Python Data Science
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...
保存先:
| 第一著者: | |
|---|---|
| 団体著者: | |
| フォーマット: | 電子媒体 eBook |
| 言語: | 英語 |
| 出版事項: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2023.
|
| 版: | 1st ed. 2023. |
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/978-981-19-7702-2 |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-981-19-7702-2 | ||
| 003 | DE-He213 | ||
| 005 | 20260810155052.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 230630s2023 si | o |||| 0|eng d | ||
| 020 | |a 9789811977022 |9 978-981-19-7702-2 | ||
| 024 | 7 | |a 10.1007/978-981-19-7702-2 |2 doi | |
| 050 | 4 | |a Q336 | |
| 072 | 7 | |a UN |2 bicssc | |
| 072 | 7 | |a COM021000 |2 bisacsh | |
| 072 | 7 | |a UN |2 thema | |
| 082 | 0 | 4 | |a 005.7 |2 23 |
| 100 | 1 | |a Borjigin, Chaolemen. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Python Data Science |h [electronic resource] / |c by Chaolemen Borjigin. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Singapore : |b Springer Nature Singapore : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (XII, 345 p. 1 illus.). | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a 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. | |
| 520 | |a 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). | ||
| 588 | |a Print version record. | ||
| 650 | 0 | |a Artificial intelligence |x Data processing. | |
| 650 | 1 | 4 | |a Data Science. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9789811977015 |
| 776 | 0 | 8 | |i Printed edition: |z 9789811977039 |
| 776 | 0 | 8 | |i Printed edition: |z 9789811995910 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-981-19-7702-2 |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||