Data Insights with Cluster Analysis
Course
Online
Description
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Type
Course
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Methodology
Online
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Start date
Different dates available
Cluster analysis is the process of grouping data that have similar attributes or properties, and is incredibly useful in a wide variety of fields and applications, including market analysis and segmentation, medical imaging, recommender systems, geospatial data, anomaly detection and more. Whether the number of groups (or “clusters”) are predefined, or determined by an algorithm, cluster analysis helps to provide you with insight about what data should belong together.
This course will provide you with all that you need to get started with cluster analysis. Beginning with a fundamental understanding of what cluster analysis is and how it can be used, you will then go on to learn the most popular clustering algorithms:
k-means Clustering
Density-based Spatial Clustering of Applications with Noise (DBSCAN)
Hierarchical Agglomerative Clustering (HAC)
Frameworks and tools covered: Python 3.7, Anaconda 5.3, Matplotlib 3.0
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About this course
Familiarity in analyzing data with Pandas is required for this course. It is recommended that you complete Data Analysis with Pandas before taking this course.
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Subjects
- Access
- IT
- Management
- Information Systems management
- IT Management
Course programme
Introduction 2:50
Introduction 2:50
2:50
Introduction to Cluster Analysis 9:39
Introduction to Cluster Analysis 9:39
9:39
K Means Clustering 11:34
K Means Clustering 11:34
11:34
K Means on Datasets - Part 1 10:42
K Means on Datasets - Part 1 10:42
10:42
K Means on Datasets - Part 2 10:30
K Means on Datasets - Part 2 10:30
10:30
DBSCAN - Part 1 9:53
DBSCAN - Part 1 9:53
9:53
DBSCAN - Part 2 11:05
DBSCAN - Part 2 11:05
11:05
DBSCAN on Datasets - Part 1 9:57
DBSCAN on Datasets - Part 1 9:57
9:57
DBSCAN on Datasets - Part 2 9:59
DBSCAN on Datasets - Part 2 9:59
9:59
HAC 9:45
HAC 9:45
9:45
HAC on Datasets 9:55
HAC on Datasets 9:55
9:55
Intro to Credit Card Dataset 9:50
Intro to Credit Card Dataset 9:50
9:50
The Elbow Method and KMeans 10:30
The Elbow Method and KMeans 10:30
10:30
Running HAC on Our Dataset 9:47
Running HAC on Our Dataset 9:47
9:47
Conclusion 1:37
Conclusion 1:37
1:37
Additional information
Data Insights with Cluster Analysis