Data visualization is an important component of the entire data science workflow. It is the data display after data analysis, including chart design, dynamic combination, two-dimensional charts, three-dimensional charts, linkage, drilling, large-screen display, etc.
With the appropriate visualizations, a data scientist can understand the structure of the data they have, identify potential issues, craft analytical strategies, and summarize their results for others.
Data scientists pretty much need to master the art of data visualization.
If you’re a beginner, don’t worry, this post is exactly for you.
Today, we’re going to give you a few data visualization examples and project ideas that you can execute to get better at the art of data visualization.
But wait, here are a few things that you should keep in mind before diving into your first data visualization:
- Employ simplicity. Simplicity is the key to creating effective data visualizations. You should focus on using simple charts that are easy to digest.
- Display only the most important information. When you’re new to charting, you may want to put in a lot of information so that you don’t leave out anything of value. Resist this urge, because people can take in only so much information before their eyes glaze over.
- Require little explanation. Don’t make things so complicated that your users require a manual to understand what you’re trying to convey.
- Don’t overload your data. It’s important to avoid overloading your data visualization.
- Stay away from 3-D. It’s recommended that you avoid this type of chart until you get more experience under your belt.
Now that you know what not to do, here are a few data visualization project ideas for beginners:
Before you start off with any project, you need to learn how to quickly make the fundamental charts. Charts like a scatter plot, histogram, bar chart, box plot, heat map, correlogram, etc.
You can use any small dataset from an external resource to practice most of these fundamental charts. In case you’re using Python, you’ll find a few built-in datasets that you can use.
Get a good time-series dataset like Tesla and Apple stock prices, Ozone level detection, or literacy rates over time. You can use these datasets to create a time-series plot.
Time-series: These are problems where a numeric or categorical value must be predicted, but the rows of data are ordered by time.
In simple words, a time-series plot is just a line graph, but you have to pay special attention to the dates on the X-axis, especially when your chart contains multiple lines with different start and end times. Also make sure your Y-axis has the correct units, and that both your lines are in the same units.
You can use a map chart to compare values and show categories across geographical regions. Use it when you have geographical regions in your data, like countries/regions, states, counties or postal codes.
Being able to make attractive data maps is a huge bonus. Here are some project ideas that you can try out: 10 examples of interactive maps data visualization
Word clouds are surprisingly informative, particularly in natural language processing work. A word cloud is a collection, or cluster, of words depicted in different sizes. The bigger and bolder the word appears, the more often it’s mentioned within a given text and the more important it is.
Also known as tag clouds or text clouds, these are ideal ways to pull out the most pertinent parts of textual data, from blog posts to databases. They can also help business users compare and contrast two different pieces of text to find the wording similarities between the two.
Here’s a tutorial for creating word clouds in python: (Tutorial) Generate Word Clouds in Python
There are a ton of data visualization projects for beginners and you’re not just limited to this list, but it is a good place to start if you’re a complete beginner.
The data visualization projects mentioned above are a great place to get started for beginners because:
- They are so simple and easy to understand.
- You can plot them easily in Excel or any other basic tool.
- You can easily plot the predictions compared to the expected results.
- You can quickly try and evaluate a suite of traditional and newer methods.
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