I’m part way through a Masters in data science and projects always require “understanding the business context” and “proposing a business case” when working with a dataset and building ML models. That’s the part I find most challenging because it’s outside my context.
In that case I recommend focusing on operations, anything that makes the customer journey (aka value chain) run smoother, faster, or look for levers. Check out this case study from HubSpot. What they call the "customer file" is basically a training dataset that could be fed to any ML algo.
This was exactly what I needed to read about.
I’m part way through a Masters in data science and projects always require “understanding the business context” and “proposing a business case” when working with a dataset and building ML models. That’s the part I find most challenging because it’s outside my context.
More please!
In that case I recommend focusing on operations, anything that makes the customer journey (aka value chain) run smoother, faster, or look for levers. Check out this case study from HubSpot. What they call the "customer file" is basically a training dataset that could be fed to any ML algo.
https://wrap-text.equals.com/p/an-analysts-quest-to-improve-retention