The Department of Geography’s Spatial Data Science ‘pathway’ offers undergraduates outside of Computer Science a unique opportunity to complete a suite of modules focussed on the fundamentals of data science in Python. We believe that our spatial data science modules not only offer students a valuable set of tools for undertaking research at undergraduate and graduate levels, but that they also offer you a competitive advantage in today’s job market by helping you to stand out from the crowd.
Read on to learn about how the pathway works and what it offers!
What is Spatial Data Science?
According to Hal Varian, Google Chief Economist and UC Berkeley Professor, data science is:
“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”Hal Varian [ref]
So if data science is about using code and algorithms to extract, process, visualise, analyse, and communicate insights derived from large volumes of data, then spatial data science is — on one level — simply about the practice of using spatial data in this process.
And when you start to look, it turns out that spatial data is everywhere because so much of ‘big data’ — everything from credit card purchases to Oyster card taps and Airbnb property listings — happens somewhere. Spatial data science can be used in retail and transportation planning, in risk management (especially reinsurance), in churn prediction and targeted marketing, in real estate (e.g. PropTech) and location-based services… the list goes on!
All of this means that the people with the skills to handle spatial data are in demand. But don’t take our word for it, here’s what Carto has to say on the matter:
However, the use of locational data presents special challenges for the data scientist: not only is spatial data stored differently from ‘regular’ data, but the presence of geography in a data set can also undermine some of the basic assumptions upon which statistical approaches depend. The short version of this is that observations are not independent: the fact that one wealthy person lives in an area makes it more likely that other wealthy people live nearby because households do not choose where to live at random. You might have heard of it as ‘birds of a feather flock together’ but it in practical analytical terms it means that patterns that look significant to a naive analyst may not actually mean all that much.
What is the SDS ‘pathway’?
We’ve designed our three modules to provide the critical skills that we think you need to get started down the path to being either a practicing (spatial) data scientist or a valuable bridge between ‘the quants’ and the rest of an organisation, whether it’s a NGO, or a private or public sector body. To deliver this we cover a lot of ground and we will expect you to work hard in order to understand how to take what you’re learning about in programming and statistics, and to apply it to interesting problems. You should think of this as an intensive language class — and learning to code is like learning a new language — in which regular practice in a ‘lab’ is essential.
This is an investment that pays off; here’s what one recent graduate had to say about their experience with the ‘Geocomputation’ pathway that we’ve now adapted into ‘Spatial Data Science’:
The Geocomputation skill-set is without a doubt the most important learning I have taken away from King’s. The course was taught at a perfect pace. It was super challenging but there was plenty of support along the way. The Jupyter notebooks were fantastic and I regularly find myself referring back to them in my own work to find solutions.Geography Graduate (2019).
I think, what has surprised me the most, is how much employers and recruiters in general value the skills. I’ve been pretty much employed in ‘data science’ since I handing in my dissertation. As things currently stand I am receiving offers left right and centre, all of which give me way more impact and pay much more than the typical city style graduate roles I’d previously been considering. I am not sure we sell the Geocomputation employment benefits enough. I also feel way more ‘future-proofed’ than my peers.
The pathway is composed of a suite of taught and online/self-directed modules:
- Code Camp (an online ‘boot camp’)
- Foundations of Spatial Data Science
- Principles of Spatial Data Science
- Applications of Spatial Data Science
- Directed Readings (currently for Geography students only)
The modules are designed to build, one on top of the other, so that it is possible to complete the pathway despite having little or no prior experience of computer programming.
Read on to learn about our suite of modules! See page links below.