How to Make Data Science Workflow Efficient?

There aren't always textbook solutions to challenges in a less developed sector like data science. While embarking on new data science projects, data scientists must consider the project's specifics, previous experiences, and personal preferences when setting up the source data, modelling, monitoring, reporting, and other tasks. While there is no one-size-fits-all solution for data science workflows, there are some best practices to follow, such as spending time setting up auto-documentation procedures and always doing post-mortems once projects are done to identify areas for improvement. Data scientists adjust their data science procedures to fit the needs of their teams and organizations. What additional best practices do they employ to improve their data workflows? Just a few examples include code reviews, communication between data scientists and data engineering teams, and agile settings. You could make your data workflow process efficient by : 1) Having a project plan drafted keeping in mind the key stakeholders decisions in mind. 2) Internalize system processes to make data transformation, cleaning & modeling streamlined. 3) Always have the customer or end user in mind while working through the collected data samples.

4) Follow tried and tested practices to increase productivity like they say - never reinvent the wheel. 5) Stay flexible through a rigid process, as it always would allow for any last minute adjustments.