Welcome to Cluster Analysis, Association Mining, and Model Evaluation. In this course we will begin with an exploration of cluster analysis and segmentation, and discuss how techniques such as collaborative filtering and association rules mining can be applied. We will also explain how a model can be evaluated for performance, and review the differences in analysis types and when to apply them.
Cluster Analysis, Association Mining, and Model Evaluation
Cluster Analysis, Association Mining, and Model Evaluation
This course is part of Data Science Fundamentals Specialization
Instructor: Julie Pai
4,657 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
4.5
(43 reviews)
Intermediate level
Some related experience required
3 hours to complete
3 weeks at 1 hour a week
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
Skills you'll gain
- Classification And Regression Tree (CART)
- Unsupervised Learning
- Scatter Plots
- Market Analysis
- Statistical Analysis
- Correlation Analysis
- Statistical Modeling
- Anomaly Detection
- Predictive Modeling
- Predictive Analytics
- Data Mining
- Probability & Statistics
- Data Analysis
- AI Personalization
- Machine Learning
- Cross Selling
- Regression Analysis
- Supervised Learning
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
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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 assignment
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 assignment1 discussion prompt
Instructor
Instructor ratings
4.4 (9 ratings)5 Courses16,824 learners
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4.5
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MM
5
Reviewed on Mar 23, 2023
This course is fairly easy if you know something about statistics for data mining already. Well explained topics & also further reading suggestions are given, which is a bonus.
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