I am a freelance chartered statistician specialising in data visualisation and Bayesian modelling. I provide training courses, coaching and consultancy. I've helped analysts from stats and machine learning backgrounds, visualisation/design people, economists, health and social care professionals, academics, civil servants, journalists and more. I'm also interested in helping managers responsible for data science teams (recruiting, managing and quality-assuring) who don't come from that background themselves.
I started my company, BayesCamp, in 2017, to provide training, one-to-one coaching and consultancy. Prior to that, I analysed clinical audit and performance indicators, evidence synthesis, health services research, project management, clincial trials, observational studies, and communication of complex findings.
Most teaching statistics and machine learning is not as effective as it could be. I believe that we could do a lot better by helping learners to look at the concepts of models, philosophy of science, inter-personal and communication skills. I've drawn inspiration from the reform movement in stats education that Joan Garfield and George Cobb started, Mike Monteiro's code of ethics, Jenny Odell's doing nothing, and a collection of science thinkers like Charles Manski and Peter Lipton.
In particular, I think that data analysis must serve the audience, typically the public or decision-makers, with statistical rituals coming far down the list of priorities. That probably makes me an outsider teacher (analogous to an outsider artist), in that I do what I think is right, not what's popular.
You can get in touch via the links on the bar above, although I especially like real-life letters and postcards: BayesCamp, 80 High Street (Unit 83), Winchester, SO23 9AT, UK.
I don't use social media.
Advanced Statistical Analysis, online, 23 September 2021
If you work in analytics in the public or charitable sector, and want to explore the more advanced techniques from statistics and machine learning, this one day introduction and overview will help. We will talk about the theory that underpins building a good analysis and critiquing it. We will also put this into practice in simple workshops using the open-source langauge R as an example, and talk about the managerial and organisational considerations.
An introduction to model building techniques in Stata, online, 13 October 2021
Ideal for anyone who uses Stata and wants to advance their skills in modelling data (supervised learning).
Bayesian Meta-analysis, online, 14-15 October 2021
This course is for everyone who has grown tired of shoehorning their complex evidence base into a one-size-fits-all meta-analysis. Instead, you can construct a bespoke model that captures non-linearity, non-normality, biases, missing statistics and more. We assume you have encountered the basics of Bayes, perhaps having previously tried out WinBUGS / JAGS / brms. We will introduce and use Stan, but the ideas you acquire can be applied to any Bayesian software.
Understanding Statistical Analysis, 4 November 2021
Maths Skills for the [public sector] Workplace, 23 November 2021
Data Analytics, 25 November 2021
Introduction to Bayesian Analysis using Stan, online, 5-6 July 2022
Aimed at beginners in Bayes, this course will get you up and running in using Stan for flexible, fast modelling. You can use either R or Python, running on your own computer or on our server via your internet browser (Jupyter notebooks).
If there are statistical topics that would interest you and your teams, send me a message at email@example.com and let's see if we can organise a training course that would cover it.