Cambridge
Academy
In London

Introduction

Predictive and prescriptive analytics transform raw data into actionable strategies. This course covers machine learning models that forecast future outcomes and prescribe optimal solutions.

Objectives

- Apply AI for predictive analytics.

- Use prescriptive analytics to recommend strategies.

- Build machine learning models for forecasting.

- Evaluate business cases using AI-driven insights.

Audience

- Data scientists

- Business analysts

- Operations managers

- Financial analysts

5-Day Outline

Day 1: Predictive Analytics Basics

- Introduction to predictive analytics

- Data requirements for predictive modeling

- AI tools for predictive analytics

- Case study: Predictive analytics in retail

- Workshop: Predictive model setup

Day 2: Building AI Predictive Models

- Regression, classification, and clustering models

- Time-series forecasting with AI

- Tools: Python AI libraries (scikit-learn, TensorFlow)

- Demo: Forecasting with AI

- Workshop: Build predictive model in Python

Day 3: Prescriptive Analytics with AI

- Introduction to prescriptive analytics

- Optimization techniques using AI

- Case study: Prescriptive analytics in logistics

- Tools: IBM Decision Optimization, Google OR-Tools

- Simulation: Prescriptive decision scenario

Day 4: Advanced AI Applications

- Reinforcement learning for decision optimization

- AI in risk analysis and mitigation

- Group exercise: AI-driven decision planning

- Peer discussion on applications

- Feedback session

Day 5: Deploying Predictive & Prescriptive Analytics

- Deployment in business systems

- Integrating AI into BI tools

- Group project: AI analytics solution design

- Presentations & review

- Wrap-up & certification