Another Successful Data Science Projects course completed

A week back I ran the 4th iteration of my 1 day Successful Data Science Projects course. We covered:

  • How to write a Project Specification including a strong Definition of Done
  • How to derisk a new dataset quickly using Pandas Profiling, Seaborn and dabl
  • Building interactive data tools using Altair to identify trends and outliers (for quick discussion and diagnosis with business colleagues)
  • A long discussion on best practice for designing and running projects to increase their odds of a good outcome
  • Several diagnosis scenarios for prioritisation and valuation of potential projects

One of the lovely outcomes in the training slack is that new tools get shared by the attendees – I particularly liked Streamlit which was shared as an alternative to my Jupyter Widgets + sklearn estimator demo (which shows a way to hand-build an estimator under Widget control with GUI plots for interactive diagnosis). I’m going to look into integrating this in a future iteration of this course. Here’s my happy room of students:

Success course group photoIf you’re interested in attending my future courses then make sure you’re on my low-volume training announce list (and/or you might want my more frequent Thoughts & Jobs email list). My upcoming Software Engineering for Data Scientists course has a seat left, if that’s sold out do contact me to be on the reserve list. If you’d like to get a discount code for future courses please complete my research survey for my 2020 courses.

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.