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Predictive Analytics Project Ideation

Курс от University of Minnesota

Начальный≈ 13.1 чАнглийский

О курсе

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

Навыки, которые вы освоите

Predictive Modeling Customer Analysis Predictive Analytics Deep Learning Ideation Project Design Analysis Solution Design Business Analytics Machine Learning Brainstorming Machine Learning Methods Advanced Analytics Trend Analysis Design Thinking Sprint Planning Applied Machine Learning Model Evaluation Business Analysis

Программа курса

5 модулей · 47 учебных материалов

01Specialization and Course Overview3 материалов

Introduction to Analytics Project Ideation Specialization OverviewВидео Personal IntroductionВидео Course OverviewВидео

02Module 1: Overview of Predictive Analytics 11 материалов

Module 1 Part 1: What is Predictive Analytics?

Course 2 Module 1 Overview VideoВидео

Учитесь у экспертов

Soumya Sen

Associate Professor

Predictive Analytics Project Ideation

О курсе

Навыки, которые вы освоите

Программа курса

Module 1 Part 1: What is Predictive Analytics?

Учитесь у экспертов

Soumya Sen

Module 1 Part 2: Predictive Model Building

Module 1 Part 3: Classification Methods

Module 1 Wrap-up

Module 2 Part 1: ML/AI Models

Module 2 Part 2: Numeric Predictions

Module 2 Part 3: Performance Metrics and Best Practices

Module 2 Wrap-up

Module 3 Part 1: High-level Needs Identification

Module 3 Part 2: SCQ Analysis (Customer Churn)

Module 3 Part 3: Identifying Subproblems

Module 3 Wrap-up

Module 4 Part 1: Outcome Sketches

Module 4 Part 2: Results Mapping

Module 4 Part 3: Application to Other Areas

Module 4 Wrap-up