Data Science in Python: Regression & Forecasting

Learn Python for Data Science & Machine Learning, and build regression and forecasting models with hands-on projects

Created by Maven Analytics, Chris Bruehl | 8.5 hours on-demand video course

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, and do a deep dive into the regression modeling steps we’ll be using throughout the course.

You’ll learn to perform exploratory data analysis, 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 learn how to diagnose and fix 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, tuning, and model selection. You’ll also learn how to improve model performance by leveraging feature engineering techniques and 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.

What you’ll learn

• Master the machine learning foundations for regression analysis in Python
• Perform exploratory data analysis on model features, the target, and relationships between them
• Build and interpret simple and multiple linear regression models with Statsmodels and Scikit-Learn
• Evaluate model performance using tools like hypothesis tests, residual plots, and mean error metrics
• Diagnose and fix violations to the assumptions of linear regression models
• Tune and test your models with data splitting, validation and cross validation, and model scoring
• Leverage regularized regression algorithms to improve test model performance & accuracy
• Employ time series analysis techniques to identify trends & seasonality, perform decomposition, and forecast future values

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