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| 008 | 230630s2023 si | s |||| 0|eng d | ||
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_a10.1007/978-981-19-7702-2 _2doi |
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_aQA76.7 _b2023 EB |
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_aBorjigin, Chaolemen _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689646 |
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| 245 | 1 | 0 |
_aPython Data Science _cby Chaolemen Borjigin |
| 250 | _a1st ed 2023 | ||
| 264 | 1 |
_aSingapore _bSpringer Nature _c2023 |
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| 300 | _a1 recurso en línea | ||
| 336 |
_atexto _btxt _2rdacontent |
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| 337 |
_aelectrónico _bc _2rdamedia |
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| 338 |
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| 505 | 0 | _a1. 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. | |
| 520 | _aRather 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). | ||
| 988 | _aSpringer_Computer_2023 | ||
| 650 | 7 |
_2embne _9678605 _aLenguajes de programación |
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| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-7702-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
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| 998 |
_b02/2024 _dz _ek _zSI |
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