Introduction
When doing data science or leading a project I invariably both learn something new or find gaps in my understanding on things that I thought I knew well.
A good forcing mechanism for me to ensure I have understood a concept a little better is to try to write something down that makes sense at the end of a project. Simply trying to
articulate an idea more often than not highlights where the gaps in knowledge are. A useful byproduct is then being able to share these ideas more clearly with my team and other colleagues.
I've captured here a selection of notes from some of my data science experiences, anonimised to preserve proprietary commercial knowledge, that illustrate a key theme or idea - I hope some are as useful to you as they were to me writing them!
In development
- Product recommender engine with R and RShiny
- Categorical variables: label encoding vs. one hot encoding
- Real world Time Series analysis - a comparison of ML techniques
- PDF scraping with Python
- Fully automated Dashboards with Alteryx Gallery and Tableau Server
- GPUs and TPUs with Google Colab