Python and Data Science

Name: Python and Data Science
ISBN: 9787567544024

王仁武East China Normal University Press · ChinaApr 2017

3.6

Word count: 111K words
Chapters: 13
ISBN: 9787567544024

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Summary

This book takes a hands-on approach to development with Python, covering: creating analytical applications with an agile big data development methodology; building value across a series of agile cycles with the data-to-value stack; extracting features and insights from a single dataset using various data structures; visualizing data with charts and presenting it from multiple angles through interactive reports; and using historical data to make predictions and turning those predictions into action.

Editor's review

Python and Data Science is a systematic, practice-oriented guide for both beginners and those looking to advance. Drawing on his solid academic background and hands-on teaching experience, Professor Wang Renwu lays out a clear path from Python programming fundamentals to core data science applications. The book's greatest strength lies in its balance: it avoids both the dryness of purely theoretical textbooks and the scatteredness of tool manuals. It deftly integrates explanations of core libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn into the complete workflow of data acquisition, cleaning, analysis, visualization, and machine learning, so that readers understand in practice where each tool fits and what role it plays in the data science chain.

The cases in the book are chosen to be close to real life, avoiding overly academic abstract problems, which helps readers build a mindset for solving real data problems. Particularly commendable is that, when introducing machine learning algorithms, the author emphasizes intuitive explanations of principles combined with Python implementations, lowering the barrier to understanding. The book is suitable as a textbook for university courses related to data science, and also as a roadmap for self-learners building a systematic body of knowledge. Its depth and breadth are well judged: after finishing it, readers can still keep it on their desks as a reference to revisit when specific problems arise. Overall, this is an excellent work that strikes a good balance between rigor and readability.

Who it's for

A good fit for

Beginners with basic programming concepts who want to systematically learn how to use Python to process data and analyze problems
University students in computer science, statistics, economics and management, and related majors who are taking data science courses
Working professionals in data analysis, business analysis, and similar roles who want to improve their Python data analysis skills
Learners interested in machine learning who want a complete introduction from data preprocessing to model building

Not a good fit for

Researchers seeking in-depth discussion of the underlying mathematical principles and cutting-edge advances of a particular machine learning algorithm
Complete beginners with no programming background who want to start from zero and learn basic Python syntax

Reading tips

Reading in chapter order is recommended; the Python and library fundamentals in the first few chapters are essential preparation for later content
Hands-on practice is crucial: be sure to reproduce the book's code and cases in a programming environment while reading
After finishing the basic machine learning chapters, try analyzing datasets in a field that interests you using the book's methods to consolidate your skills

About the publisher

East China Normal University Press China · Founded in 1957

Adhering to the publishing purpose of "broad education," East China Normal University Press extends beyond its three main areas of textbooks, academic works and general-interest books, and its catalogue clearly reflects a vision that bridges Chinese and foreign humanities and social sciences. In foreign literature it offers classics such as Yasunari Kawabata's Snow Country and The Old Capital (translated by Chen Dewen), Jane Austen's Pride and Prejudice, and Rilke's annotated edition of The Notebooks of Malte Laurids Brigge (translated by Chen Zao); in architecture, Chen Congzhou's two-volume Appreciation Dictionary of Chinese Gardens; in social psychology, The Social Animal: 12th Edition; and it also includes philosophy titles, traditional-culture works such as the six-volume Paintings for the Protection of Life, and education books. Its authors include renowned Chinese and foreign writers and skilled translators, showing a publishing profile that balances classic translation, scholarly accumulation and public reading. Public information is limited; the above is based only on the books in the catalogue.

版权信息
本书简介
基础编
1 数据科学简介
2 Python基础知识
分析编
3 Python数据获取与数据预处理
4 利用Python进行数据分析
挖掘编
5 利用Python进行数据挖掘
6 利用Python进行文本挖掘
提高编
7 Python与海量数据处理简介

FAQ

How much math background does this book require?

The book is application- and practice-oriented. The mathematical knowledge involved (such as linear algebra and basic probability and statistics) is explained as necessary and intuitively when it is used. Readers with undergraduate-level mathematics can read it smoothly; the focus is on understanding concepts and application methods.

How is this book different from a pure Python programming book or a machine learning theory book?

The core focus of this book is the "data science workflow," connecting the entire process from data to knowledge. Compared with books that teach only Python syntax, it focuses more on libraries and scenarios for data analysis; compared with advanced machine learning theory books, it emphasizes engineering implementation and tool application. It fills the gap between the two.

Which version of Python are the code and cases in the book based on?

According to the book's information, the content is mainly based on Python 3.x and uses current mainstream library versions in the data science field. Although the specific versions may depend on the publication date, the core syntax and library usage logic are highly continuous, so readers learning with a newer version environment should generally not encounter obstacles.

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About the author

王仁武 Author

Wang Renwu is an author about whom limited public information is available, with two works held in the collection. Judging from these works, his writing spans two rather different areas. One is programming and data analysis, represented by Python and Data Science, which is aimed at readers who wish to begin or deepen their data-processing skills with Python. The other is Digital and Intelligent Resource Discovery, which discusses the combination of resource discovery with digital intelligence and falls into the overlapping field of social culture and information management. The two works are classified respectively under examination and social culture, suggesting content that combines practical teaching with reflection on the industry. Available materials do not provide his nationality, dates of birth and death, education, career, or awards, so a full biography cannot be outlined. Based solely on his works, he focuses on the practical use of technical tools and the digital transformation of the information resource field, with a style that emphasizes organizing knowledge and introducing methods, suitable for readers with learning or professional needs.