This is a short piece on a project I’m developing at NHS England, using Python to analyse patients’ inpatient care and determining where a Clinical Commission Group’s (CCG) patients are being treated significantly differently to those patients in its peer group CCGs (similar 10 CCGs as determined by NHS Rightcare Methodologies).
Upon the enlightening release of the Cambridge Analytica exposé that revealed the company’s underhanded tactics it utilises to influence and manipulate people with a combination of data analytics, espionage, and ‘honeypotting’, I think it’s incredibly important to discuss how the analytical community needs to ensure a commitment to honest and unbiased analytics, and ethically sourced data.
I’m going on holiday soon! I’m headed to Florida with my parents and being the cost savvy Brits that we are we’re always on the hunt for a bargain. We wanted to go do some of the Theme Parks (we settled on Disney and/or Universal Studios) and went about finding the cheapest ticket rates we could. During the internet hunt my Mum kept using the Calculator on her PC to do things like exchange rate conversions and to calculate price differences – but, being the Excel monkey that I am I proposed a much better solution: let’s whack it all in Excel and let me make some pretty charts!
In this final Splatfest Analysis, we will be taking a closer look at the distributions of the 9 games I had with the Splattershot to determine whether its consistent place in the top 3 was due to me consistently playing well with the weapon, or whether the averages were skewed by particularly high leverage outliers.
After watching a long session of ‘VSauce’ videos (great brain food videos, albeit very addictive!), I came across this video discussing ‘Zipf’s Law’. Zipf’s law states that in any corpus of natural language, the frequency of any word is inversely proportional to its rank in the frequency table. This Zipfian distribution applies to many different types of data studied across a variety of fields (the video discusses a large variety of these instances). Zipfian distributions also follow the ‘Pareto Principle’, the 80-20 rule. 80% of the words used in any corpus are only 20% of the unique words used.
After being utterly captivated by this phenomenon, I decided it would be fun to check whether my blog, being a corpus of natural language, followed Zipf’s law.
Up next in this multi-part analysis is a look at my performance with each weapon I used during the Splatfest. I’ll take a closer look at my Win to Loss ratio by each weapon and how well I did overall with them regardless of outcome of the game.
Splatoon 2 came out on the Nintendo Switch a couple weeks back, and they’ve had their first ‘Splatfest’ since the game was released. ‘Splatfests’ are worldwide events that pit two similar things against each other in an opinion poll (for this Splatfest it was Mayo vs Ketchup). After voting for your favourite, you’ll then be playing the game mode ‘Turf War’ against members of the opposite team, to determine which side is the best at Splatoon. I picked Ketchup, and I battled valiantly to prove that Mayo is bad and everyone who likes it deserves to lose at Splatoon.
During this Splatfest, I used Nintendo’s phone app to collect the data of my Splatfest matches in order to analyse my performance over the 24-hour event. I’ll be using this data for a multi-part analysis, covering a variety of sections like which weapons was I best with, what stages I was better at and more!
Spurred on by conversations with my brother and various Reddit posts making claims about immigrant populations in the UK, I decided to check the ONS website to find some of their recent analyses on the UK population (which are fantastic, by the way)1.
