Description

Welcome to the "2.Foundations of Descriptive Analytics" section of our Business Analytics and Data Science course. This part is designed to ground you in the fundamental principles and practices of descriptive analytics, setting the stage for more complex analytical techniques. Here’s what you can expect:

1. Descriptive Analytics in a Nutshell

   • Scope and Flavors: Gain a clear understanding of the various aspects of descriptive analytics, including its scope and different approaches.
   • Business Applications: Learn about the practical applications of descriptive analytics in business, helping you see how these techniques can be applied to real-world scenarios.

2. Cluster Analysis Methods

   • Goal Formalization: Understand the goals of cluster analysis and how to formalize them in a structured way.
   • Similarity Measures: Explore different similarity measures used in cluster analysis to determine how closely related data points are.
   • The k-means Algorithm: Dive deep into the k-means algorithm, a fundamental method in cluster analysis, and learn how to apply it to segment data into meaningful clusters.

Learning Objectives

By the end of this section, you will be able to:

• Define and explain the core concepts and scope of descriptive analytics.
• Identify and describe various business applications of descriptive analytics.
• Understand and formalize the goals of cluster analysis.
• Apply different similarity measures to assess the relationships between data points.
• Implement the k-means algorithm to perform cluster analysis.

Next Steps

Upon completing this section, you are recommended to proceed to the section on predictive analytics. There are no strict prerequisites for this section, but for the best learning experience, ensure continuous engagement with the videos and accompanying PDFs.