Setting Goals for Data Science Projects

A part of the Data Science & AI project planning and management Series

Contents:

· I. Intro
· II. Goal-setting considerations
∘ 1. Expected business value:
∘ 2. Required effort & resources:
∘ 3. Efficient achievement of goals possible?
· III. Project goals -Focus areas
∘ 1. Model development
∘ 2. Data flow pipeline
∘ 3. Documentation
· IV. Final words

I. Intro

We saw some examples of how to map a business problem to a data science problem that the project can solve. Now in this article, we will see what goals need to be set for a data science project.

II. Goal-setting considerations

When we set the project’s goals, we must consider 3 things:

1. Expected business value:

Remember that a data science project in a professional setting is not a hobby activity for enjoyment or intellectual stimulation. Your company should benefit from this project. The previous article provided instances of how data science challenges are solved to enhance income, reduce losses, make customers happy, gain market share, and so on.