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Jon Reades[^index-1] <a href="https://twitter.com/jreades"><img src="img/Twitter social icons - circle - blue.png" height="17" /></a> <a href="https://www.github.com/jreades"><img src="img/GitHub-Mark-120px-plus.png" height="17" /></a>

[^index-1]: The Bartlett Centre for Advanced Spatial Analysis

<a href="https://www.ucl.ac.uk/bartlett/casa/"><img src="img/casa_logo.jpg" alt="CASA0013 course" width="120" align="right" style="margin: 0 1em 0 1em"/></a>

The *Foundations of Spatial Data Science* (FSDS) module is an optional element of CASA's [MSc programmes](https://www.ucl.ac.uk/bartlett/casa/programmes) and is intended provide an introduction to **doing data science in Python** for students who are new to programming or whose previous exposure to coding is fairly limited. The module seeks to enable students to access, understand, and communicate data in a spatial context. FSDS is *not* about pushing buttons, but about using logic, programming, and your growing analytical skills to tackle real-world problems in a creative, reproducible, and open manner.

**FSDS is _not_ easy**: in order to make the most of the module---and the foundation that it provides both for Term 2 modules on the MSc _and_ for post-Masters employment---you will need to work hard. This does not mean cramming before each practical, it means **practicing** between practicals, **doing the readings**, and **really watching the videos**. UCL expectations for a Masters-level module is 150 hours of study time: there are 4 hours of timetabled activity per week, and about an hour of videos to watch before each workshop, leaving up to 100 hours of 'self-study'. By implication, you should expect to spend about **1.25 hours/day** studying for this module: reading, coding, and (above all) practicing.

In exchange for your hard work, there is a pressing need for analysts, planners, and geographers able to *think* computationally using programming, analytics, and data manipulation skills that are anchored in the needs of policy-makers, businesses, and non-profits. There is a severe skills shortage in this domain across all sectors and, consequently, significant opportunity for those who can 'make sense' of data+code.

## Acknowledgements

While this module in indebted to both feedback from students and colleagues over the years, several people played a particularly outsize role in my thinking and deserve special acknowledgement:

1. [Dani](http://darribas.org) for help with Docker, geopandas, and any number of other new tools with which I've had to familiarise myself.
2. [Andy](https://www.ucl.ac.uk/bartlett/casa/people/dr-andrew-maclachlan) for somehow knowing about all kinds of new web apps that I could use to support the module.
3. The [Geocomp team](https://kingsgeocomputation.org) at [King's College London](https://www.kcl.ac.uk/geography), who supported my hare-brained scheme to teach Geography undergrads to code and offered all manner of useful feedback on what we could/could not feasibly cover.