Data Insights with Cluster Analysis
Learn the fundamentals of cluster analysis and the most popular clustering algorithms
This title is part of the Data Science Mini-Degree
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)
About the Data Science Mini-Degree
The Data Science Mini-Degree is a collection of professional-grade online courses designed to take you from absolute beginner to industry-ready Data Scientist with Python. From the basics of reading and storing data, to using statistical analysis to solve real-world problems, and visualizing your data in beautiful plots and charts, this comprehensive curriculum features everything you need to get started in the industry.
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.