University of Colorado Boulder

Predictive Modeling and Analytics

This course is part of Advanced Business Analytics Specialization

Taught in English

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Dan Zhang

Instructor: Dan Zhang

36,371 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

3.6

(592 reviews)

|

61%

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

What you'll learn

  • Apply exploratory data analysis to gain insights and prepare data for predictive modeling

  • Summarize and visualize datasets using appropriate tools

  • Identify modeling techniques for prediction of continuous and discrete outcomes

  • Identify appropriate graphs to explore and display datasets

Details to know

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Assessments

9 quizzes

Course

Gain insight into a topic and learn the fundamentals

3.6

(592 reviews)

|

61%

Intermediate level
Some related experience required
10 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 Advanced Business Analytics 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

At the end of this module students will be able to: 1. Carry out exploratory data analysis to gain insights and prepare data for predictive modeling 2. Summarize and visualize datasets using appropriate tools 3. Identify modeling techniques for prediction of continuous and discrete outcomes. 4. Explore datasets using Excel 5. Explain and perform several common data preprocessing steps 6. Choose appropriate graphs to explore and display datasets

What's included

8 videos1 reading2 quizzes1 peer review1 discussion prompt

This module introduces regression techniques to predict the value of continuous variables. Some fundamental concepts of predictive modeling are covered, including cross-validation, model selection, and overfitting. You will also learn how to build predictive models using the software tool XLMiner.

What's included

8 videos2 quizzes1 discussion prompt

This module introduces logistic regression models to predict the value of binary variables. Unlike continuous variables, a binary variable can only take two different values and predicting its value is commonly called classification. Several important concepts regarding classification are discussed, including cross validation and confusion matrix, cost sensitive classification, and ROC curves. You will also learn how to build classification models using the software tool XLMiner.

What's included

8 videos2 quizzes1 discussion prompt

This module introduces more advanced predictive models, including trees and neural networks. Both trees and neural networks can be used to predict continuous or binary variables. You will also learn how to build trees and neural networks using the software tool XLMiner.

What's included

8 videos3 quizzes1 peer review1 discussion prompt

Instructor

Instructor ratings
3.0 (108 ratings)
Dan Zhang
University of Colorado Boulder
3 Courses49,898 learners

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3.6

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