Foundations of Data Science

Data-driven decision-making is at the heart of modern industry, yet entering the field of data science can feel overwhelming with its mix of mathematics, programming, and analysis. This course breaks down these complex ideas into clear, digestible concepts, helping you build a strong foundation from the ground up. You will transition from a complete beginner to a confident data practitioner capable of cleaning messy datasets, performing exploratory data analysis, and training machine learning models. Along the way, you will also learn how to structure your data pipelines using modern Python practices and understand how data science fits into today's AI-driven technology landscape. What you'll learn: - Understand the core mathematical and statistical principles behind data analysis - Clean and manipulate raw data using modern Python libraries and dataframe structures - Perform exploratory data analysis to discover trends, patterns, and anomalies - Build and evaluate foundational machine learning models for classification and regression - Apply modern coding practices, including type hints and structured pipelines, for cleaner data workflows - Explore modern data concepts, including an introduction to vector databases and machine learning operations (MLOps) The curriculum begins with essential terminology and statistical foundations before moving step-by-step into data manipulation, exploratory analysis, and practical machine learning workflows. This course is designed for absolute beginners with no prior experience in data science who want a structured, text-based introduction to the field. Start your journey into the world of data today and unlock the power of data-driven insights.