During my Pythonic data science team coaching I see various problems coming up that I’ve helped solve before. Based on these observations and my prior IP design and delivery for clients over the years I’ve put together a 1 day public course aimed at data scientists (any level) who want to be more confident with lower-risk approaches to delivering data science projects.
Successfully Delivering Data Science Projects runs on Friday February 1st 2019, early bird tickets have sold out, a handful of regular tickets remain (be quick). This course suits any data scientist who has discovered just how vague and confusing a research to deployment project can be, who’d like to be more confident in their plans and outcomes.
I’ve developed these techniques whilst working with the teams at companies like Hotels.com, Channel 4, QBE Insurance and smaller companies across domains including media, energy, adtech and travel.
The topics covered in the course will include:
- Building a Project Plan that derisks uncertainties and identifies expected deliverables, based on a well-understood problem and data set (but starting from…we don’t know what we have or really what we want!)
- Scenarios based on real-world (and sometimes very difficult) experience that have to be solved in small teams
- Team best practice with practical exercises covering coding standards, code reviews, testing (during R&D and in production) and retrospectives using tools such as pyjanitor, engarde, pandas profiling and discover-feature-relationships.
- Group discussion around the problems everyone faces, to be solved or moved forwards by everyone in the group (the group will have more experience than any single teacher)
- A slack channel that lives during and after the course for continued support and discussion among the attendees
You’re welcome to get in contact if you have questions. Further announces will be made on my low-volume training email list. I will also link to upcoming courses from my every-two-weeks data scientist jobs and thoughts email list.
Ian is a Chief Interim Data Scientist via his Mor Consulting. Sign-up for Data Science tutorials in London and to hear about his data science thoughts and jobs. He lives in London, is walked by his high energy Springer Spaniel and is a consumer of fine coffees.