- Code is small, most of it is calling well-know libraries with the algorithms.
- Every 3-4 lines of code you usually show the results of what is happening (either how the data changed, a graph or whatever)
- 95% of the errors in data science come from bad practices: you either didn't clean up the data correctly, you didn't split the train/test/validation sets correctly or at the right step in the process, you chosed the wrong algorithm, etc
- most of the time you don't know whether you are doing something wrong or not because you don't have enough knowledge, but the code shows something all the time. No bugs in the code.
Those are the reason I don't care very much about bugs in the code because usually there are bigger problems in the data science process rather than in the code.
- Code is small, most of it is calling well-know libraries with the algorithms.
- Every 3-4 lines of code you usually show the results of what is happening (either how the data changed, a graph or whatever)
- 95% of the errors in data science come from bad practices: you either didn't clean up the data correctly, you didn't split the train/test/validation sets correctly or at the right step in the process, you chosed the wrong algorithm, etc
- most of the time you don't know whether you are doing something wrong or not because you don't have enough knowledge, but the code shows something all the time. No bugs in the code.
Those are the reason I don't care very much about bugs in the code because usually there are bigger problems in the data science process rather than in the code.