Training

Higher Performance Python runs April 9th. You'll learn to profile slow & memory hogging code including Pandas and sklearn, learn to compile using Numba and to parallelise to multi-core with Dask.

Software Engineering for Data Scientists runs on May 4-5th in Central London, you'll learn to write robust research code which can be deployed to production for greater confidence across the whole team.Join Ian’s data science training list to hear about upcoming training events in the UK for 2020.
Intermediate Scikit-Learn will run in May. You'll learn to diagnose bad predictions, explain decisions, identify bad data and quick processes to efficiently and automatically get working first-pass models so you can show if there's useful signal in your data.
Successful Data Science Projects runs in July in Central London, we'll work through how to estimate the value of a project, derisk it, design a project and develop a defensible project specification to improve the value and likelihood of your delivery. This course also covers leadership training. The next session will be later in 2020.

Successfully Delivering Data Science Projects:
"After attending the course I can identify and communicate to the project team and client the uncertainties of the project efficiently. I am using the techniques covered on the course to write project initiation documents and put in place the necessary processes to reduce uncertainty. The course was very engaging and I was very happy to learn from Ian's experience to ensure a successful delivery on all future projects." - Dani Papamaximou, Data Scientist at Arcadia

"One of the highlights from Ian’s Successfully Delivering Data Science Projects course was being introduced to the concept of a specialised project specification document. This provides a systematic framework to directly tackle numerous problems I have experienced when trying to move a project beyond an initial prototyping stage. I have now applied my own tailored specification document at my organisation and it immediately surfaced critical questions and issues that otherwise would not have been realised for months." - Thomas Brown, Data Scientist at aire.io