Recently, the "Top 100 Good Books of 2025" event, co-launched by JD Books and Guangming Online, has come to a successful conclusion. This list of good books, completely selected by readers' votes, not only provides a yearly reading guide for the general public but also offers creators a reference to gain insights into market trends and understand reader preferences. Among the "Top 100 Good Books of 2025," covering various categories such as literature, social sciences, children's books, education, economics, and management, the CDA Level 1 textbook CDA Business Data Analysis (4th Edition), edited by the CDA Data Science Research Institute and published by China Machine Press, ranked among the top of the overall list due to its excellent professional quality, wide reader reputation, and outstanding market performance. This is not only the love of readers for our book but also the recognition and praise for the CDA team! We are greatly surprised that a book filled with code, formulas, and logical frameworks can compete alongside literary novels and exquisite picture albums... and even rank high and win an award! So far, the answer written by the readers themselves has been given.

The vitality of a good book always comes from being seen, discussed, and cherished. This award indicates that the authoritative status and practical value of CDA data analysts in the field of data analysis education have been recognized by both the market and readers.

CDA Business Data Analysis (4th Edition) adapts to the workplace needs of the AI era, and its content updates mainly focus on two core directions:

1. Keeping up with the dual frontiers of technology and business Strengthening the cultivation of AI-irreplaceable thinking abilities

It focuses on updating content related to business thinking and data thinking. In the current context of the widespread application of AI, machines can replace operational work such as data cleaning and basic modeling code writing, but human thinking processes and business understanding capabilities are irreplaceable by machines, which are also the core competitiveness of data analysis talents in the future.

Incorporating cutting-edge technologies and methods

It integrates new technologies and methods that have received high industry attention and wide application in recent years into the textbook, ensuring that learners master skills that can directly meet the current actual needs of enterprises and avoid the disconnection between learned knowledge and the industry.

2. Target audience It is suitable for beginners with no foundation or just starting to接触 data analysis, college students, professionals hoping to transition to data analysis positions, and candidates planning to take the CDA Level 1 certification. There are no restrictions on academic background or major, helping to quickly build a data analysis knowledge framework.

3. Book features Newly added business analysis framework content to cultivate underlying thinking abilities. Breaking the limitation of traditional textbooks that "emphasize tools over thinking," the new edition of the textbook has achieved an advanced transformation from "skill-oriented" to "thinking-oriented." It introduces a complete business analysis framework process in the Level 1 textbook, decomposes the thinking process of each framework module, and helps zero-based learners establish scientific data analysis thinking from the introductory stage, rather than just mastering tool operations. The book inserts hundreds of charts and mind maps to facilitate learning.

Zero-based friendly design to lower the entry threshold. Continuing the popular explanation style of the 2022 edition textbook, it refines the operation steps of core tools such as Excel and SQL, with supporting detailed screenshots and error排查 guides. Cases cover basic scenarios in multiple industries, paired with downloadable real data sources, allowing beginners to quickly get started and achieve "learn and use immediately."

The structure of the new edition textbook is more systematic. The new edition is divided into three major parts - Principles, Technology, and Management.

The Principles part helps you establish an overall understanding of "what data analysis is all about" and tells you how to analyze different types of problems. For example, strategic analysis focuses on macro operational indicators, while operational analysis focuses on user behavior and process transformation.

The Technology part covers key capabilities such as descriptive statistics, databases, tag portraits, and time series. Instead of simply teaching tools, it combines specific business problems for implementation.

The Management part introduces essential qualities for enterprise data teams such as data governance, data modeling, and indicator caliber management, which were almost completely not involved in the old edition.

The content design of attribution analysis and strategy output is more complete. Previously, we would end after checking data and finding phenomena; now we also need to explain clearly "why" and propose "what to do." The new edition textbook introduces a three-layer attribution model:

• Qualitative attribution: relying on experience judgment, which is the starting point;
• Indicator attribution: splitting the indicator structure and analyzing sub-factor changes;
• Model attribution: using methods such as regression and classification to quantitatively identify influencing factors.

Moreover, it clearly points out that attribution is not the end, but the starting point for strategy formulation. This is in line with the requirements of specific work. You have to write suggestions that the business can understand and execute, such as "adding guidance components on pages with high bounce rates," instead of just saying "high bounce rates" and submitting the work.

In the new year, CDA will continue to uphold the concept of "professionalism, innovation, and practice," closely follow industry dynamics and technological developments, continuously optimize and update the knowledge system, and launch more high-quality learning resources.