Exploratory analytics is where data meets discovery, revealing hidden patterns and turning raw information into breakthrough insights. In this course we will first provide an overview of exploratory analytics methods such as clustering, association rule mining, anomaly detection, and study their business use cases. We will then consider a case study to learn how to use a design sprint framework to ideate about exploratory analytics project plan.
Exploratory Analytics Project Ideation
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Exploratory Analytics Project Ideation
This course is part of Analytics Project Ideation Specialization
Instructor: Soumya Sen
Included with 
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
14 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Details to know
Shareable certificate
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Recently updated!
January 2025
Assessments
8 assignments
Taught in English
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This course is part of the Analytics Project Ideation Specialization
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There are 5 modules in this course
What's included
3 videos
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.
What's included
10 videos3 readings2 assignments
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.
What's included
9 videos2 readings2 assignments
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.
What's included
9 videos1 reading2 assignments
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.
What's included
9 videos2 readings2 assignments
Instructor
Offered by
Recommended if you're interested in Leadership and Management
University of Minnesota
University of Minnesota
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