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6 Bad Habits Killing Your Productivity in Data Science
And what to do for achieving success
Learning data science is like learning how to play a musical instrument — you must develop good habits and get the foundations straight to succeed.
Just like a musician requires scales, arpeggios, and rhythm exercises before being able to play concertos, a data scientist needs to ingrain key practices to develop their potential.
Avoiding detrimental habits and cultivating productive ones allows you to shift your mental focus from the mechanics to the artistry of your work.
Developing data science habits like using virtual environments and tracking experiments transforms your workflow from a struggle to a smooth-flowing creative process.
In this article, we’ll explore six everyday bad habits that can secretly destroy your effectiveness as a data scientist and provide tips to help boost your productivity.
Using the system interpreter
A virtual environment is a siloed Python installation separate from your system environment. It lets you install packages and libraries for a specific project without affecting your system Python setup. Neglecting to use virtual environments can lead to dependency hell.
