Call it gentrification, supply-and-demand, call it whatever you’d like… the fact is, rent prices have gone up in Colorado in the last decade. Chip Oglesby – GitHub – did a nice analysis on the data provided by colorado.gov.
What Type of Data Visualization Do You Choose (if any)?
Determining whether or not you need a visualization is step one. While it seems silly, this is probably something everyone (including myself) should be doing more often. A lot of times, it seems like a great way to showcase the amount of work you have been doing, but winds up being completely ineffective and could potentially harm what you’re doing. Once you determine that you actually need to visualize your data, you should have a rough idea of the options to look at. This post will explain and demonstrate some of the common types of charts and plots.
Introduction to Data Visualization – Theory, R & ggplot2
The topic of data visualization is very popular in the data science community. The market size for visualization products is valued at $4 Billion and is projected to reach $7 Billion by the end of 2022 according to Mordor Intelligence. While we have seen amazing advances in the technology to display information, the understanding of how, why, and when to use visualization techniques has not kept up. Unfortunately, people are often taught how to make a chart before even thinking about whether or not it’s appropriate.
In short, are you adding value to your work or are you simply adding this to make it seem less boring? Let’s take a look at some examples before going through the Stoltzmaniac Data Visualization Philosophy.
I am asked this question regularly, both online and in person. There is a simple answer: it doesn’t matter. There are pros and cons to both which have been written about extensively so I won’t reinvent the wheel by making a list here (do a quick search in Google and you’ll find tens of thousands of relevant results).
There is a plethora of classification algorithms available to people who have a bit of coding experience and a set of data. A common machine learning method is the random forest, which is a good place to start.