Louisa’s background has interspersed spells of study and work. Having come out of a design oriented university, her first degree in English focused on how people understand information and stories, which she then used working in change management for a FMCG in the UK and in Denmark. Following that she returned to study, but this time focusing on analytic programming using R and Python and decision making psychology, which exposed her to the burgeoning field of data science and visualisation.
After her MSc, Louisa worked as a business analyst with the technology used for London’s infrastructure, on varied projects such as fare forecasting, procurement dashboards and text categorisation modelling. These projects were interesting but after attending a Tableau event run by Data + Women at The Data School, she found a way to combine her interests of story telling and data analysis together.
Away from work, Louisa likes heading to the gym, science fiction and travelling.
At the end of our training period in the Data School we are encouraged to reflect on our experience, something which often we don’t find ourselves having too much time to do during the DS itself (though not from want of people getting you to do this).
In the last post (which was before dashboard week, so a rather distant last) we discussed how to navigate the wodge of documentation Google have around their Place API. Now, let’s find some geometry!
Last day of dashboard week! Continuing on with the space theme established earlier in the week for me, today’s dataset was a subset of NASA’s meteorite fall data.There were no preparation in Alteryx needed for the core requirements (as Tableau does pretty well with simple JSON file) though some people did go down that route. First however, a gif which I took out of my original dashboard, which because it’s Friday I’ve taken the liberty of putting in a very questionable font and colours.
Today we had a slightly shorter amount of time to complete out dashboards due to a really interesting visit from James Eiloart, Tableau SVP for EMEA. He spoke to us about the history of Tableau and where he sees the software platform in the current marketplace, which we were really pleased to come to terms with again in 2018.3, but first had to overcome an Alteryx wall of building a whole analytic app in 4 hours.
Today’s task was extracting Traffic Data for areas of the UK and chucking it throw a ye olde analysis in Tableau 8 (by Friday we’re hoping to be on pasta shapes, glitter and crayon). I had the region of Yorkshire and the Humber, and decided to do an analysis of the % change in traffic volumes over the life of the dataset (since 2000), with a subsection on cycling.
Today our task was to extract data from NOAA’s daily summaries interface and generate a dashboard of climate data. Lately I’ve been a lot about spaceflight so I decided to keep it simple with a dashboard comparing climate data from the two main launch sites in the US, which are Cape Canaveral in Florida and Vandenberg Air Force Base in California.
Our hill start today was a text response API known as FOAAS, which generates profane responses based on parameters in your input query. After some deliberation and some help from newly minted Alteryx Ace Ben, I decided to make a network diagram based on the common relationships between words in the response calls.
Often information with a geographic context (such as a list of locations that a company operates in) doesn’t come with any kind of geometry to give this context a spatial dimension – no address, no postcode, and certainly no latitude and longitude. But Google Maps’ Places API allows us to automate a set of searches based on how we might search for these places as a human, leaving their engines to do the hard work. In this blog post we’ll get started with using the Google Cloud Platform.
Much maligned (feared?) core programming technique, one of Regex’s easiest use cases involves creating dynamic API calls which can respond to page limits.
From its foundations in R, Alteryx can support quite advanced predictive modelling from a variety of data sources. This week, we learned how to create three kinds of predictive models in Alteryx (Regression, Classifiers and Forecasts).
Last week was learning more about visual analytics – the nuts and bolts of how humans understand information and using that knowledge to create effective visualisations.
Our first presentation from the first week of the Data School was to review and update one of our presentation visualisations, enriching the data set by joining it with a new one.
Our first introduction to Alteryx was processing data from BoardGameGeek, to answer the following question – does a game’s release year affect reviews and ratings of products?