Why Data Scientists should learn Statistics?

Data Science, AI and ML
#1

These are the three reasons why you should know statistics as a data scientist.

Know what you’ve got.

Understanding the data is the first step in creating a successful product. We can’t just throw raw data and expect the model to provide useful outcomes. Understanding the data takes a large amount of time in a regular workflow.

We can characterize what we have in terms of quantitative measurements using statistics. We can make sense of a large amount of data by using a few metrics rather than searching through everything.

Go above and beyond with what you have.

Statistics not only help us understand what we have, but they also help us broaden our horizons. Using statistics and a tiny sample of data, we can derive meaningful results about the entire scope. Inferential statistics is the name given to this branch of statistics. It helps us to broaden the scope of our discoveries based on the available data. It is critical since we rarely have data for the complete extent of the project.

Importing an algorithm is not all that machine learning entails.

It is not enough to just import and utilizes an algorithm in machine learning. We must properly prepare and process the data. Similarly, a model’s output must be carefully assessed. Statistical expertise is required for both activities, making it a must-have talent for data scientists.