Introduction to Data Science

Grab your front-row seat to the world of data with Introduction to Data Science, authored by Gaoyan Ou, Zhanxing Zhu, Bin Dong, and Weinan E. This concise yet comprehensive guide bridges theory and practice to make core data science concepts accessible to students, early-career professionals, and lifelong learners.

Inside, you’ll find clear explanations of probability and statistics, exploratory data analysis, supervised and unsupervised learning, model evaluation, and essential data visualization techniques. The authors emphasize intuitive understanding alongside mathematical rigor, using real-world examples to demystify algorithms and show how to turn raw data into actionable insight. Practical workflows for data cleaning, feature engineering, and model selection are presented in a way that’s immediately applicable to projects across industries.

Whether you’re preparing for university courses, reskilling for analytics roles, or seeking a reliable reference for applied machine learning, this book equips you with the foundational tools and thinking patterns data scientists need. Its balanced blend of theory, examples, and problem-oriented exposition makes complex topics approachable without sacrificing depth.

Relevant to readers in North America, Europe, Asia and beyond, Introduction to Data Science is designed for global learners who want a structured pathway into data analysis, predictive modeling, and data-driven decision making. Ready to build practical data skills and advance your career? Add this essential guide to your bookshelf and start transforming data into insight today.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.