Use machine learning to create segments. The Segmentation and Clustering course provides students with the foundational knowledge to build and apply clustering models to develop more sophisticated segmentation in business contexts. In this course, you'll learn how to use an advanced analytical method called clustering to create useful segments for business contexts, whether its stores, customers, geographies, etc. You'll learn this through improving your fluency in Alteryx, a data analytics tool that enables you prepare, blend, and analyze data quickly.
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You will learn:
- The key concepts of segmentation and clustering, such as standardization vs. localization, distance, and scaling
- The concepts of variable reduction and how to use principal components analysis (PCA) to prepare data for clustering models
- How to choose between hierarchical and k-centroid clustering models
- How to build and apply k-centroid clustering models
Throughout this course you’ll also learn the techniques to apply your knowledge in a data analytics program called Alteryx.
Segmentation is used by companies across industries to better target the right products to the right customers. Most segmentation approaches are rather simple, such as segmenting customers by geography or age. However, with the rich amount of data business have now, much more sophisticated segmentation approaches are available.
This course is ideal for anyone who is interested in pursuing a career in business analysis, but lacks programming experience.
This course is part of the Business Analyst Nanodegree Program.
What You Will Learn
Lesson 1
Segmentation and Clustering Fundamentals
- Learn the difference between standardization and localization.
- Learn about the concept of distance in clustering models.
- Get introduced to how segmentation is used in business.
Lesson 2
Data Preparation for Clustering Models
- Learn how to select data for clustering models.
- Learn what data types can be used in clustering models.
- Scale and transform data for clustering models.
Lesson 3
Variable Reduction
- Learn the difference between factor analysis and principle components analysis.
- Learn to use principal components analysis to reduce the number of variables in a model.
Lesson 4
Clustering models design
- Learn the difference between k-centroid and hierarchical clustering models.
- Be able to select the number of clusters for a k-centroid model.
- Validate your clusters in Alteryx.
Lesson 5
Lesson 5 – Building a Clustering Model
- Build a k-centroid clustering model to segment retail stores.
- Learn how to visualize and validate your clusters.
- Be able to interpret the results and communicate the “story” of the analysis.
Prerequisites and Requirements
- No programming experience required
- Interested in using data to make better business decisions
- Alteryx license (provided to Nanodegree students at no cost, compatible with Windows only)
