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Name: Jupyter Notebooks for Data Science
--client pyconza --show pyconza2018 --room baobab 14346 --force
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Author(s):	Ari Ramkilowan
Location	Baobab
Date	oct Thu 11	Days Raw Files
Start	13:55	First Raw Start	13:30
Duration	00:45:00	Offset	0:24:23
End	14:40	Last Raw End	15:00
Chapters	00:00 0:06:47 0:36:46
Total cuts_time	38 min.

https://za.pycon.org/talks/12-jupyter-notebooks-for-data-science/
raw-playlist raw-mp4-playlist encoded-files-playlist host archive tweet mp4 svg png

final audio

assets release.pdf Python_as_a_tool_for_ehealth_systems.json
logs
Admin: episode episode list cut list raw files day marks day marks day image_files

State:
Locked:	clear this to unlock
Locked by:	user/process that locked.
Start:	initially scheduled time from master, adjusted to match reality
Duration:	length in hh:mm:ss
Name:	Video Title (shows in video search results)
Emails:	email(s) of the presenter(s)
Released:	has someone authorised pubication
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Channelcopy:	m=mono, 01=copy left to right, 10=right to left, 00=ignore.
Thumbnail:	filename.png
Description:	This talk is intended for beginner and intermediate data scientists/ analysts/ engineers, although I hope that even experienced data scientists can gain something from the talk. The talk will focus on using Jupyter notebooks in data science applications. I will discuss the basics of how to get it up and running and the common features like using markup and code in the same notebook, I will highlight the advantages of working in a notebook rather than a traditional IDE. I will also discuss other features like using code snippets, autocomplete, linting and creating a table of contents. Inserting images and videos into a notebook along side your code can be a handy way of learning something new. I will end the talk with a look into jupyterlab. Attendees of this course will gain an understanding and appreciation of the quick prototyping that is afforded to you when using Jupyter notebooks in your data science pipeline. Especially when it comes to exploratory data analysis. I want to be able to showcase all the common features of Jupyter notebooks but also some less known ones, so that there everyone attending the talk will learn something. markdown
Comment:	lots of microphone crackle. Right channel could be better. production notes

2018-10-11/13_30_37.ts Apply: 13:30:37 - 13:53:19 ( 00:22:42 ) S: 13:30:37 - E: 14:00:37 D: 00:30:00 (End: 1362.0) show more...

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2018-10-11/13_30_37.ts Apply: 13:53:19 - 13:53:50 ( 00:00:31 ) S: 13:30:37 - E: 14:00:37 D: 00:30:00 (Start: 1362.0) (End: 1393.0) show more...

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	13:30:37		seconds: 1362.0 Wall: 13:53:19
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2018-10-11/13_30_37.ts Apply: 13:53:49 - 14:00:37 ( 00:06:47 ) S: 13:30:37 - E: 14:00:37 D: 00:30:00 (Start: 1392.288582) show more...

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2018-10-11/14_00_37.ts Apply: 14:00:37 - 14:30:36 ( 00:29:59 ) S: 14:00:37 - E: 14:30:36 D: 00:29:59 show more...

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2018-10-11/14_30_37.ts Apply: 14:30:37 - 14:32:16 ( 00:01:39 ) S: 14:30:37 - E: 15:00:36 D: 00:29:59 (End: 99.0) show more...

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2018-10-11/14_30_37.ts Apply: 14:32:16 - 14:33:31 ( 00:01:15 ) S: 14:30:37 - E: 15:00:36 D: 00:29:59 (Start: 99.0) (End: 174.0) show more...

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2018-10-11/14_30_37.ts Apply: 14:33:31 - 14:33:55 ( 00:00:24 ) S: 14:30:37 - E: 15:00:36 D: 00:29:59 (Start: 174.0) (End: 198.0) show more...

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2018-10-11/14_30_37.ts Apply: 14:34:06 - 14:34:37 ( 00:00:31 ) S: 14:30:37 - E: 15:00:36 D: 00:29:59 (Start: 209.0) (End: 240.0) show more...

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2018-10-11/14_30_37.ts Apply: 14:34:37 - 14:36:44 ( 00:02:07 ) S: 14:30:37 - E: 15:00:36 D: 00:29:59 (Start: 240.0) (End: 367.0) show more...

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