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| 008 |
210123 ||| eng |
| 050 |
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4 |
|a QA76.9.D343
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| 100 |
1 |
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|a Lučanin, Dražen
|
| 245 |
0 |
0 |
|a Jupyter Notebook for data science
|c Dražen Lučanin
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| 260 |
|
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|a [Place of publication not identified]
|b Packt
|c 2018
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| 300 |
|
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|a 1 streaming video file (3 hr., 11 min., 18 sec.)
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| 653 |
|
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|a Science / Data processing / fast / (OCoLC)fst01108207
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| 653 |
|
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
|
| 653 |
|
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|a Science / Data processing / http://id.loc.gov/authorities/subjects/sh85118562
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| 653 |
|
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|a Visualisation de l'information
|
| 653 |
|
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|a Data mining / fast / (OCoLC)fst00887946
|
| 653 |
|
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|a Information visualization / fast / (OCoLC)fst00973185
|
| 653 |
|
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|a Data mining / http://id.loc.gov/authorities/subjects/sh97002073
|
| 653 |
|
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|a Sciences / Informatique
|
| 653 |
|
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|a Information visualization / http://id.loc.gov/authorities/subjects/sh2002000243
|
| 653 |
|
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|a Python (Langage de programmation)
|
| 653 |
|
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|a Python (Computer program language) / fast / (OCoLC)fst01084736
|
| 653 |
|
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|a Exploration de données (Informatique)
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
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|b OREILLY
|a O'Reilly
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| 500 |
|
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|a Title from title screen (viewed September 25, 2018). - Date of publication from resource description page
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/videos/~/9781789135411/?ar
|x Verlag
|3 Volltext
|
| 082 |
0 |
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|a 500
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| 082 |
0 |
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|a 000
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| 520 |
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|a "This video course will help you get familiar with Jupyter Notebook and all of its features to perform various data science tasks in Python. Jupyter Notebook is a powerful tool for interactive data exploration and visualization and has become the standard tool among data scientists. In the course, we will start from basic data analysis tasks in Jupyter Notebook and work our way up to learn some common scientific Python tools such as pandas, matplotlib, and plotly. We will work with real datasets, such as crime and traffic accidents in New York City, to explore common issues such as data scraping and cleaning. We will create insightful visualizations, showing time-stamped and spatial data. By the end of the course, you will feel confident about approaching a new dataset, cleaning it up, exploring it, and analyzing it in Jupyter Notebook to extract useful information in the form of interactive reports and information-dense data visualizations."--Resource description page
|