Data Science Shorts

20 Key Things Every Data Scientist Needs To Understand to Be in the Top 1%

“The best way to learn data science is to do data science”.

1. Your first model will not be the best one; reiteration is the key.
2. Documenting your code is equally important as finding ideal parameters for models.
3. It's always a good idea to divide your analysis into different files.
4. Simplicity often outperforms complexity; don’t assume complex models are the solutions to every problem.
5. Knowledge of databases is as important as knowledge of different ML algorithms.
6. A good understanding of the problem domain will enhance the quality of your model.
7. Your insights are valuable only if others can understand them;
8. Always divide your time to be spent on a project into different parts where data analysis should own the biggest pie.
9. Never make assumptions based on base param score
10. It's a good idea to Group Bar and Pie Charts Together.
11. mean() is not the answer always; Try using other methods for replacing missing values.
12. Always place labels on your graphs; a million-dollar painting without a signature is of no worth.
13. Make sure to try different evaluation metrics.
14. Automate certain tasks that don’t require creativity.
15. 1% of clean data is always better than 99% of garbage; data preprocessing is the key;
16. Algorithms are tools, not magic wands; understand them deeply.
17. “Data science is not a playground for developers to mimic”; it is a scientific expedition, where each analysis is a unique exploration.
18. Never Output a Particular Number For Regression; Make use of probability and range;
19. Try to use sample() instead of head() and tail() to get a better understanding of data.
20. If you can’t explain your analysis it's pure garbage.

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