Navigating the magical data visualisation forest

Speaker: Dr Margriet Groenendijk from IDM

Margriet is a tech enthusiast at IBM and gave a lightning talk at the Django Bristol Bath meetup as well.

Slides are online here: https://www.slideshare.net/PoleSystematicParisRegion/a-beginners-guide-to-weather-climate-data-margriet-groenendijk

This talk is about using Jupyter notebooks, https://jupyter.org/, for data analysis and visualisations.

NB: These can be run on the desktop and is available in the cloud as well.

Libraries Margriet has used:

PixieDust package, https://github.com/pixiedust/pixiedust is an addon for Jupyter notebooks https://www.ibm.com/cloud/pixiedust

– is an open source package developed by David Taieb and Margriet Groenendijk.  It is a a wrapper around various libraries, which turns into GUI options in the Jupyter notebook.  This is an amazing useful tool for data scientist and others that would like to explore their data without learning as much code and the various differences between each library.

It can load pandas and spark data frames.  It can also load data via URLs, very helpful for cloud based notebooks.

PixieDust provides a serious amount of options and less code for busy people or those exploring data or data science newbies.

seabourne has a nice map based visualisation.

PixieDust integrates with google, mapbox, and seaborne

PixieApps – https://dataplatform.cloud.ibm.com/docs/content/pixiedust/pixieapps.html

The technical lead for PixieDust, David Taieb https://twitter.com/dtaieb55, has also published this book: Thoughtful Data Science – https://www.safaribooksonline.com/library/view/thoughtful-data-science/9781788839969/ 

 

Categorizing Tweets Using Machine Learning – Halide Bey

Code in this talk can be found here https://github.com/halidebey/PyCon2018

Speaker made use of this tool, Kaggle – https://www.kaggle.com/ – the place to do data science projects.

Speaker refers to this source of data

https://www.figure-eight.com/data-for-everyone/

Discussed the approaches to machine learning

Need some information about statistics and algorithms, a such as LogisticRegression.