Author: Tessa (the external)

Assume the worst – Alex Chan

Posted on by Tessa (the external)

If your system allows a user interaction, say a chat function, ensure you can block people.  Gave an example about a payment platform which was abused to harass an ex.

How to build services to reduce being open to abuse online.

# Diversify the team

Interestingly you cannot change your name in the history of git say.

# Real name policies

This does ensure people will be civil, truthful, is helpful for protecting vulnerable groups

# Robust privacy controls

Have the ability to ban/block malicious users (individual/platform)

Consider shadow blocking or muting / so the blocked user cannot se they have been blocked per say.  As blocking can trigger retaliation.

This could be changeable in the past.

# Human moderation is best

Think about your moderators – they need access to counselling etc before burning out.

# Design with abusive personas in mind

PyCon UK 2018

Posted on by Tessa (the external)

I am lucky enough to be back at PyCon UK again this year, https://2018.pyconuk.org/.  Lots of talks, workshops and there are sprints to follow.  One of the most inclusive and friendly technical conferences I have ever attended, I am glad to be back again this year.  I will be adding write ups for the sessions I attend here: https://asset.blogs.bris.ac.uk/tag/pyconuk2018/

Navigating the magical data visualisation forest

Posted on by Tessa (the external)

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

Posted on by Tessa (the external)

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.

Is it Shakespeare?

Posted on by Tessa (the external)

Using Python for authorship attribution in Renaissance drama

A lecturer, Paul Brown, and a student, Katie Jones, present their exploration of analysing old plays in an automated fashion.

The period of interest there are at least a 1/3 of which the authorship is unknown.

A source of early plays online is the Early Book Online tool, https://eebo.chadwyck.com/home

Not common to use their approach to examine the full canon of work.  They turned to Python to look at the treatment of whore and prostitutes during the time period.