This course is PART 3 of a 4-PART SERIES designed to help you build a fundamental understanding of machine learning:

1. QA & Data Profiling

2. Classification

3. Regression & Forecasting

4. Unsupervised Learning

We’ll start by introducing core building blocks like linear relationships and least squared error, then show you how these concepts can be applied to univariate, multivariate, and non-linear regression models.

From there we’ll review common diagnostic metrics like R-squared, mean error, F-significance, and P-values, along with important concepts like homoscedasticity and multicollinearity.

Last but not least we’ll dive into time-series forecasting, and explore powerful techniques for identifying seasonality, predicting nonlinear trends, and measuring the impact of key business decisions using intervention analysis.

Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll see how regression analysis can be used to estimate property prices, forecast seasonal trends, predict sales for a new product launch, and even measure the business impact of a new website design.

NOTE: This is NOT a coding course, and doesn’t cover programming languages like Python or R. Our goal is to use familiar tools like Excel to demystify complex topics and explain exactly how they work.

If you’re ready to build the foundation for a successful career in data science, this is the course for you.