Best Practice for Data Scientist to handle Daily Tasks in Any Industry

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As a Data Scientist, It is required to understand business problem from root to trouble shout it.

Data Scientists spend 80–90% of time on collecting data , pre processing and cleaning them and finally feed them in algorithms , check accuracy and fine tune algorithm and select best algorithm to solve business problem.

Enough generic discussion did for main tasks of Data Scientist Life . I will share few questions which are expected from Data Scientist that every one should know them.

1. How do I handle missing data in my dataset?
2. What are some techniques for dealing with categorical variables in my model?
3. How do I evaluate the performance of my model?
4. How can I avoid overfitting in my model?
5. How do I properly split my data into training and test sets?
6. How can I select the appropriate algorithm for my problem?
7. How can I ensure that my model is not biased?
8. How do I interpret the results of my model?
9. How can I improve the performance of my model?
10. How can I properly handle large-scale and high-dimensional data?