Abstract: This book addresses the data-processing demands of the big data era, and focuses on the training needs of applied statistics master’s students. It integrates classical statistical theory with the characteristics of big data to construct a coherent knowledge framework. Organized around the logic of data processing, the book systematically explains key techniques such as sampling methods, outlier detection, and variable selection, and covers both univariate and multivariate data visualization. It further extends to high-dimensional data, spatial data visualization, and principles of graphic optimization. Through case-based demonstrations, the book presents the full workflow of exploratory data analysis and introduces spatial analytical methods such as space-time scanning and regression modeling, while emphasizing the application of programming languages in data practice. The content is suitable for master's students in statistics, and professionals from related fields engaged in data analysis.