A Quick Overview of Data Visualization
A Quick Overview of Data Visualization
Data visualization is playing an increasingly important role in big data and advanced analytics projects. This article just scratches the surface of what it's all about.
Join the DZone community and get the full member experience.
Join For FreeLearn how to operationalize machine learning and data science projects to monetize your AI initiatives. Download the Gartner report now.
Data visualization enables decision-makers to see analytics presented visually so that they can grasp difficult concepts or identify new patterns. Data visualization software plays an important role in big data and advanced analytics projects.
Applications of Data Visualization
Many business organizations implement data visualization software to track their own initiatives. For example, a marketing team might implement a data visualization software to monitor the performance of an email campaign and track metrics like open rate, click-through rate, and conversion rate.
Nowadays, data visualization is also used as BI reporting tool.
Benefits of Data Visualization
As per Robert Cordray's article, the benefits of data visualization are:
Faster action.
Communicate findings in constructive ways.
Understand connections between operations and results.
Embrace emerging trends.
Interact with data.
Create new discussion.
Data Visualization Tools
Here is a list of some of the best data visualization tools!
Tableau
Tableau Public is perhaps the most popular visualization tool. It has a very large customer base of 57,000+ accounts across many industries and supports a wide variety of charts, graphs, maps, and other graphics.
You can download the free version of Tableau's data visualization software here. Users can easily drag and drop data into the system and watch it update in real-time, as well as collaborate with other team members for quick project turnaround.
QlikView
QlikView is the most flexible business intelligence platform for turning data into knowledge. More than 40,000 organizations in over 100 countries have enabled their users to easily consolidate, search, and visually analyze all of their data for unprecedented business insight using QlikView’s simplicity. It brings a whole new level of analysis, insight, and value to existing data stores with user interfaces that are clean, simple, and straightforward.
QlikView is commonly used alongside its sister package, Qliksense, which handles data exploration and discovery. Try Qliksense by downloading the free version here!
FusionCharts
This is a very widely used, JavaScript-based charting and visualization package that has established itself as one of the leaders in the paid-for market. With over 90+ chart types and 965 maps, you’ll find everything that you need right out of the box.
FusionCharts supports both JSON and XML data formats, and you can export charts in PNG, JPEG, SVG, or PDF formats. They have a nice collection of business dashboards and live demos for inspiration.
You can always get started with their unrestricted free trial and then only buy it if you like it!
Datawrapper
Datawrapper is increasingly becoming a popular choice, particularly among media organizations that frequently use it to create charts and present statistics. Once you upload the data from CSV file or paste it directly into the field, Datawrapper will generate a bar, line, or any other related visualization.
It is very easy to use, and it produces effective graphics.
Highcharts
Highcharts is a purely JavaScript-based charting library meant to enhance web applications by adding interactive charting capability. Highcharts provides a wide variety of charts; for example, spline charts, line charts, area charts, bar charts, pie charts, and so on.
Conclusion
Here, I explained what data visualization is and provided information about five of the most popular data visualization tools on the market. There are many more tools available, so don't hesitate to leave your insights about other tools in the comments!
Bias comes in a variety of forms, all of them potentially damaging to the efficacy of your ML algorithm. Our Chief Data Scientist discusses the source of most headlines about AI failures here.
Like This Article? Read More From DZone
Published at DZone with permission of Saanvi Sharma . See the original article here.
Opinions expressed by DZone contributors are their own.
{{ parent.title || parent.header.title}}
{{ parent.tldr }}
{{ parent.linkDescription }}
{{ parent.urlSource.name }}