Bayes camp

Tailored training
One-to-one coaching
Impartial consultancy

Photo of teaching in progress


Training

We provide tailored on-site courses for teams working with data, and affordable workshops open to all. Our specialities are: introducing data science, stats, machine learning and AI for managers; Bayesian modelling and software; data visualisation; statistics for health research and clinical audit. email robert@bayescamp.com to discuss your team's goals.


Coaching

Take time out from work pressures and look at the big picture of your career working with data. We provide one-to-one, confidential sessions with a trained co-active coach who can ask the right questions, so you can define your values, priorities and goals. email robert@bayescamp.com to discuss your personal journey.


Consultancy

Bring in some confidential expertise in statistics to review your work or help develop new analyses and outputs. We can help with statistical and machine learning analyses, making data processing faster and more reliable, Bayesian modelling and prediction, health research and clinical audit, meta-analysis, data visualisation and communicating complex results, and recruitment or management for data science teams. email robert@bayescamp.com to start a conversation about how your organisation could develop.




Robert's BayesCamp story

Case studies


I started BayesCamp in 2017 to provide training and coaching for people who analyse data. We aim to do what the big providers neglect:

  • supporting managers, recruiters and others who work with data analysis teams, who want to know more, but don't need to be experts themselves
  • small-group, flexible, tailored learning
  • one-to-one coaching
  • primers in more advanced topics like Bayesian modelling, effective data visualisation, and social network analysis
  • all trainers have been there and done it

But there's something we don't do: we don't give you any hype. When some software or algorithm doesn't meet expectations, we'll tell you so.

I've worked on hospital quality and safety data, evidence synthesis, a wide range of health, social care and education projects, teaching research methods and data analysis to people from all over the world, and consulting for clients in private, public and charitable sectors.

Have you ever sat in a training course where everybody was kept busy with post-it notes and word association, but nobody had learnt anything by the end of the day? I certainly have. I realised that employees deserve learning experiences as good as any university course.

There's no reason why it should be dumbed down just because it lasts one or two days. And the trainer should be someone who has worked on serious projects, not just pressing buttons but also communicating results and thinking about the interpersonal skills that are needed.


The Economist's EAGLE golf system

The Economist's sports blog had teamed up with Dell to take a collection of R programs that ran multiple projections of how golf tournaments would play out, and make them into a system that would update with live data, and feed the predictions into a website.

Data Editor, Dan Rosenheck, had spent three years developing the core of the system up to this point, and one of his goals was to push up the predictive ability of the system by giving it more flexibility. The code that took data from each player, each course, and each hole, and detected how player talent was evolving over time, could only generate future projections from certain formulas.

Dan knew that if he gave the system more freedom, it could pick up anomalies and give more accurate predictions. But the expert advice he got was that it just couldn't be done -- unless he could switch the core of the prediction code to Stan, Bayesian inference software with a reputation for being difficult to learn and use. And meanwhile, the start of the 2019 majors season was getting ever closer!

That's when he got in touch with BayesCamp. We were able to sit down with him and take the time to understand his code, the constraints he was working in, and the output format that the front-end developers would need. We wrote some new functions within Stan for the weird distribution that golf scores have, and rebuilt the core of his probabilistic model to run in Stan's Hamiltonian Monte Carlo algorithm.

The new system updates the model with Stan and uses parallel computing in the cloud to get that done in the timescales required. We got it up and running just in time for the launch... and just in time for Tiger Woods' freakish comeback to catch out every prediction out there. Well, the most likely prediction isn't going to happen every time -- that's probability.

You can check out Dan's system, EAGLE, while golf majors are in progress at eagle.economist.com



Company principles

  • We focus on a few areas of specialist strength, we don't trick clients with hype.
  • We contribute to local tech and science communities, and prioritise face-to-face, local work.
  • Our work must promote healthy, happy, sustainable workplaces and professional practices for people working with data, whether they call it statistics, data science, machine learning or artificial intelligence. We help people to confront and change stressful, unrealistic, uninformed expectations.
  • We will work with any individuals or organisations based anywhere in the world (unless prevented by UK law, and except any foreign government); dialogue builds a better world. No BayesCamp activity ever physically takes place in jurisdictions that endorse torture or extrajudicial killings, or that legislate discrimination.
  • bayescamp.com does not contain tracking or snooping code of any form.
  • We never use exploitative subcontracting practices for individuals, such as zero-hour contracts. If we're happy to have someone supply BayesCamp training or coaching, we support them with employment and the legal protection that brings them.
  • Prizes and awards for lifetime achievement are inimical to nurturing new ideas, collaboration, and early-career diversity. BayesCamp and its directors never accept any such prize, and give awards rewarding projects, not individuals.

Research

BayesCamp supports and conducts research in Bayesian methodology. Read more about our work here.

BayesCamp Ltd is incorporated in England and Wales, number 10666858.
VAT registration: 317349595
The term 'BayesCamp' and the Gaussian tent logo are registered trademarks.