Dr. Andrew Chamberlain’s Data Visualization Posts


Data visualization is one of the most important skills in data science. It’s not enough to just tell people that your model is better than another. You have to show them why it’s better and how they can use those insights themselves. This post will give you an overview of the history and power of data visualization, as well as some tips on choosing the right chart type for your data.

Dr. Andrew Chamberlain’s Data Visualization Posts

Data Visualization for Beginners

Data visualization is a powerful way to convey information. It can be used in many different ways, such as telling stories, showing trends and relationships between variables. Data visualization allows you to see the data in your head without having to write down anything on paper first.

A History of Data Visualization

Data visualization has been around for a long time. The first data visualization was probably a cave painting, which is the same thing you do when you make charts and graphs today: you’re trying to convey information in a way that makes it easier for humans to understand.

The first tools used for data visualization were paper and pen (or whatever else people had back then). Then Excel came along, followed by Tableau and other software packages designed specifically for making charts and graphs. And now we have big data analytics platforms like Splunk, where all of these tools can be combined into one piece of software!

The Power of Data Visualization

Data visualization is a powerful tool to help you make sense of the data you have. It can be used to communicate your story, identify trends in that story, and understand complex relationships in the data.

It also has the potential to help you make decisions based on your findings–decisions that could have a huge impact on your business or organization.

How to Choose the Right Chart Type for Your Data

The first step in choosing a chart type is to think about what story you are trying to tell. If you want to show the change in sales over time, then use a line chart. If your audience needs to compare two sets of data, such as average income by gender and age group, then consider using a bar chart. If there is no obvious pattern or trend within your data set (e.g., sales figures), consider using an area chart instead of a line graph because it shows more information at once without making readers feel overwhelmed by too much information on the page.*

What Makes a Good Chart?

Good charts are easy to understand, read and interpret. They let you compare data easily, which is important for spotting trends in your data or comparing different sets of information. This means that your chart should have a clear title that explains what the chart shows, as well as labels on each axis (the horizontal lines) so you know where each number comes from.

Good charts also make it easy for people who don’t work with your type of data every day–or even at all–to understand what’s going on with the numbers in them: if someone else can’t figure out what a bar chart represents after looking at it briefly, then there’s probably something wrong with either their design or their content (or both).

The Best Time to Use a Bubble Chart

You should use a bubble chart when you want to compare two or more sets of data. For example, let’s say you have sales data for your entire company broken down by product line and region. You could use a bar graph that shows each product line and its respective sales figures on the y-axis (vertical axis), but this would make it difficult to see where one product line ends and another begins–you’d have no idea how much each region sold overall because they’re all bunched together in one big blob at the bottom of your chart! A better option would be using a bubble chart with two axes: one showing products sold by region, while another shows regions by number sold within them. This way, each bubble represents both values simultaneously–so when looking at any given point (i.e., “Northern Region”), we’ll know exactly how many units were sold in that area without having to look elsewhere on our graph for those numbers separately

Know what chart is best for your data.

Knowing what chart is best for your data is an important skill. In this post, we’ll go over the pros and cons of each chart type and how to choose the right one for your needs. Then we’ll look at some example charts from different industries so you can see how they’re used in practice.

We’ll start with a quick refresher on the different types of charts: bar graphs, line graphs (also called time series), pie charts and scatter plots are all common types of visualizations that can be used in data science projects or presentations. You should take note of their strengths and weaknesses as well as any special formatting requirements before deciding which one will work best with your data set!


If you have any questions about the best chart type for your data, please feel free to reach out! We’re always happy to help.