Clustering Analysis
Master unsupervised learning techniques with hands-on practice in clustering and dimension reduction methods using Python.
Master unsupervised learning techniques with hands-on practice in clustering and dimension reduction methods using Python.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Analysis with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Apply various clustering techniques to discover patterns in datasets
Implement Principal Component Analysis for dimension reduction
Evaluate clustering results using performance metrics
Understand different clustering algorithms and their applications
Solve real-world problems using unsupervised learning methods
Skills you'll gain
This course includes:
0.8 Hours PreRecorded video
5 quizzes, 1 assignment
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course explores unsupervised learning techniques with a focus on clustering analysis and dimension reduction. Students learn various clustering methods including partitioning, hierarchical, density-based, and grid-based approaches. The curriculum covers Principal Component Analysis (PCA) for dimension reduction, featuring hands-on tutorials and real-world case studies. Through practical applications and interactive exercises, participants develop skills in pattern discovery, data exploration, and dimensionality reduction.
Introduction and Partitioning Clustering
Module 1 · 7 Hours to complete
Hierarchical Clustering
Module 2 · 6 Hours to complete
Density-based Clustering
Module 3 · 6 Hours to complete
Grid-based Clustering
Module 4 · 4 Hours to complete
Dimension Reduction Methods
Module 5 · 6 Hours to complete
Case Study
Module 6 · 5 Hours to complete
Fee Structure
Instructor
Teaching Assistant Professor
Dr. Di Wu is a Teaching Assistant Professor at the University of Colorado Boulder, specializing in data science and computer science. His primary research interests include temporal databases, the semantic web, knowledge representation, and data science, with a focus on extending the Resource Description Framework (RDF) for temporal dimensions. Before joining CU Boulder, he taught various courses such as algorithms and data structures, programming languages, and database management. Dr. Wu aims to develop an inclusive and engaging pedagogy in data science education over the next five years, emphasizing experiential learning in both in-person and online formats. He is involved in teaching courses related to data science and programming, including specializations in Python programming for data scientists.
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