Introduction

Predictive analytics and forecasting models are essential tools in today's data-driven decision-making landscape. This course will equip participants with the techniques and technologies necessary to harness historical data and make informed forecasts that drive strategic initiatives. Through a blend of theoretical insights and practical applications, participants will gain the skills needed to construct accurate predictive models and implement robust forecasting strategies.

• Understand the fundamentals of predictive analytics and forecasting.
• Explore various statistical and machine learning models used for prediction.
• Develop skills to prepare and process data for predictive modeling.
• Implement predictive models using popular software tools and frameworks.
• Evaluate and improve the accuracy of forecasting models.

Course Outlines

Day 1: Introduction to Predictive Analytics

• Overview of predictive analytics and its business applications
• Key concepts and terminologies
• Data types and data sources in predictive modeling
• Introduction to data preprocessing and cleaning
• Hands-on session: Setting up a predictive analytics environment

Day 2: Statistical Models and Techniques

• Exploratory data analysis (EDA) techniques
• Linear and logistic regression models
• Time series analysis fundamentals
• Introduction to ARIMA models
• Hands-on session: Building simple regression models

Day 3: Machine Learning for Prediction

• Introduction to supervised learning techniques
• Decision trees and random forests for prediction
• Support vector machines and neural networks
• Model evaluation and validation techniques
• Hands-on session: Implementing machine learning algorithms

Day 4: Advanced Forecasting Models

• Advanced time series forecasting methods
• Autoregressive models and their extensions
• Understanding seasonality and trends
• Integration of external factors in forecasting models
• Hands-on session: Advanced forecasting model development

Day 5: Implementing and Operationalizing Models

• Integrating predictive models into business processes
• Monitoring and maintaining predictive models
• Case studies of successful implementations
• Scaling predictive models and managing large datasets
• Hands-on session: Deploying predictive models in a cloud environment