Tailored training and coaching
Rasmus (@rabaath) is a Data Scientist who has worked both in academia (Lund University) and industry (castle.io, King, DataCamp). He has a PhD in cognitive science. Currently he's working on using data and Bayes for cybersecurity at Castle.io. He's passionate about Bayesian statistics, good graphs and free coffee, and blogs at Publishable Stuff.
Robert (@robertstats) started BayesCamp after teaching and data analysis in health and social care statistics (St George's University of London, Kingston University, National Clinical Guidelines Centre, National Institute for Health & Care Excellence, Royal College of Physicians). He's a medical statistician who went on to learn machine learning techniques and see how data science actually happens in commercial settings. He has trained and consulted for many organisations in the public and private sectors. Robert loves Bayesian analysis and data visualisation: he wrote the ASA / CRC Press book "Data Visualization: charts maps and interactive graphics" in 2018, and is one of the Stan developers.
Leto (@PiratePeel) has over 10 years of experience in theoretical and applied machine learning research in both academia and industry. His research interest is in analysis of complex networks. He has worked on many research projects in biology, computer vision, crime science, economics, game theory, geography, geomatics, physics and security. His collaborations in industry include Airbus, BAE Systems and London Metropolitan Police, and in academia include Imperial College London, MIT, Santa Fe Institute and Oxford University.
The world-leading Bayesian software comes with interfaces for R, Python, Julia and more. It is tried and trusted for almost every kind of model you might want to fit to your data. It is used by every serious data science organisation you can name. But maybe you find it a little scary, a little unapproachable. Let's break through that barrier with an introductory or specialist course.
There are many ways to access powerful Bayesian tools in R, from user-friendly, high-level packages like brms and rethinking through to the NUTS and bolts of Stan, JAGS and BUGS. An overview course will show you the options and let you weigh up which one works best for you and your data.
BUGS / JAGS
Bayesian inference Using the Gibbs Sampler is still the world's favourite Bayesian software, and Just Another Gibbs Sampler is close behind. An introductory course will get you up and running with these tools and show you many of the models that have been effectively programmed in them already.
BayesCamp supports and conducts research in Bayesian methodology. Read more about our work here.
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