Cluster Analysis- Theory & workout using SAS and R

🎉 Master Unsupervised Machine Learning with "Cluster Analysis - Theory & Workout using SAS and R" 📚✨

About the Course: Dive into the fascinating world of Cluster Analysis, a cornerstone of data mining that uncovers natural groupings within your data. This course is your gateway to understanding how to categorize data in a way that each group's elements are as similar as possible to each other while being as different from other groups as possible. Whether for marketing analysis or customer segmentation, this skill is indispensable for anyone looking to extract meaningful patterns and insights from their data.

Course Materials: Get ready to learn with comprehensive video presentations (complete with PowerPoint slides and voiceovers), detailed PDF notes, an extensive Excel workbook, and SAS codes. These resources are carefully crafted to cater to both theoretical understanding and practical application.

Duration & Commitment: This course is designed to be completed in approximately 10 hours. This allows you the time needed to fully grasp the concepts and apply them effectively through hands-on practice.

Course Structure (Contents):

Part 01 - Cluster Analysis Theory and Workout using SAS

🎓 Motivation:

• Discover where and why cluster analysis is applied.
• Learn the importance of cluster analysis in today's data-driven decision-making.
• Understand how it differs from objective segmentation techniques like CHAID or CART.

Part 02 - Cluster Analysis using SAS

🔍 Statistical foundation and practical application:

• Explore different types of cluster analysis.

• Gain a high-level view of cluster analysis.

• Dive into Hierarchical Clustering:

  • Learn about Agglomerative and Divisive techniques.
  • Create and interpret Dendrograms, including understanding Scree plots to determine the optimal number of clusters.
  • Master the SAS commands for running hierarchical clustering.
  • Discover when and why standardizing data is crucial.
  • Interpret the output of hierarchical clustering in SAS.

• Delve into Non-Hierarchical Clustering (k-means):

  • Understand the need for a k-means approach.
  • Explore how the algorithm works, including its iterative process and criteria for merging clusters.
  • Master the SAS commands for running k-means clustering.
  • Learn when standardizing data is important in k-means.
  • Interpret the output of k-means clustering in SAS.

Part 03 - Cluster Analysis using R

Learn the R syntax for both hierarchical and non-hierarchical clustering, with hands-on examples to solidify your understanding.

Part 04 - Cluster Analysis in Data Mining Scenario

Apply your newfound knowledge to real-world data mining scenarios, showcasing the versatility and power of cluster analysis techniques.

Assignment on Cluster Analysis: Put your learning into practice with an assignment that will challenge you to apply your theoretical knowledge and practical skills in a tangible way. This is your opportunity to demonstrate mastery of cluster analysis and enhance your analytical toolkit.

Enroll now and embark on a journey to become proficient in Cluster Analysis using SAS and R! 🚀💻📈