The art of using data is ensuring that it moves people to action; these stories are no less fragile.
One of the clearer roots of the problem is the wretched state of American policing, which has been in dire need of reform for longer than I’ve been alive; a rallying call of the 2020 movement has been “Defund the Police”. I’ve not looked into police spending to know how much is too much, so I decided to do a comparative analysis of some American and international cities to see what their police budgets looked like.
Webscraping can vary between wildly hard (purposefully or otherwise) and being the notch below an open API to extract data. However, there are often some hints you can use to check whether or not a particular site will be one of the easy or hard ones.
I actually don’t spend that much time working with Tableau, so in the last year I’m a little out of practice. I went back to my training to restart my practice; do something you’re passionate about, and draw it out first.
On Wednesday my esteemed colleague @liluns and I went to the AWS Summit in London, which is several thousand people crammed into one end of the Excel Centre in the Docklands.
There’s a lot of data where one observation to a human (e.g. one survey) isn’t ideally one observation to the query language of a database system.
Sneaking in just before April does, I’ve decided to come out swinging with an frankly uncontroversial idea which may still split my colleagues, but only one this month.
This week we’re going to be listening and reading, rather than completely reading! We’re feeling pretty seasonal with an item about, uh “interpersonal” topics, in time for February, and also for mental health, as February hosts the annual Time to Talk day.
This year I’m going to recommend two books every month which have help shape my adventures in data – we’re visiting probability and feedback in January.
From just getting it out the door to ensuring the doors are all custom, let’s take a look at the different ways to generate them.
You’re not really meant to say this when you are (or have been) a data analyst/scientist/whatever, but I have a limited patience/tolerance for the reformatting and cleaning of data.
