Applied Data Science: Build a Bicycle Rental Predictor

Have you ever wondered how businesses forecast demand using historical data? Predicting future trends is a core skill for any data professional, and hands-on practice is the best way to master it. In this text-based project course, you will build a complete predictive model from scratch. You will take raw historical data—such as weather conditions, seasons, and days of the week—and transform it into a robust machine learning pipeline that accurately forecasts daily bicycle rentals. By working through this practical scenario, you will bridge the gap between theoretical algorithms and real-world business applications. What you'll learn: • Understand foundational data science terminology, concepts, and regression principles. • Process and explore historical datasets using modern dataframe techniques. • Engineer predictive features from dates, weather conditions, and categorical variables. • Build reproducible machine learning pipelines to streamline data transformations. • Train and evaluate regression models to predict continuous numerical values. • Apply basic model interpretability concepts to explain what drives your predictions. The course begins with an introduction to key data science terminology and the machine learning workflow before diving into applied practice. From there, you will progress through written explanations and practical code snippets, moving step-by-step from data acquisition and cleaning to model training and evaluation. This course is designed for beginners who want to practice their programming skills on a realistic, end-to-end data science project without needing prior machine learning experience. Start reading today to build your first predictive machine learning model.