University of California, Irvine
Cluster Analysis, Association Mining, and Model Evaluation
University of California, Irvine

Cluster Analysis, Association Mining, and Model Evaluation

This course is part of Data Science Fundamentals Specialization

Taught in English

Some content may not be translated

Julie Pai

Instructor: Julie Pai

3,945 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(35 reviews)

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

What you'll learn

  • Cluster analysis and segmentation

  • Collaborative filtering and market basket analysis

  • Applications of classification- and regression-type prediction models

Details to know

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Assessments

2 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(35 reviews)

Intermediate level
Some related experience required
3 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
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There are 4 modules in this course

Welcome to Module 1, Cluster Analysis and Segmentation. In this module we will explore cluster analysis, a popular unsupervised learning algorithm. We will also review the two major styles of cluster analysis, and discuss potential applications to different industries.

What's included

2 readings1 discussion prompt

Welcome to Module 2, Collaborative Filtering, Association Rules Mining, & Market Basket Analysis. In this module we will begin with an explanation of collaborative filtering and association rules mining, and how these techniques are used to make automatic predictions. We will also take a closer look at the various common applications of market basket analysis.

What's included

1 video1 reading1 quiz

Welcome to Module 3, Classification-Type Prediction Models. In this module we will begin with an explanation of how classification-type prediction models are evaluated for performance, and how a confusion matrix can help visualize that performance. We will also discuss the applicability of cluster analysis, and how it can be used to detect rare events such as fraudulent transactions.

What's included

1 video2 readings1 discussion prompt

Welcome to Module 4, Regression-Type Prediction Models. In this module we will review how regression analytics are used for both hypothesis testing and prediction, and how a scatter plot can be leveraged to better understand the relationship between two variables. We will also discuss the differences between correlation analysis and a regression analysis, and a look at simple vs multiple regression.

What's included

1 reading1 quiz1 discussion prompt

Instructor

Instructor ratings
4.4 (9 ratings)
Julie Pai
University of California, Irvine
8 Courses19,930 learners

Offered by

Recommended if you're interested in Data Analysis

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4.5

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Reviewed on Mar 23, 2023

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