Practical Forecasting and Regression with Python

Want to predict future trends or understand the relationships hidden in your data? Regression and forecasting are essential skills for any data analyst or scientist, and Python provides the perfect tools to apply them. This course provides a practical, text-based foundation in building predictive models from scratch. You will move from core statistical concepts to implementing and evaluating common regression and time-series forecasting models, gaining the confidence to turn raw data into valuable insights and accurate predictions. What you'll learn: - Understand the core principles of linear regression and time-series analysis. - Practice essential data cleaning, preprocessing, and feature engineering techniques. - Build, train, and test predictive regression models using Python and scikit-learn. - Evaluate model performance using key metrics like R-squared, MAE, and MSE. - Implement foundational forecasting techniques, from moving averages to ARIMA models. - Interpret model coefficients and results to explain relationships in your data. The course begins with key terminology and statistical foundations before guiding you through hands-on coding exercises. You'll start with simple linear models and progressively build up to more complex time-series applications. This course is designed for beginners. No prior experience in statistics or machine learning is required, though a basic familiarity with Python syntax will be helpful. Start building your predictive modeling skills today.