Predictive Modeling, Model Fitting, and Regression Analysis

Predictive Modeling, Model Fitting, and Regression Analysis

This course is part of Data Science Fundamentals Specialization

Instructor: Julie Pai

8,471 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

75 reviews

Intermediate level

Some related experience required

4 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

75 reviews

Intermediate level

Some related experience required

4 hours to complete

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)

Skills you'll gain

Tools you'll learn

Classification Algorithms

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

2 assignments

Taught in English

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This course is part of the Data Science Fundamentals Specialization

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There are 4 modules in this course

Welcome to Predictive Modeling, Model Fitting, and Regression Analysis. In this course, we will explore different approaches in predictive modeling, and discuss how a model can be either supervised or unsupervised. We will review how a model can be fitted, trained and scored to apply to both historical and future data in an effort to address business objectives. Finally, this course includes a hands-on activity to develop a linear regression model.

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.