Author(s):	Andrew Collier
Location	Cedarwood
Date	oct Wed 10	Days Raw Files
Start	13:30	First Raw Start	error-in-template
Duration	04:00:00	Offset	None
End	17:30	Last Raw End
Chapters
Total cuts_time	None min.

https://za.pycon.org/talks/38-introduction-to-python-for-data-science-part-2/
raw-playlist raw-mp4-playlist encoded-files-playlist mp4 svg png

assets release.pdf Introduction_to_Python_for_Data_Science_Part_2.json
logs
Admin: episode episode list cut list raw files day marks day marks day image_files

State:	--------- borked edit encode push to queue post richard review 1 email review 2 make public tweet to-miror conf done
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:	Unknown Yes No has someone authorised pubication
Normalise:
Channelcopy:	m=mono, 01=copy left to right, 10=right to left, 00=ignore.
Thumbnail:	filename.png
Description:	**This is the second half of the 2-session tutorial.** Python is a popular platform for doing Data Science. The two dominant libraries, <a href="https://pandas.pydata.org/">pandas</a> and <a href="http://scikit-learn.org/stable/">sklearn</a>, provide extensive functionality for data preparation, data manipulation and Machine Learning. This workshop will provide an introduction to using these libraries. Specifically we’ll cover the following topics: <ul> <li>What is Data Science?</li> <li>Grabbing data from various sources</li> <li>Working with <code>Series</code> and <code>DataFrame</code> objects</li> <li>Dealing with funky data (missing data and outliers)</li> <li>Overview of Machine Learning</li> <li>Keeping it simple using Nearest Neighbours</li> <li>Capturing a trend: <code>LinearRegression</code></li> <li>Predicting categories: <code>DecisionTreeClassifier</code></li> <li>Binary outcomes: <code>LogisticRegression</code></li> <li>Using <code>Pipeline</code> to streamline your workflow</li> <li>Cross Validation</li> </ul> The workshop will be intensely hands on, so you will definitely need a laptop. Instructions for getting everything set up will be provided prior to the workshop. No prior knowledge of Data Science or Machine Learning is assumed, although it will be helpful if you have worked with a spreadsheet before and are moderately competent with basic Python. We will work with a diverse selection of data sets and perform a variety of analyses. Along the way we’ll build and submit an entry to a <a href="https://www.kaggle.com/">Kaggle</a> competition. By the end of the day you will be functionally competent to venture forth on your own Data Science projects. markdown
Comment:	production notes