While data science has matured in recent years, it’s still not a mature field with well established patterns, practices, etc,

As such, there isn’t a textbook answer for building a successful data science workflow.

Instead, data scientists undertaking new data science projects must consider the specificities of each project, past experiences, and personal preferences when setting up the source data, cleaning the data, modeling, monitoring, reporting and more.

While there’s no one-size-fits-all method for data science workflows, there are some best practices, like taking the time to set up auto-documentation processes and always conducting post-mortems after projects are completed to find areas ripe for improvement.

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