Jamie’s passion for sport and data analysis resulted in him spending the last 5 years as a sports trader, for bookmakers as well as proprietary data firms. After spending the last few years working with excel vba to create in play algorithms he is now looking forward to working with tableau and alteryx to broaden his data analytical skills.
Outside of work, Jamie enjoys travel, having recently spending a year travelling and working remotely in Central and South America. Also he is a keen surfer as well as being a regular attender of music gigs.
So DS6 wrapped up last weekend and here are some tips, based on my experiences, for anyone about to start or interested in joining the information labs data school.
As a follow up to my random forest model blog, the next logical step is to explain the theory behind Alteryx’s gradient boosted model (GBM). Both models typically use an ensemble of decision trees to create a strong predictor. However, they differ in how they act at each stage of the ensemble.
For day 3 of dashboard week, Andy gave us the challenge of creating a transport dashboard in relation to a City. As we were not allowed to viz on New York or London, the initial challenge was to find a City that had data granular enough to build an insightful viz.
This morning Andy greeted us with alcoholic data and the choice was between analysing wine or beer datasets. I chose the latter which turned out to be very challenging due to the lack of detailed statistical datasets.
I spent the morning searching online datasets and API’s to try and find a dataset suitable for tableau. Eventually, I came across Beeradvocate.com data on data.world that provided 1,586,614 reviews of around 45,000 beers.
Today was our first day of dashboard week and we had to create dashboards with NBA data. I drew lot number 4 and ended up with the task of creating a dashboard the illustrated NBA franchise history.
One of the major strengths of Alteryx is the ease with which it allows the user to run powerful predictive models. It can do this by merely attaching specified data to a predictive tool.
This blog will give a brief overview of a forest model, which is an extension of the classic decision tree model. Decision tree models transform each independent (predictor) variable into a classification question.
This blog documents the creation of a dynamic tooltip for my Donald Trump calendar vis. A trick that is great in preventing tooltips appearing for null values.
This blog will document the first part of creating an interactive calendar visualisation – the initial scraping and cleaning of the data in Alteryx.
Today was our second day of Alteryx training and one tool that stood out was the multi-row formula tool. It can perform complex clean-up tasks with a few simple actions.
Today was our introduction to Alteryx and we ended the day by editing two CSV files that were unreadable with Tableau. By using a similar data set, I will run through a couple of functions that can be used to make such data readable by Tableau.
Week 3 of DS6 began with an overview of visual analytics best practices. This blog will explain and provide examples of principles I found particularly useful.
The visualisation I chose to edit was my first ever tableau publication, a breakdown of the statistics behind Leicester’s title win.
Having worked as a sports trader for the past 5 years my overall aim of data school is to build on my data analysis skills.