Small-group training and one-to-one coaching for data visualisation and Bayesian analysis.
28 October 2019
From Statistics To Machine Learning
(early 2020 date to be confirmed: email firstname.lastname@example.org to reserve a seat)
This one-day workshop on 28 October 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.
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.
25 November 2019
Get Up and Running in Bayesian Data Analysis with Rasmus Bååth
Bayesian data analysis is a powerful tool for inferential statistics and prediction, and this one-day course will get you up to speed with doing Bayesian data analysis using R or python. The goal of the course is for you to get an understanding of what Bayesian data analysis is and why it is useful. After the tutorial you should be able to run Bayesian analyses in R or python and will be setup to learn about more advanced Bayesian techniques. The target audience you with little or no knowledge of Bayesian statistics, but with basic knowledge of R and/or Python
Part one of the tutorial will introduce Bayesian modelling from a simulation-based perspective. Part two will introduce three packages for doing Bayesian data analysis: Rstanarm, PyMC3 GLM, and Facebook's Prophet. Part three will introduce you to the popular and powerful Stan framework for building and running flexible Bayesian models in R and Python.
The course will be taught by Rasmus Bååth, a popular blogger and YouTuber on Bayesian methods, based at Lund University and King (makers of Candy Crush). Rasmus is particularly interested in making Bayes accessible to beginners and has experience through academic teaching, writing and creating online courses.
There is a reduced price for students and unwaged participants at GBP 125 including VAT -- email email@example.com to get this or if you want an invoice prior to paying through your organisation.
1 November 2019
Introduction to Survival Analysis
Survival, or time-to-event, analysis has been widely used in biomedical research for decades but is also increasingly relevant to analysts working in fields like marketing, social sciences and engineering. Data follow people, organisations or other subjects over time and capture the times at which an event of interest happens. A cancer researcher may be watching for recurrence of a tumour, while in marketing, the event of interest could be a customer calling in to cancel their contract.
Stata has an established and trusted capability to manipulate such data and carry out a wide range of analyses. This half-day online course, delivered by experienced medical statistician Robert Grant, will introduce newcomers to the tools that Stata offers.
23 September 2019
Stata Autumn School, day 1:
Introduction to Bayesian analysis in Stata
Stata has rapidly built a selection of Bayesian data analysis tools since version 14. The release of version 16 (anticipated summer 2019) adds more to this. There are also existing community-contributed interfaces to WinBUGS, JAGS and Stan. This day at Stata Autumn School will introduce these options and give participants a grounding in what Bayesian analysis is and how it can be useful. Further details of material to be covered will be added following the release of version 16.
24 September 2019
Stata Autumn School, day 2:
Multivariate analysis and unsupervised learning in Stata
Finding patterns in datasets of many variables is a key skill in contemporary data science, whether you call it multivariate analysis or unsupervised learning. This day at Stata Autumn School will look at the many options available in Stata and give participants a grounding in choosing an appropriate method and critiquing results.
18-19 November 2019
Bayesian data analysis in Stata 16
TBC Central London
Stata has rapidly built a selection of Bayesian data analysis tools since version 14. The release of version 16 (anticipated summer 2019) adds more to this with multiple chains, posterior predictive checking, and convergence diagnostics. Integration with Python has also made the PyMC3 library available for Bayesian analysis, and there are existing community-contributed interfaces from Stata to WinBUGS, JAGS and Stan.
This two-day course is aimed at anyone who uses Stata and is interested in learning about Bayesian analysis. We will use the latest functionality in Stata version 16 but you can also bring a laptop with Stata 14 or 15 and be able to take part in most of the course. After completing this course, you will have learnt:
Presenter Robert Grant is an experienced Stata user, trainer and medical statistician, who programmed the interface from Stata to Stan.
Contact Timberlake to book
9-13 December 2019
The ability to visualise data and analyses effectively is a highly prized skill in the data analysis workplace. Increasingly, employers are placing a high value on the ability to communicate as well as calculate. In this week-long course, you will learn how to design visualisations such as charts, maps and interactive graphics, which bring insight and understanding to your audience. You will learn about perception, the design process and the tools that you can use to make visualising easy. The course is highly interactive with group exercises throughout.
There will be two versions: for those who wish to practice some coding, for example with R, and for those who wish to work on paper throughout. In the last two days, participants in their groups will carry out a larger "project", where we present real-life data with a briefing from a researcher. The challenge will be to produce some visualisations and pitch them to the researcher on the last day, with feedback on your project work.
After taking this course, you will:
Winter / spring courses to be confirmed soon:
You can also contact Robert at firstname.lastname@example.org to discuss bespoke training for your team.
BayesCamp Ltd is incorporated in England and Wales, number 10666858. The term 'BayesCamp' and the Gaussian tent logo are registered trademarks.