Data visualization and data analytics are hot topics in business today. Why? Because visualizing and analyzing data not only helps you see patterns, but it also helps you understand what those patterns mean. In this blog post you will learn about why businesses use data visualization and data analytics, what differentiates a Data Visualization from an Analytics project and how to start working on your own projects with these tools.
What is Data Visualization?
Data visualization helps you gain insight into the data you have collected. Visualization can be done in several different ways: - Bar Charts - Visualize data with a bar chart. You can plot different values against each other and make trends more visible. - Bar Graphs - Visualize data with a bar graph. This type of graph is useful when you want to compare data values. - Pie Charts - Visualize data with a pie chart. This is a great chart type to use when you want to show percentages. - Line Charts - Visualize data with a line chart. You can plot different values against each other with this chart type and see trends become clearer. - Diagrams - Visualize data by using diagrams. You can create infographics with diagrams and create awareness among your audience.
Why use Data Analytics?
Analytics help you understand why people come to your site and what they do once they are there. This means you can create a better user experience and make better business decisions. You can use both qualitative and quantitative data to build your analytics projects. This can include data from websites where users have filled out forms, emails that you have sent, or surveys that you have asked your customers. Quantitative data can include things like how many times a certain page was viewed, how much time people spent on specific parts of the site, or what kinds of emails they responded to. Qualitative data is more difficult to quantify, but could include things like how satisfied a user is with the product, how much they liked a page, or how they feel about your company as a whole.
Differences between a Data Visualization and
an Analytics Project
Visualizations can be used to gain insight into your data, while analytics projects are used to understand why people are visiting your site, what they do once they are there, and what impact your company has on their lives. Visualizations don’t necessarily need to have a purpose of understanding your data, whereas analytics projects need to have a purpose of understanding the data. You can have both qualitative and quantitative data for your analytics projects. Visualizations can also have both qualitative and quantitative data, but the purpose of the visualization is to make the data more accessible. For example, you can have a diagram that shows how many visits to your site came from specific countries, cities, or IP addresses. While this is not necessarily used for analytics, it could help you see patterns in your data.
Tools to work on Data Visualization Projects
There are a lot of tools you can use to build data visualization projects. Some popular ones are: - Tableau - This software is great for doing data visualizations, business analytics, and data science. It’s also free to use. - Adobe Photoshop - This is a great tool for creating visual designs that go along with your data visualizations. - Microsoft Excel - This is still a popular tool for doing basic data analysis and plotting graphs. - Open Source Tools - There are many open source tools out there that you can use for building your projects. GitHub is a great place to find these.
Conclusion
Visualizing your data can help you gain insight into your data, see patterns and trends become clearer, and understand the impact your company has on your audience. There are many different types of data visualization projects, and each one has its own different purposes. If you’re interested in learning more about data visualization and data analytics, try starting with a project that doesn’t have a specific purpose. You can always switch up your project later once you learn more about data visualization and data analytics.
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