Introduction to Predictive Analytics

Introduction to Predictive Analytics is a practical course that introduces the core methods and mindset behind Data Science-driven prediction. You will learn how to turn data into forecasts, decisions, and measurable business value with Professor Anthony Owens., building the confidence to ask the right questions and evaluate results with clarity.

Build Predictive Thinking With Introduction To Predictive Analytics

• Learn the foundations of Predictive Analytics and how it fits into modern Data Science workflows
• Understand how to frame business questions that can be predicted and acted on
• Gain hands-on familiarity with data preparation, model development, and model evaluation
• Apply predictive methods to real-world use cases while communicating results responsibly

A clear introduction to predicting outcomes with data, models, and business insight.

This course walks you through the full predictive analytics process, from defining a problem to presenting results that stakeholders can use. You will explore the types of data used in prediction, how raw information is transformed into usable features, and how targets, inputs, and outcomes work together in a predictive model.

You will also study core methods such as regression, classification, and forecasting, with a focus on when and why each approach is used. The course explains how to train, test, and validate models, how to measure performance, and how to identify common risks like overfitting, bias, and data leakage. These concepts are essential for anyone building a strong foundation in Data Science.

Beyond model building, you will learn how to interpret variable importance, read predictions in context, and communicate findings to stakeholders in a way that supports decisions. The course also covers ethics, fairness, and responsible use, helping you understand not just what predictive models can do, but what they should do.

By the end of Introduction to Predictive Analytics, you will be able to approach prediction problems with a structured method, evaluate model quality with confidence, and connect analytics work to business value. You will finish better prepared to create practical forecasting and decision-support solutions in Data Science.