One of the toughest challenges for data scientists, and big data engineers is gathering, preparing, and transforming data, and creating data pipelines.

Gathering data for a machine learning or big data initiative can be hard work. The mere act of getting it all together can leave data teams so excited that they can overlook critical safeguards. The result? You could have data that’s skewed, or biased, or data sets that are too small to generate accurate models.

This is why it’s important to have a pre-ML data checklist. A pre-machine-learning data checklist will ensure you have the right data sets for your models, thereby improving your chances of success.

Here are some other benefits:

You waste less time: A checklist allows you to save the time you would normally spend trying to work through your own mental checklist.

Fewer errors: A pre-ML data checklist ensures you have don’t overlook obvious mistakes, and have relevant, unbiased, and representative data.

Lower cognitive load: By using a checklist, you remove the burden of unnecessary cognitive load. This enables you to free up the brain power required for more productive tasks such as model selection and tuning.