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Introduction: The new book is on the market and occupies the first place in the japanese amazon statistical book Many books on information science, machine learning, content full of modeling techniques, perfect case analysis, but forget more important material sourcing.It is only with the information in hand that machine learning is the first step to successful modeling. Learning deeply can't save you without knowing the data; on the contrary, collecting the data correctly and modeling easily is not troubling. How do you know the information?Of course, starting with statistical concepts.Must all study statistics look at abstract and complex mathematics?No!The examples used in this book will tell you why statistics define loafers so much, and why techniques use loafers so much.When readers understand the ideas behind statistics, they do not lose in complex mathematics.In addition, unlike the typical sales book, which always shows the results of the program in beautiful cases, the book uses quite a few examples of mistakes that experts will also make. it is believed that readers can learn both success and failure cases and gain a more comprehensive understanding of the concept of the analytical process of information science. This book introduces the basic concepts of many techniques in data analysis, such as variance analysis, partial correlation coefficient, self-correlation function, Bonferroni correction, Holm correction, randomized controlled test, breakpoint regression analysis, tendency score matching, T-verification, F verification, layered multi-stage sampling, etc., and directly hits the thinking of analytical techniques in the process rather than just showing beautiful mathematics. This book will also describe the qualities of being a data scientist to avoid identifying errors, survivors' errors, choices, publishing errors, volunteers' errors, availability errors, optimism errors, normal errors, the latter fallacies, gamblers' errors, simpson errors, p-hacking, hawking, unilateral arguments, hawthorne effects, and the bi malone effect.The trap. This book will be your best solution if the reader has ever had the following questions. ● I want to know how to collect high-quality data to speed up machine learning modeling ● I 'd like to know what kind of data is suitable for what analytical techniques ● I wonder if there are any unconscious biases in the analysis process ● I want to know what the results of the analysis mean ● I wonder why data analysis can solve the problem In addition, the chinese version also provides free Python downloads, allowing readers to practice the examples of books and deepen their understanding of knowledge. This book is consistent with the idea of interpreting each link from data collection, data analysis, data analysis, and information, so that you can take the information scientist's kangzhuang road! Features of the book: ● Positive comments (listed in alphabetical order) Assistant professor of st