught in English

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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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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 assignment

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 assignment1 discussion prompt

Instructor

Instructor ratings
4.6 (19 ratings)
Julie Pai
University of California, Irvine
8 Courses23,115 learners

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Recommended if you're interested in Data Analysis

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4.4

61 reviews

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