What is Data Visualization?
Simply put, data visualization is a graphic representation of data. Data visualization allows you to interpret data more easily, identify trends, and take action.
A huge appeal of data visualization is its ability to wrangle together big data. “Big data” describes a tremendous volume of structured and unstructured data that is often so large that it is difficult to process using traditional methods. Big data could be anything—migration patterns, internet trends, market growth—and this information often grows at a rapid rate. This is where data visualization comes in handy since its goal is to convey information clearly and efficiently in order to communicate specific ideas.
Good data visualization involves data exploration, data analysis, and intentionality. Many consider it to be both a science and an art. Though there are many tools available for data visualization, some of the most popular tools are Tableau, Excel, Python, and Power BI.
Data Visualization Tools
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Excel: Although Excel has its limitations in data visualization, it can be very effective for making quick, one-off reports. Excel is fairly easy to use and many people already have familiarity with the tool. Since Excel is not primarily a data visualization software, you’ll need to learn some advanced techniques to create valuable and dynamic charts. In our Advanced Analytics & Visualization in Excel course, you’ll learn lots of techniques to maximize the visualization capabilities of Excel. Learn more about our Excel classes in NYC.
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Tableau: Tableau is one of the most commonly used data visualization tools. Tableau has robust visualization capabilities with a large collection of data connections and visualizations off the shelf. The tool requires limited coding knowledge and uses simple drag-and-drop functionality. Take one of our Tableau classes to master data visualization in Tableau.
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Python: Python is a programming language that is also commonly used for data science and analytics. As part of Python as an analytics tool, it provides lots of data visualization functionality. There are a handful of packages specifically for visualization like Matplotlib and Plotly, among others, that makes data visualization in Python a lot easier. In our Python classes in NYC, you’ll learn how to use Python to import, clean, analyze, and visualize data.
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PowerBI: Another popular tool is PowerBI from Microsoft which is similar to Tableau, but has various advantages and disadvantages relative to the other options. Some of the advantages of PowerBI include its affordability to get started (free version), custom visualizations, and Excel integration. The key limitations of PowerBI are the challenges of managing complex data relationships, the configuration of visualizations, and its relatively steep learning curve as a new tool.
Different Types of Visualizations
There are many types of visualizations that can be used to display data. Some of the commonly used charts include:
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Line Charts: The simplest technique, a line chart shows the relationship between two variables on the x and y-axis. A use case for a line chart would be to track the number of cars sold at a dealership since 2000. The x-axis would be time, in years, and the y-axis would contain the number of cars sold each year.
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Bar Charts: These charts are used for comparing the quantities of categories or groups with rectangular bars proportional to the values they represent. For example, you might represent the number of cars sold and the number of employees as a double bar chart.
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Histogram: Used to show the frequency distribution of continuous variables. For example, to see the distribution of students within a certain test score ranges, you would use a histogram.
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Pie Chart: There is a lot of debate about the usefulness of pie charts because they are difficult to interpret. Say you wanted to see how your sales were split by state, you might use a pie chart for a quick visualization.