Applied Forecasting, Regression, and Time Series Analysis

In today's data-driven world, the ability to predict future trends and understand underlying relationships is a crucial skill for analysts, researchers, and business decision-makers. This course provides a comprehensive, beginner-friendly introduction to statistical forecasting, regression analysis, and time series modeling. You will learn how to analyze historical data, build predictive models, and evaluate their accuracy through structured, written explanations and practical code examples. What you'll learn: 1. Understand the foundational concepts of statistical modeling, probability distributions, and data preparation. 2. Build and interpret linear and multiple regression models to identify relationships between variables. 3. Analyze time series data by identifying trends, seasonality, and cyclical patterns. 4. Apply classic forecasting techniques, including moving averages, exponential smoothing, and ARIMA models. 5. Evaluate model performance using modern metrics and validate your forecasts for real-world reliability. 6. Practice implementing these models using clean, modern Python code snippets and structured exercises. The course begins with essential statistical foundations before guiding you step-by-step through regression analysis, time series decomposition, and forecasting methodologies. This text-only program is designed for beginners, requiring no prior experience in forecasting or advanced statistics. Start reading today to unlock the power of predictive data analysis.