These are some of our most popular topics. We can tailor them to your team's needs – contact email@example.com to discuss what you would like to achieve.
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.
24 January 2020
From Statistics To Machine Learning
Now sold out. Contact firstname.lastname@example.org to discuss running this as an in-house training event for your team.
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:
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 email@example.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.
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.
15-16 October 2020
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.
More 2020 courses to be confirmed soon:
Talks coming up:
You can also contact Robert at firstname.lastname@example.org to discuss bespoke training for your team.
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