Summer 2019 workshops: clinical audit stats, and a data science primer for managers

Edited 24 May 2019: the “primer for managers” course is postponed and will hopefully run in the winter (Northern Hemisphere obvs). Watch this space. The clinical audit one is going ahead.

 

I am doing two BayesCamp workshops in central London this summer:

  • Statistical Analysis for Clinical Audit, 21 June [bookings]
  • Data Science, Statistics, Machine Learning & AI: a primer for managers, 15 July [bookings]

These address two gaps in the market where it’s hard to get training at the right level.

 

Statistical Analysis for Clinical Audit

8-funnel

Clinical audit people often have basic statistical training, and until recently that’s been fine. National audit projects are now routinely using more advanced techniques, and the pressure is on local teams too to step up to this standard. Some of the topics we’ll cover are:

  • imputing missing data
  • casemix adjustment: training on old data and applying it to new data
  • seasonal and other time series adjustments
  • visualising audit data
  • the potential use of machine learning methods
  • how to choose software or contractors to carry out audit analysis

 

Data Science, Statistics, Machine Learning & AI: a primer for managers

robotIf you are in charge of a data science team, drawing on people with backgrounds in statistics, machine learning and maybe AI, life can be hard work. New buzzwords appear all the time and the pressure is on for you not only to produce demonstrable impact, but also to be seen to be using the latest methods. You have to compete with tech giants to recruit highly talented individuals, retain them, and keep the whole diverse team functioning cohesively. It’s easy for them to get technical training, but what about you? You don’t need to know how to run random forests or multilevel regression, but you do have to know what it is and when a vendor is trying to pull a fast one.

That’s where this workshop comes in. We will discuss the distinctive features of data analysis from the backgrounds of statistics and computer science, and the strengths and weaknesses on both sides. Then, we’ll take a look at the norms and motivations of employees coming from those backgrounds, and understand why they can antagonise each other. Throughout the day, there will be a lot of thought-provoking small-group exercises and great networking.

 

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