Coursera

Predictive Modeling, Model Fitting, and Regression Analysis (Coursera)

Offered by University of California, Irvine,
Predictive Modeling, Model Fitting, and Regression Analysis (Coursera)

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.

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What You Will Learn

  • The application of predictive modeling to professional and academic work
  • Applications of classification analysis: decision trees
  • Applications of regression analysis (linear and logistic)

Course 2 of 4 in the Data Science Fundamentals Specialization

Syllabus

WEEK 1
Predictive Modeling
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.

WEEK 2
Data Dimensionality and Classification Analysis
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.

WEEK 3
Model Fitting
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.

WEEK 4
Regression Analysis
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.

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