Visualizing SQL: A Beginners Guide to Relational Databases

Convolutional Neural Networks, Random Forests, Support Vector Machines, and other cool machine learning models tend to be the bait that lures many scholars, such as myself, into the field of Data Science. We come with big dreams: to create stock trading algorithms, image classification for autonomous vehicles, and a whole string of other grandiose plans to improve the world through data science. However, the models are only as good as the data it is based on, which is why being able to extract the desired data will always be a skill which every data scientist, analyst, or statistician should be comfortable with. This article will act as a short guide to the design of segmented databases, provide a quick and dirty introduction to the theory behind such databases, as well as providing some exposure to Relational Schemas and Entity-Relationship Diagrams.

The problem with traditional methods of data storage, such as Microsoft Excel is two-fold. The simplest problem is that it is not designed to handle the quantity of information needed by most companies.Excel would crash immediately if one tried to view a table with millions of rows. Secondly, there is a real problem with allowing multiple users to access the information simultaneously. Doing so on Excel would not only slow down the computational power for everyone, but inevitably people would edit…