T
Tomi Mester
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Yesterday 9 a.m. to 4 p.m. I worked on my data science project with complete focus.
No distractions.
Deep work.
Just as one wishes to work when reaching for high efficiency. And indeed, I churned out ~300 lines of Python code to crack a very challenging NLP classification problem. The result was good: the model achieved about 90% accuracy. That’s pretty good, actually! However, for this specific task, I needed closer to 99%.
So I sat down again, later the same day, around 11 p.m.
And I realized that there is a waaay better solution to the issue! I completely deleted my original script and replaced it with ~50 lines of Python — which I wrote in roughly an hour.
Now, the model reaches the desired 99% accuracy!
9am-4pm: I worked efficiently.
11pm-midnight: I worked effectively.
There is a difference.
P.S. The funny part is that it felt painful to delete my 9-to-4 code, even though I knew the second solution would be far superior. I wish I had thought of the second solution at 9 a.m., finished it by 10 a.m., and taken the rest of the day off. I guess I need to remind myself that mistakes are just stepping stones. ¯_(ツ)_/¯
Cheers,
Tomi Mester
The post Working Efficiently vs. Effectively with Python and Data Science appeared first on Data36.
No distractions.
Deep work.
Just as one wishes to work when reaching for high efficiency. And indeed, I churned out ~300 lines of Python code to crack a very challenging NLP classification problem. The result was good: the model achieved about 90% accuracy. That’s pretty good, actually! However, for this specific task, I needed closer to 99%.
So I sat down again, later the same day, around 11 p.m.
And I realized that there is a waaay better solution to the issue! I completely deleted my original script and replaced it with ~50 lines of Python — which I wrote in roughly an hour.
Now, the model reaches the desired 99% accuracy!
9am-4pm: I worked efficiently.
11pm-midnight: I worked effectively.
There is a difference.
P.S. The funny part is that it felt painful to delete my 9-to-4 code, even though I knew the second solution would be far superior. I wish I had thought of the second solution at 9 a.m., finished it by 10 a.m., and taken the rest of the day off. I guess I need to remind myself that mistakes are just stepping stones. ¯_(ツ)_/¯
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Cheers,
Tomi Mester
The post Working Efficiently vs. Effectively with Python and Data Science appeared first on Data36.