Principles of Data Science offers an end-to-end introduction to data science fundamentals, blending key mathematical concepts with practical programming. You'll learn how to clean and prepare data, construct predictive models, and leverage modern tools like pre-trained models for NLP and computer vision. By integrating theory and practice, this book sets the foundation for impactful data-driven decision-making. What this Book will help me do Develop a solid understanding of foundational statistics and machine learning. Learn how to clean, transform, and visualize data for impactful analysis. Explore transfer learning and pre-trained models for advanced AI tasks. Understand ethical implications, biases, and governance in AI and ML. Gain the knowledge to implement complete data pipelines effectively. Author(s) Sinan Ozdemir is an experienced data scientist, educator, and author with a deep passion for making complex topics accessible. With a background in computer science and applied statistics, Sinan has taught data science at leading institutions and authored multiple books on the topic. His practical approach to teaching combines real-world examples with insightful explanations, ensuring learners gain both competence and confidence. Who is it for? This book is ideal for beginners in data science who want to gain a comprehensive understanding of the field. If you have a background in programming or mathematics and are eager to combine these skills to analyze and extract insights from data, this book will guide you. Individuals working with machine learning or AI who need to solidify their foundational knowledge will find it invaluable. Some familiarity with Python is recommended to follow along seamlessly.
