Time series are everywhere, if you notice them or not. They provide a great wealth of knowledge but only if you grasp what they are. For a long time, I was confounded with the concept and that was my motivation in writing this piece. Hopefully this clears up your confusion too.

I used the Northwind database to showcase some SQL skills as well as practicing some foundational knowledge of statistical testing. All the coding is done in python.

Visualizations, in my naive opinion, are the cornerstone to understanding what you are doing in data science. It is easy to quickly call something in your jupyter notebook and check you are handling the kind of the object you think you are. It gets a bit harder when it comes to visualizing your data. There are plenty of quick ways to see what the spread of your data, quantity, etc. A histogram does wonders. But what about the data you don’t have? Or you have some other piece of information that ties the data to the physical world (an address, lat/long, or even something more specific). There are a number of easy to implement libraries to help you see how your data set internal looks and mapped onto things.