Data Science Essentials: Preprocessing and Core Algorithms

Understanding how to prepare data and choose the right algorithm is essential for anyone entering the field of data science. This written course provides a structured introduction to core data handling techniques and fundamental machine learning models. You will gain a practical foundation in turning raw datasets into actionable insights through effective feature engineering and reliable model building. What you will learn: Understand core data science terminology and foundational workflow steps. Clean, preprocess, and handle missing values in raw datasets. Perform feature engineering, feature selection, and data scaling. Apply fundamental algorithms including linear models, decision trees, and k-nearest neighbors. Combine models using simple ensemble methods to boost performance. Evaluate model accuracy using standard validation metrics. The course begins with key terminology and foundational concepts before advancing to practical data cleaning and classic supervised learning algorithms. You will read detailed explanations and study practical code snippets to build strong analytical intuition. This course is designed for complete beginners and entry-level analysts who want a solid grounding in data science without requiring advanced math background. Start reading today and build your data science toolkit step by step.