New public course on Successfully Delivering Data Science Projects for March 1st

On Friday February 1st I ran my first Successfully Delivering Data Science Projects, this is a part of my new plan to give more training this year. This went really well and I got to both teach and learn a lot from my students. We talked through best practice, project design, derisking strategies, communication plans and we tried various new tools that’ll improve workflow. Conversation has continued in our private slack channel (which all attendees get access to).

The next iteration of Successfully Delivering Data Science Projects is online for March 1st, the course has half sold-out already. If you’d like to improve your confidence around the successful delivery of Python data science projects – you’ll want to get a ticket soon. The material I teach is based on years of helping clients from start-ups to corporates to successfully deliver data science projects.

I’m really happy that the discursive format gave room for students to raise their own issues and to add recommendations for tools and books in addition to my own. We continued our conversations in the pub after whilst decompressing – there we got to dig into some of the hard topics (such as mental health, imposter syndrome and running open source projects) in a more relaxed setting.

The topics covered in the next iteration 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!) – you take the project plan template away for use in your own projects
  • 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 nbdime, pandas profiling and discover-feature-relationships – you take away the solutions and a guide to running code reviews to support relentless quality improvements in your team’s solutions
  • 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.

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