The "Clustering Analysis" course introduces students to the fundamental concepts of unsupervised learning, focusing on clustering and dimension reduction techniques. Participants will explore various clustering methods, including partitioning, hierarchical, density-based, and grid-based clustering. Additionally, students will learn about Principal Component Analysis (PCA) for dimension reduction. Through interactive tutorials and practical case studies, students will gain hands-on experience in applying clustering and dimension reduction techniques to diverse datasets.
Clustering Analysis
This course is part of Data Analysis with Python Specialization
Instructor: Di Wu
1,685 already enrolled
Included with
Recommended experience
What you'll learn
Understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction.
Apply clustering techniques to diverse datasets for pattern discovery and data exploration.
Implement Principal Component Analysis (PCA) for dimension reduction and interpret the reduced feature space.
Skills you'll gain
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There are 6 modules in this course
This week provides an introduction to unsupervised learning and clustering analysis. You will delve into partitioning clustering methods, such as K-Means and K-Medoids, understanding their principles and applications.
What's included
2 videos5 readings1 assignment1 discussion prompt
This week you will explore hierarchical clustering, a method that creates a tree-like structure to represent data similarities.
What's included
1 video3 readings1 assignment1 discussion prompt
This week focuses on density-based clustering, which groups data points based on their density within the dataset.
What's included
1 video3 readings1 assignment1 discussion prompt
Throughout this week, you will explore grid-based clustering, an approach that partitions the data space into grids for efficient clustering.
What's included
1 video2 readings1 assignment1 discussion prompt
This week introduces dimension reduction techniques as a critical preprocessing step for handling high-dimensional data.
What's included
1 video3 readings1 assignment1 discussion prompt
The final week focuses on a comprehensive case study where you will apply clustering and dimension reduction techniques to solve a real-world problem.
What's included
1 reading1 assignment1 discussion prompt
Instructor
Offered by
Recommended if you're interested in Data Analysis
University of Michigan
University of London
S.P. Jain Institute of Management and Research
University of Illinois Urbana-Champaign
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