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This module delves into exploratory analytics methods, including clustering, anomaly detection, and association rule mining. Students will learn how to group similar data points, identify outliers, and uncover hidden relationships within datasets, leveraging these techniques to gain deeper insights and drive data-driven decisions.

Learn about different business contexts where exploratory analytics have been applied to generate insights from data. Practical examples in this module will reinforce theoretical concepts in exploratory analytics, preparing students to effectively apply these methods in other real-world scenarios.

This module illustrates how to set up a design sprint for exploratory project ideation with team members. The process begins with brainstorming to decompose a complex business process and its needs into clear, actionable data analytics questions using Situation-Complication-Question (SCQ) analysis. These questions are then prioritized and mapped out using an issue tree to ensure a structured approach to problem-solving and to identify the most critical areas for data-driven insights.

In the next step of the design sprint for exploratory project ideation, teams identify desired project outcomes by creating mockups to visualize potential solutions. They then construct a solution approach to map these outcomes to the initial questions by selecting the appropriate exploratory analytics methods and available data, ensuring a structured and data-driven path to achieve the project goals.