Data science begins with strong foundations in statistics, mathematics, and computational thinking. Fundamentals for Data Science - Volume I provides a comprehensive and accessible introduction to the core principles that underpin data-driven analysis and decision-making.

This volume introduces readers to essential programming tools in R and Python, enabling hands-on engagement with data from the outset. Foundational topics such as probability theory, probability distributions, sampling, and statistical inference are developed with clarity and intuition, ensuring conceptual understanding without excessive mathematical formalism. Key mathematical tools, including linear algebra, are presented in a manner tailored to data science applications.

The book places strong emphasis on exploratory data analysis, regression modeling, causal inference, and business-focused statistical applications, reinforced through real-world case studies and executable code examples. Designed for undergraduate and postgraduate students, as well as professionals transitioning into analytics roles, this volume serves as both a classroom-ready textbook and a practical self-learning resource. It prepares readers with the analytical maturity required to progress confidently to advanced machine learning and modern data science techniques.