9 Quick guide to data visualization
What is data visualization?
Data visualization is the graphic display of information and data utilizing interactive visual tools like maps, charts, graphs, and other visual elements. It is used to make complex data sets easier to grasp and express findings in a clear and simple manner.
Visual data representation makes it simple to spot patterns and trends that may not be immediately obvious from raw data. Because data analysis and dissemination of findings are crucial in industries including business, science, government, and journalism, data visualization is a valuable tool in these sectors.
In order to effectively communicate insights and information, data visualization must take into account the intended audience, the type of data being presented, and the best visualization approaches.
Why is data visualization important?
Data visualization is crucial for a number of reasons:
1) Complex information communication: By displaying complex information in a visual style, data visualization makes it easier to obtain and comprehend. This makes it possible for individuals to rapidly and simply understand data and spot patterns, connections, and trends.
2) Supporting decision-making: Visualization aids in decision-making by presenting data in a clear and succinct manner. It allows decision-makers to base their choices on data-driven insights.
3) Increasing engagement: Compared to conventional modes of data presentation, data visualization can be more intriguing and engaging. Users can examine data in a more engaging and intuitive way with the help of interactive visualizations, which can improve knowledge and engagement.
How is data visualization used?
Various fields employ data visualization in different ways. Here are a few instances of data visualization in action:
Business: To evaluate and present financial data, sales trends, market research, and customer behavior, businesses employ data visualization. This aids in the development of sensible decisions and successful company strategies.
Science: The presentation of scientific data, such as experimental findings, statistics on climate change, or genetic information, uses data visualization. Visualization technologies can assist researchers in exploring and interpreting large, complicated data sets, as well as communicating findings to the public and other scientists.
Government: Governments convey data on topics including population demographics, public health, crime statistics, and election results via data visualization. This aids in informing programs and policies and disseminating information to the public
Journalism: Journalists employ data visualization to produce interesting and fact-based articles. This aids in conveying difficult information in a manner that the general audience can understand and find interesting.
Education: To help students understand difficult concepts, data visualization is being used more and more in classroom settings. It is possible to utilize it to produce interactive visualizations that assist students in exploring data sets, comprehending mathematical ideas, and visualizing scientific occurrences.
Personal use: Tracking fitness objectives, displaying personal finances, or examining social media activities are just a few examples of how data visualization can be utilized for private purposes.
What is the data visualization process?
The process of data visualization can be broken down into several steps, including:
- Create your research question first.
Understanding the purpose of your research must be a priority. This will define the kind of data required, the kind of analysis required, and the kinds of visualizations that will best convey your research or conclusions. - Get your data.
The web provides access to a vast collection of metric, metrical, and spatial data. You can also download a vast amount of open data online. You can use the Qualtrics web survey platform to create, distribute, and get guidance on survey design, development, and distribution, which will help you with data collection, aid with developing your own data using techniques like digitizing photos and documents, gathering data via APIs, and scraping data from online sources. - Clean your data.
Prior to constructing a visualization, this step must be completed. Visualizing data that is clear and consistent will be considerably simpler. Clean data is devoid of flaws or anomalies that can make the data difficult to utilize or interpret. Instead of attempting to identify and correct problems as you create visualizations, you can concentrate on generating an effective visualization by starting with a clean dataset. The dataset you’re dealing with will have a big impact on the data cleansing duties. Data cleansing typically entails:Removing extraneous variablesRemoving redundant rows and observationsTaking care of outliers or bad dataHandling missing valuesCreating categories or standards for valuesMaking typographical corrections
Visit Clean and Prepare Your Data for further details and best practices for data cleansing.
- Select a type of chart.
It’s crucial to choose a chart or graph that will help you convey your ideas to your audience. To choose a chart, you must first decide what message you want to convey.
Chart Chooser: This website offers Excel and PowerPoint templates for creating a variety of charts.
Catalog of Data Visualization: An effective formula for choosing a chart depending on your analytical or communication requirements
- Choose your tools: There are many tools to choose from ranging from freely available open web-based tools to licensed desktop tools. Refer to the section below.
- Prepare data: The kind of chart or visualization you want to make and the tool you use will influence the steps you need to take in data preparation.
Common data preparation tasks consist of:
1) Formatting correctly (treating numbers as numbers and dates as dates)
2) Put values into the proper units.
3) Focus on the information that interests you by filtering your data.
4) Data can be grouped and aggregate values (counts, minimum, maximum, mean, median, and mode) created.
5) Obtain values from intricate columns
6) Creating new columns by combining variables - Create your own data visualization
The standard approach for making charts entails the following steps, which should be followed:1) Enter data into a software you choose
2) Pick the kind of chart you want to make
3) Determine whether the chart is effective
4) Apply design concepts to refine
The effectiveness of your chart might be significantly impacted by the way it is designed. Think about these design tenets.
