The purpose of this project is to create a series of data visualizations to better analyze and understand the cost of child care and its availibility in California. The main libraries I am currently using to accomplish this are Panda, Plotly, and Dash.
Over the past few months I've used Jupyter Notebook to manipulate existing data sets to analyze the data properly. I learned how to clean data sets and how to format the numerical data correctly so it can be used as an input. I found out how to combine two different data sets in order to showcase the large gap between child care availibility and its demand. I also learned what type of graph should be used to effectively show different types of data.
While developing my understanding of data science through this project, I have also been able to discover and learn multiple skills that can also be applied in other fields of computer science. Although this should have been an obvious discovery, I learned to thoroughly read through the documentation of libraries I use rather than simply skimming it. Also the format of Jupyter Note which allows you to create different cells taught me how to better organize my code into sections that made it easily digestible. And funnily enough, I also learned how to navigate through stackoverflow since I had to visit that website costantly to help me through multiple road blocks.