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Introduction to Python Programming Practice: Preparation for Data Analysis/Python??????--???????
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Introduction to Python Programming Practice: Preparation for Data Analysis/Python??????--???????

          
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International Edition


About the Book
This book is written for readers who have not formally studied computer software programming but frequently need to perform data analysis. 本书为没有专门学过计算机软件编程但又要经常做数据分析的读者所编写 A programming language is an essential tool for data science. Programming is not the goal itself; rather, it serves data science. We aim to learn a programming language through the process of handling data. This book adopts a goal-oriented programming training approach, guiding readers to learn programming while completing tasks using software. Naturally, our objective is data analysis, not other practices such as web browsing, animation, or webpage generation. Therefore, the training content here is also designed around the needs of data processing and related graphical plotting. Our training progresses from simple numerical and logical operations, and linear algebra operations, to machine learning model fitting-a gradual process from the elementary to the more advanced. This book is written for readers who have not formally studied computer software programming but frequently need to perform data analysis, enabling them to use Python programming to handle various data-related tasks. The target audience includes students and teachers in fields involving statistics or its extensions (such as econometrics, etc.), as well as the broader readership engaged in data analysis. 编程语言是数据科学重要的工具, 编程不是目的, 编程是为数据科学服务。我们希望通过处理数据来学习编程语言, 本书以目标导向的编程训练方式, 引导读者在使用软件完成任务过程中学会编程。当然, 我们的目标是数据分析而不是诸如漫游、动画、生成网页等其他实践, 因此, 这里的训练内容也是基于数据处理及相关画图的需要。我们的训练是从简单的数字及逻辑运算、线性代数运算到机器学习模型拟合的由简入繁、循序渐进的过程。 本书为没有专门学过计算机软件编程又要经常做数据分析的读者所编写, 让人们学会用 Python 编程处理各种数据课题。本书的对象群体包括涉及统计或其延伸领域 (比如计量经济等) 的师生及做数据分析的广大读者。 Wu Xizhi graduated with a bachelor's degree from the Department of Mathematics and Mechanics at Peking University and earned his Ph.D. in Statistics from the University of North Carolina. He is a Professor and Doctoral Supervisor at the School of Statistics, Renmin University of China. He has taught at several prestigious institutions, including Nankai University, Peking University, the University of California, and the University of North Carolina. Zhang Min holds a Ph.D. in Statistics from Yunnan University of Finance and Economics and currently works at Chongqing Technology and Business University. As an author, she has published multiple papers in journals indexed in CSSCI, CSCD, and SCI. She has led or participated in numerous national, provincial, and ministerial-level research projects and has co-authored several textbooks on data science as the second author. 吴喜之,北京大学数学力学系本科毕业,北卡罗来纳大学统计学博士。中国人民大学统计学院教授,博士生导师。曾在南开大学、北京大学、加利福尼亚大学、北卡罗来纳大学等多所著名学府执教。 张敏,云南财经大学统计学博士, 现就职于重庆工商大学。以作者公开发表了CSSCI/CSCD/SCI 多篇文章, 主持或参与国家及省部级课题多项, 以第二作者出版关于数据科学的教材多部。
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About the Book

This book is written for readers who have not formally studied computer software programming but frequently need to perform data analysis. 本书为没有专门学过计算机软件编程但又要经常做数据分析的读者所编写 A programming language is an essential tool for data science. Programming is not the goal itself; rather, it serves data science. We aim to learn a programming language through the process of handling data. This book adopts a goal-oriented programming training approach, guiding readers to learn programming while completing tasks using software. Naturally, our objective is data analysis, not other practices such as web browsing, animation, or webpage generation. Therefore, the training content here is also designed around the needs of data processing and related graphical plotting. Our training progresses from simple numerical and logical operations, and linear algebra operations, to machine learning model fitting-a gradual process from the elementary to the more advanced. This book is written for readers who have not formally studied computer software programming but frequently need to perform data analysis, enabling them to use Python programming to handle various data-related tasks. The target audience includes students and teachers in fields involving statistics or its extensions (such as econometrics, etc.), as well as the broader readership engaged in data analysis. 编程语言是数据科学重要的工具, 编程不是目的, 编程是为数据科学服务。我们希望通过处理数据来学习编程语言, 本书以目标导向的编程训练方式, 引导读者在使用软件完成任务过程中学会编程。当然, 我们的目标是数据分析而不是诸如漫游、动画、生成网页等其他实践, 因此, 这里的训练内容也是基于数据处理及相关画图的需要。我们的训练是从简单的数字及逻辑运算、线性代数运算到机器学习模型拟合的由简入繁、循序渐进的过程。 本书为没有专门学过计算机软件编程又要经常做数据分析的读者所编写, 让人们学会用 Python 编程处理各种数据课题。本书的对象群体包括涉及统计或其延伸领域 (比如计量经济等) 的师生及做数据分析的广大读者。 Wu Xizhi graduated with a bachelor's degree from the Department of Mathematics and Mechanics at Peking University and earned his Ph.D. in Statistics from the University of North Carolina. He is a Professor and Doctoral Supervisor at the School of Statistics, Renmin University of China. He has taught at several prestigious institutions, including Nankai University, Peking University, the University of California, and the University of North Carolina. Zhang Min holds a Ph.D. in Statistics from Yunnan University of Finance and Economics and currently works at Chongqing Technology and Business University. As an author, she has published multiple papers in journals indexed in CSSCI, CSCD, and SCI. She has led or participated in numerous national, provincial, and ministerial-level research projects and has co-authored several textbooks on data science as the second author. 吴喜之,北京大学数学力学系本科毕业,北卡罗来纳大学统计学博士。中国人民大学统计学院教授,博士生导师。曾在南开大学、北京大学、加利福尼亚大学、北卡罗来纳大学等多所著名学府执教。 张敏,云南财经大学统计学博士, 现就职于重庆工商大学。以作者公开发表了CSSCI/CSCD/SCI 多篇文章, 主持或参与国家及省部级课题多项, 以第二作者出版关于数据科学的教材多部。


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Product Details
  • ISBN-13: 9787300302379
  • Publisher: China National Publications Import & Export C
  • Publisher Imprint: China National Publications Import & Export C
  • Edition: Unabridged edition
  • Language: Chinese
  • Returnable: N
  • Sub Title: Preparation for Data Analysis/Python??????--???????
  • Width: 178 mm
  • ISBN-10: 7300302378
  • Publisher Date: 15 Feb 2022
  • Binding: Paperback
  • Height: 254 mm
  • No of Pages: 266
  • Spine Width: 18 mm
  • Weight: 625 gr


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