Every story needs a good plot
One could think of data science as “art, grounded in facts”. It tells a story through visualisation. Both story and visualisation rely on a good plot. And an abundance of those has evolved over time. Many have their own dedicated Wikipedia page!
Which generate the most interest? How is the interest in each trending over time? Try this app to find out. Continue reading “The plots thicken”
Responding to a weak property market
In December I looked at how recent events have shaped the property market in London SW10. If short-distance moves are off the table in the current climate, how are property owners responding? When sales are weak, are planning applications in the ascendency? I applied data science techniques to Royal Borough of Kensington and Chelsea (RBKC) planning data to find out.
Continue reading “SW10 digs deep”
Why take a deeper look at G-Cloud categories?
The last blog – “The key to unlocking services on G-Cloud” – touched briefly upon their overlap. And as the concept of G-Cloud categories was newly introduced in the current iteration (G9), it may be worth taking a deeper look at their impact in advance of the next.
So, in this blog, I want to explore the extent and effects of category overlap. And let’s see what insights may be drawn. For example, are some categories of less value than others? Could some suppliers gain an advantage? Perhaps by aligning each service to many categories so buyers find them irrespective of their carefully crafted search criteria?
Continue reading “Do G-Cloud categories need a tweak?”