It's a shame there is a huge disconnect between what data scientists ACTUALLY do in their day to day vs what's published in the media. Many people see machine l

It's a shame there is a huge disconnect between what data scientists ACTUALLY do in their day to day vs what's published in the media. Many people see machine learning as synonymous with data science when a data scientist's real job is to leverage data to achieve business objectives. This has a wide range of implications from producing reports, to visualizations for understanding different kinds of trends. Model making is, imo, the fun part of the process, but by far from what actually needs to be done. That being said, kaggle is a great platform to learn on. There is a lot of value in understanding how to train a model, but it's only as valuable as understanding how to take data from disparate sources, cleaning and normalizing it, doing some EDA, and then understanding AFTER all that if you need a model to achieve your goals.