Data Science in Python: Regression

This badge is earned by successfully completing the Data Science in Python: Regression course at Maven Analytics. COURSE HOURS: 14.5 COURSE DESCRIPTION: This is a hands-on, project-based course designed to help you master the foundations for regression analysis in Python. We’ll start by reviewing the data science workflow, discussing the primary goals & types of regression analysis, then do a deep dive into the regression modelling workflow we’ll be using throughout the course. You’ll learn to perform exploratory data analysis for the purpose of regression modeling, fit simple & multiple linear regression models, and build an intuition for interpreting models and evaluating their performance using tools like hypothesis tests, residual plots, and error metrics. We’ll also review the assumptions of linear regression, and show examples of checking and fixing each one. From there, we’ll cover the model testing & validation steps that help ensure our models perform well on new, unseen data, including the concepts of data splitting, validation & cross-validation, and model scoring & selection. You’ll also learn how to improve model performance through feature engineering and by leveraging regularized regression algorithms. Throughout the course you'll play the role of Associate Data Scientist for Maven Consulting Group on a team that focuses on pricing strategy for their clients. Using the skills you learn throughout the course, you'll use Python to explore their data and build regression models to help firms accurately predict prices and understand the variables that impact them. Last but not least, you'll get an introduction to time series analysis & forecasting techniques. You’ll learn to analyze trends & seasonality, perform decomposition, and forecast future values. If you're an aspiring data scientist looking for an introduction to the world of regression modeling with Python, this is the course for you.