As aspiring data scientists or data scientists new to the field (and working in a business-centric rather than a research-centric role), it is easy to be blinded by the technical aspects of data science such as exploratory data analysis and machine learning, failing to see the forest for the trees.
My goal with this post and accompanying GitHub project is to provide a more accurate example of an enterprise-grade data science project that includes what is often missed in these sorts of examples: the rigorous positioning of a business problem and scoping of a data science solution.
The majority of the time spent to complete this project was allocated to the understanding and definition of the business problem rather than optimizing a machine learning model. As with any other complex problem, spending some time upfront to work through the various aspects of a business problem in a systematic way will reduce the risk of coming up with the wrong solution, which clearly wastes time (read: opportunity loss) and money.
My goal with this post and accompanying GitHub project is to provide a more accurate example of an enterprise-grade data science project that includes what is often missed in these sorts of examples: the rigorous positioning of a business problem and scoping of a data science solution.
The majority of the time spent to complete this project was allocated to the understanding and definition of the business problem rather than optimizing a machine learning model. As with any other complex problem, spending some time upfront to work through the various aspects of a business problem in a systematic way will reduce the risk of coming up with the wrong solution, which clearly wastes time (read: opportunity loss) and money.