Predictive analytics turns data into a crystal ball, empowering your organization to anticipate trends, seize opportunities, and stay ahead of the curve with every decision. In this course, we will begin with an overview of predictive analytics models, such as decision trees, kNN, and neural networks, and explore their business applications. Following this, we will examine a case study about customer churn to learn how to use a design sprint framework for brainstorming a predictive analytics project plan. Learning objectives: - Examine how predictive analytics principles can be applied to address business challenges. - Examine advanced ML/AI models for predictive analytics - Analyze business context and construct an issue tree for a predictive analytics project - Select a solution approach and define a predictive modeling project
Personal Introduction
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Skills You'll Learn
Time Series Analysis and Forecasting, Project Design, Predictive Analytics, Business Analytics, Analysis, Machine Learning, Decision Tree Learning, Deep Learning, Solution Design, Customer Analysis, Sprint Planning, Applied Machine Learning, Design Thinking, Brainstorming
From the lesson
Specialization and Course Overview
Taught By
Soumya Sen
Associate Professor