Do I need to be a math expert?

You do not need an advanced math degree to take this course. If you understand basic logic and how to read a spreadsheet, you can learn the core concepts of data prediction.

What kind of data do I need to start?

You can start with almost any spreadsheet that has historical information. The course shows you how to turn that history into a map for future guesses.

Will I learn a specific programming language?

The course focuses on the logic and steps that apply to any tool. While we use code to demonstrate the ideas, the goal is to teach you the workflow that stays the same regardless of which software you use.

Can I use these skills at work?

Yes. Most businesses have piles of data they are not using effectively. This course gives you a framework to turn that history into useful predictions for your team or project.

Predicting the Future with Data

Name: Predicting the Future with Data
Availability: InStock

Most people treat data science like magic. This course shows you the logic computers use to find patterns in the past and make guesses about the future.

Updated Sep 14, 2026

About this course

Many people talk about AI as if it is a black box that just knows things. In reality, it is a set of instructions for finding patterns. This course moves past the buzzwords to show you the logic behind how machines actually learn from information. You will learn to see data the way a computer does, by breaking it down into features it can measure and labels it can predict. You will learn how to pick the right tool for different tasks. Sometimes you need to predict a specific number, like the price of a car. Other times you need to sort things into groups, like deciding if a transaction is fraud or not. We will look at how to handle messy, real-world data that often has gaps or errors. By the end, you will have a clear workflow for building your own predictions based on evidence rather than just a hunch.

Details

Updated Sep 14, 2026

3 units, 9 lessons

3 assessments

Skills you'll learn

Data Organization

Organize raw information into features and labels so a computer can process it correctly.

Model Selection

Choose between regression and classification tools based on the specific problem you are trying to solve.

Data Cleaning

Identify and fix missing or messy information that would otherwise make your predictions inaccurate.

Accuracy Testing

Use separate training and testing data to measure how well your model actually works before you trust it.

Syllabus

3 units • 9 lessons • 3 assessments

Learning activities

Every lesson gives you several ways to learn.

Read

Flashcards

Quiz

Arcade

Frequently asked questions

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