Python Data Science
Borjigin, Chaolemen.
Python Data Science [electronic resource] / by Chaolemen Borjigin. - 1st ed. 2023. - XII, 345 p. 1 illus. online resource.
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.
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).
9789811977022
10.1007/978-981-19-7702-2 doi
Artificial intelligence--Data processing.
Data Science.
Q336
005.7
Python Data Science [electronic resource] / by Chaolemen Borjigin. - 1st ed. 2023. - XII, 345 p. 1 illus. online resource.
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.
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).
9789811977022
10.1007/978-981-19-7702-2 doi
Artificial intelligence--Data processing.
Data Science.
Q336
005.7