Tailored training and coaching
Experienced trainers
Impartial advice

On-site workshops

These are some of our most popular topics. We can tailor them to your team's needs – contact robert@bayescamp.com to discuss what you would like to achieve.

Data visualisation

This workshop for teams that work with data will introduce principles of good practice in both the analytical and design sides of dataviz. Throughout, small group exercises build confidence in sketching, innovating user-testing and communicating what the data show us. This can be a one-day or half-day workshop and works with anything from 5 to 25 people. The facilitator, Robert Grant, is the author of "Data Visualization: charts, maps and interactive graphics" on CRC Press.

Data science, statistics, machine learning and AI: a primer for managers

This one-day workshops aims to support managers responsible for data analysis teams or outsourcing. We consider the four terms in the title, what different backgrounds go to make up a successful 21st century data analysis team, and how those backgrounds create different norms and motivations. Understanding this is essential to recruiting and retaining. We also explore the strengths and weaknesses of different analytical methods, and contemporary concerns around anonymity and ethics.

Bayesian data analysis

A one-day workshop for data analysts will introduce the theory of Bayesian analysis, explore the kind of critical thinking necessary to do it well, and get your team started with software. After this course, your team will be able to design complex Bayesian models and fit them to your data. You'll be poised to teach yourself more advanced Bayesian algorithms. We can tailor the content to the kind of data and analysis they typically encounter. If relevant, we can also introduce methods such as particle filters, continuous-time samplers, ABC or Bayesian non-parametrics. The software covered could be Stan, R, BUGS/JAGS, Stata or Python PyMC3.

Future workshops

Introduction to Stan, half-day tutorial at BayesComp2020, 7 January 2020

7 January 2020
Introduction to Stan

Robert Grant will be teaching a condensed version of his popular Stan introductory classes from 10:30-1:30 on the day before the full conference begins. Tutorials are free for conference delegates, and the other software topics on the day are SAS, NIMBLE and AutoStat.

Pre-requisites: Participants should know the basics of model fitting by MCMC simulation. There is no need for experience of Hamiltonian Monte Carlo or Stan but we will assume understanding of Bayesian analysis, model comparison and diagnosing MCMC problems such as non-convergence. Please bring a laptop with one of the Stan interfaces installed -- it doesn't matter which one as we will focus on the Stan code which is common to all.

Learning outcomes:

  1. Know how to get started with Stan via the various interfaces, including the common functionality of checking your model code for errors, translating it to C++, compiling it, sampling from the posterior, summarising the output and exporting chains.
  2. Understand the basics of coding regression models up to multilevel models.
  3. Be aware of tricks for more efficient parameterisation
  4. Know how to obtain statistical and graphical diagnostic outputs, recognise problems and set about debugging.
  5. Know how to add a new distribution as a Stan function, expose it to R/Python/Julia for debugging, and use it in the log-likelihood and posterior predictive checks.

From Statistics To Machine Learning, 24 January 2020

24 January 2020
From Statistics To Machine Learning

This one-day workshop is aimed at anyone who studied some statistics in the past, and wants to understand the principles of machine learning. There are a number of techniques and ways of thinking that can be useful in any form of data analysis.

We will combine discussions about theory and working practices with thought-provoking small-group exercises. You will learn about:

  • Terminology and jargon
  • Supervised and unsupervised learning
  • Ensembles, bagging and boosting
  • Neural networks, image data and adversarial thinking
  • AI and ethical concerns
  • Reinforcement and imitation learning
  • Big data's challenges, opportunities and hype
  • Speed and memory efficiency
  • Concepts of model building such as cross-validation and feature engineering
  • Options for software, outsourcing and software-as-a-service
  • Data science workplaces combining statistical expertise with machine learning: what makes them happy and healthy

You can bring a laptop to try out some of the examples in R, but this is not essential. Refreshments and lunch will be provided.

Students can get a discounted ticket for GBP 90 incl VAT -- email robert@bayescamp.com for yours!

Robert Grant is a medical statistician by training, more recently involved in machine learning techniques, who runs his own training and coaching company, BayesCamp. His specialities are Bayesian modelling (he is one of the contributing developers of Stan) and data visualisation (his book "Data Visualization: charts, maps and interactive graphics" is on CRC Press). He is currently test-driving around 20 different commercial machine learning software packages with the aim of publishing reviews and comparisons. He has many years' experience of teaching introductory courses and is committed to making advanced data analysis accessible to everyone who's interested.

Book here

Applying Artificial Intelligence in the Public Sector, with Understanding ModernGov, 27 Feb 2020

27 February 2020
Applying Artificial Intelligence in the Public Sector

The influence of technology in making better decisions, improving services and delivering better outcomes for the public sector is undeniable. One technology driving this transformation is AI.
By understanding how to effectively use artificial intelligence (AI), you will be able to make greater sense of your data, improve how you analyse large volumes of information, and enhance your decision making.
Attend this course to learn how AI currently operates in the public sector and determine what skills and resources are needed to successfully apply it; gain an overview on a range of AI tools, from machine learning to algorithms, and work through scenarios to explore how you can develop and implement a range of AI systems.
This Applying Artificial Intelligence in the Public Sector training course has been specifically designed to introduce public sector organisations to the theory and practical application of AI.
Through a mixture of trainer led discussions and use of laptops you will leave the day with a greater understanding of AI and how this can be practically used within your organisation.

Book here

Introduction to Bayesian Analysis Using Stan: Royal Statistical Society, 7-8 July 2020

7-8 July 2020
Introduction to Bayesian Analysis Using Stan

This two-day course is ideal for beginners or intermediate users of Bayesian modelling, who want to learn how to use Stan software within R (the material we cover can easily be applied to other Stan interfaces, such as Python or Julia). We will learn about constructing a Bayesian model in a flexible and transparent way, and the benefits of using a probabilistic programming language for this. The language in question, Stan, provides the fastest and most stable algorithms available today for fitting your model to your data. Participants will get lots of hands-on practice with real-life data, and lots of discussion time. We will also look at ways of validating, critiquing and improving your models.

Book here

Bayesian Meta-Analysis: Royal Statistical Society, 15-16 October 2020

15-16 October 2020
Bayesian Meta-Analysis

This course introduces the Bayesian approach to meta-analysis. Attendees will learn practical ways in which they can combine multiple sources of published evidence while accounting for uncertainties such as response bias, publication bias, confounding, and missing information, using either BUGS, JAGS or Stan as software. With Bayesian models, this can be transparent and reproducible.
This two-day course begins by reviewing classic meta-analysis methods and expressing them as statistical models. Once attendees understand meta-analysis in this larger context, they are able to extend the model flexibly to account for common problems such as papers that report only change from baseline. A series of problems will be tackled in this course, and attendees will leave with model code that they can immediately start using with their own projects.
Facilitator Robert Grant has worked on meta-analysis and Bayesian models for many years, having been part of the NICE guideline development technical staff from 2000-2006. He is responsible for innovating several of the techniques we will cover today.

Book here

More 2020 courses to be confirmed soon:

You can also contact Robert at robert@bayescamp.com to discuss bespoke training for your team.

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