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In response to the growing demand for expertise in data science and artificial intelligence (AI), Japanese higher education institutions are spearheading initiatives to nurture diverse talent in these fields. This textbook, focusing on data analysis, serves as a cornerstone in this educational landscape, providing a robust foundation for mathematics, data science, and AI studies. Data analysis, a pivotal component of data science, offers a rich toolkit for uncovering hidden patterns and structures within data. By enabling accurate interpretation of results, it facilitates data-driven problem-solving and decision-making across various domains. Our text provides a comprehensive exploration of these analytical techniques, their mathematical underpinnings, and their practical implementation through programming.
 
This textbook is the culmination of two flagship courses offered at the University of Tokyo: "Introduction to Python Programming" and "Introduction to Data Mining." These courses, integral to the interdisciplinary program at the Center for Mathematics and Data Science Education and Research, have successfully introduced students from diverse academic backgrounds to the fundamentals of programming and data science.
 
In crafting this textbook, we conducted an extensive review of curricula from leading global institutions in related fields. Our objective was to equip students with a knowledge base that not only meets but exceeds international standards, preparing them for success on the global stage. We recognize that mastery in data science and AI requires a multifaceted approach: a solid grasp of core concepts, a deep understanding of the underlying mathematics, and proficiency in practical application through programming.
 
The book's structure reflects this holistic approach. It elucidates key concepts and representative methods in data analysis, exploring their mathematical foundations while simultaneously offering hands-on experience through Python programming exercises. The content builds upon the mathematical groundwork typically established in the initial years of university education, encompassing linear algebra, probability, statistics, and analysis. Our goal is to foster a robust skill set in mathematics, data science, and AI that is both theoretically sound and practically applicable.
 
Each chapter presents a careful balance of theory and practice. Beginning with foundational concepts, the text progresses to practical examples and exercises, allowing readers to apply their knowledge using Python. This methodology ensures a comprehensive understanding of both theoretical principles and their real-world applications. The book covers a broad spectrum of essential topics in data analysis, including data preprocessing, exploratory data analysis, statistical inference, machine learning algorithms, and advanced data visualization techniques. This comprehensive coverage provides students with a solid foundation to build upon as they progress in their studies or careers in data science and AI.
 
We aspire for this textbook to serve as an indispensable resource for students embarking on their journey in data analysis, mathematics, data science, and AI. By providing a solid grounding in both theoretical concepts and practical techniques, this textbook prepares readers to tackle the challenges of our data-driven world with confidence and innovation, setting the stage for future advancements in these rapidly evolving fields.
 

(Written by MORI Junichiro, Associate Professor, Graduate School of Information Science and Technology / 2024)