Python Data Science
Autor Chaolemen Borjiginen Limba Engleză Hardback – iul 2023
Unlike conventional textbooks, it is based on Markdown and uses full-color printing and a code-centric approach to highlight the 3C principles in data science: creative design of data solutions, curiosity about the data lifecycle, and critical thinking regarding data insights. Q&A-based knowledge maps, tips and suggestions, notes, as well as warnings and cautions are employed to explain the key points, difficulties, and common mistakes in Python programming for data science. In addition, it includes suggestions for further reading.
This textbook provides an open-source community via GitHub, and the course materials are licensed for free use under the following license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).
More teaching materials including Codes, Datasets, Slides, Syllabus can be found at https://github.com/LemenChao/PythonDataScience
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 423.73 lei 38-44 zile | |
| Springer – 2 iul 2024 | 423.73 lei 38-44 zile | |
| Hardback (1) | 502.56 lei 3-5 săpt. | +50.16 lei 7-13 zile |
| Springer – iul 2023 | 502.56 lei 3-5 săpt. | +50.16 lei 7-13 zile |
Preț: 502.56 lei
Preț vechi: 628.20 lei
-20%
Puncte Express: 754
Preț estimativ în valută:
88.80€ • 102.36$ • 77.53£
88.80€ • 102.36$ • 77.53£
Carte disponibilă
Livrare economică 25 aprilie-09 mai
Livrare express 11-17 aprilie pentru 60.15 lei
Specificații
ISBN-13: 9789811977015
ISBN-10: 9811977011
Pagini: 360
Ilustrații: XII, 345 p. 1 illus.
Dimensiuni: 215 x 285 x 25 mm
Greutate: 1.13 kg
Ediția:2023
Editura: Springer
Locul publicării:Singapore, Singapore
ISBN-10: 9811977011
Pagini: 360
Ilustrații: XII, 345 p. 1 illus.
Dimensiuni: 215 x 285 x 25 mm
Greutate: 1.13 kg
Ediția:2023
Editura: Springer
Locul publicării:Singapore, Singapore
V-ar putea interesa
-
Python for ProgrammersPaul Deitel-20%Preț: 349.64 lei437.05 lei -
-23%Preț: 701.25 lei910.72 lei -
Foundational Python for Data ScienceKennedy Behrman-20%Preț: 343.76 lei429.69 lei -
-18%Preț: 688.22 lei839.29 lei -
Data Science and Big Data AnalyticsEmc Education Services-20%Preț: 377.17 lei471.46 lei -
Data Science Programming All-In-One for DummiesJohn Paul MuellerPreț: 216.31 lei -
R-TiculateMartin Bader-23%Preț: 624.36 lei810.87 lei -
Data Science For DummiesLillian Pierson-20%Preț: 152.40 lei190.51 lei -
Python for Data Science For DummiesJohn Paul Mueller-20%Preț: 143.41 lei179.27 lei -
Data Science Essentials for DummiesLillian Pierson-20%Preț: 62.50 lei78.13 lei -
Statistical Analysis with Python for DummiesJoseph SchmullerPreț: 188.00 lei -
Data Science BookcampLeonard Apeltsin-20%Preț: 341.68 lei427.09 lei -
Python for Data Science: A Hands-On IntroductionYuli Vasiliev-20%Preț: 270.70 lei338.37 lei -
-38%Preț: 185.26 lei298.31 lei -
-36%Preț: 274.73 lei426.89 lei -
-38%Preț: 174.08 lei281.84 lei -
PythonMartin Czygan-20%Preț: 693.27 lei866.59 lei -
-15%Preț: 572.64 lei673.69 lei -
The Data Science Design ManualSteven S. Skiena-20%Preț: 398.51 lei498.15 lei
Cuprins
1. Python and Data Science.- 2. Basic Python Programming for Data Science.- 3. Advanced Python Programming for Data Science.- 4. Data preprocessing and wrangling.- 5. Data analysis algorithms and models.
Notă biografică
Chaolemen Borjigin is an associate professor at Renmin University of China, and one of the top 50 data science influencers in China. He is a member of the Information System Special Committee of the Chinese Computer Federation, deputy director of the Expert Committee of the National University Artificial Intelligence and Big Data Innovation Alliance of China, executive editorial board member of the academic journal Computer Science, and deputy editor-in-chief of the international journal Data Science and Informatics.
He is the author of Data Science (Tsinghua University Press, 2016), the first monograph in China that systematically introduced data science principles, theories, methods, technologies, and tools. His textbook Data Science Theory and Practice (Second Edition) was recognized as a high-quality textbook by the Beijing Municipal Education Commission in 2019. His course Introduction to Data Science is one of the China National First-ClassUndergraduate Courses.
He is the author of Data Science (Tsinghua University Press, 2016), the first monograph in China that systematically introduced data science principles, theories, methods, technologies, and tools. His textbook Data Science Theory and Practice (Second Edition) was recognized as a high-quality textbook by the Beijing Municipal Education Commission in 2019. His course Introduction to Data Science is one of the China National First-ClassUndergraduate Courses.
Textul de pe ultima copertă
Rather than presenting Python as Java or C, this book focuses on the essential Python programming skills for data scientists and advanced methods for big data analysts.
Unlike conventional textbooks, it is based on Markdown and uses full-color printing and a code-centric approach to highlight the 3C principles in data science: creative design of data solutions, curiosity about the data lifecycle, and critical thinking regarding data insights. Q&A-based knowledge maps, tips and suggestions, notes, as well as warnings and cautions are employed to explain the key points, difficulties, and common mistakes in Python programming for data science. In addition, it includes suggestions for further reading.
This textbook provides an open-source community via GitHub, and the course materials are licensed for free use under the following license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).
Unlike conventional textbooks, it is based on Markdown and uses full-color printing and a code-centric approach to highlight the 3C principles in data science: creative design of data solutions, curiosity about the data lifecycle, and critical thinking regarding data insights. Q&A-based knowledge maps, tips and suggestions, notes, as well as warnings and cautions are employed to explain the key points, difficulties, and common mistakes in Python programming for data science. In addition, it includes suggestions for further reading.
This textbook provides an open-source community via GitHub, and the course materials are licensed for free use under the following license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).
Caracteristici
A best-selling book on Python programming for data science and big data analytics in China Combines Python coding with data science thinking Provides an open-source community via GitHub, and course materials licensed for free consumption