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In this tech world, it is essential to interpret and visualize data to take data-driven decisions for businesses. Visualization of data effectively assists in getting insightful insights from a massive amount of data. Here, we will explore the top 10 free tools for data visualization:
Data visualization involves turning data into visual forms like maps or graphs to simplify its comprehension and extraction of insights for the human mind. Its primary aim is to simplify the identification of patterns, trends, and anomalies within extensive data collections. It is frequently referred to as information graphics, information visualization, or statistical graphics.
It involves the collection, processing, and modeling of data, and precedes the drawing of conclusions. Hence, data visualisation is part of the broad area of data specification and dissemination as it involves knowing, finding, altering, formatting and delivering data.
Data visualization offers a fast and efficient method to convey data across different audiences through the use of visual elements. While business experts vary in their specific knowledge and skill sets, the goal of visualizations is to ensure their comprehension by all. These visual aids simplify the process for workers within a company to make choices and take actions based on the knowledge they gain from them. Some of the importance of data visualization includes:
Data visualization can be described as one of the most effective tools used in the analysis of data through the determination of relationships within the data set. It is because when data is in a format which shows trends as images then it is easier to view then when the data is in a tabular form.
Through a combination of trends, figures and graphical analysis, it becomes easier to understand the role and importance of data in the overall concepts of a given field. It illustrates the relationship of specific data points to the overall dataset.
Understanding the setting of the data through data visualization is challenging. Context, which encompasses all the surrounding details of the data, is hard to comprehend solely by examining figures in a table.
Collecting insights from data through visualization is undoubtedly quicker than merely examining a chart.
Tableau stands out as a leading tool for business data visualization. Through a combination of trends, figures and graphical analysis, it becomes easier to understand the role and importance of data in the overall concepts of a given field. It helps in understanding how small particularities relate to the wider data set as a whole.
Tableau grants you access to a majority of the features found in the premium Tableau Desktop edition, also links to different data sources such as CSV, Text, and Excel documents. However, there's a limitation: your data is accessible to the public and you're unable to store your projects on your local device. This makes it an excellent choice for those new to the field or passionate about it, but it falls short for projects that handle confidential information.
Microsoft's widespread presence extends beyond just spreadsheets, emails, and word documents. They excel in data visualization as well. The complete Microsoft Power BI platform encompasses every stage of data analysis, with a premium version including all its capabilities. However, they also provide a basic, free version that grants you access to some impressive data visualizations.
Additionally, Power BI is an excellent tool for learning about various types of charts and graphs through its straightforward drag-and-drop interface. It supports over two dozen data visualization formats, from heatmaps to scatter plots and tables.
The free version of Power BI allows you to link to more than 70 data sources, share on the web, and convert data to PowerPoint, Excel, and CSV formats. Given that many companies cover the cost of the enterprise version, building a few basic data dashboards could be an effective way to showcase your proficiency with the software.
Google Data Studio offers more than just the ability to create visualizations; it also provides a comprehensive suite of data analytics features for exploration. However, its primary focus is on its visualizations, which are straightforward to set up and, as expected from a leading company like Google, support connections to a broad array of data sources, including Google Sheets and CSV files.
Moreover, Google Data Studio is equipped to handle real-time data feeds from various sources, including MySQL, YouTube, and social media outlets like Twitter and Facebook. This allows data visualization to put the data into the correct context and to draw attention to the trends.
Visualizing the data setting is difficult as it tries to convey an understanding of in which settings that data is collected.
Mainly aimed at users without technical backgrounds or design skills, like marketers, journalists, teachers, and professionals in the business world, Infogram specializes in quickly sharing data on the internet. Just a heads-up: it's mainly a subscription-based tool, but we've included it on our list because it offers a free trial version that lets you try out its capabilities.
With an easy-to-use drag-and-drop feature, eye-catching animations, and engaging charts and maps, the free version boasts over 35 interactive chart types and lets you create up to 10 projects to share as online applications for free.
Although it occupies a place among tools that focuses on generating graphical charts, Infogram is geared more towards creating infographics, Facebook posts, slides, posters, and other kinds of visuals.
Similar to Tableau Public, you can't share these privately, but it's an excellent tool for experimenting with different methods of producing high-quality visuals, especially if you're more interested in the creative side of graphic design rather than diving deep into the technical details of statistical analysis.
Building upon the matplotlib Python framework, Seaborn offers a user-friendly approach to crafting visually appealing yet informative static charts. Tools like Infogram, which are designed to be code-free and accessible to those without programming skills, are excellent for basic tasks.
On the other hand, Seaborn is available under the open-source license, allowing users to delve into its core code and achieve unparalleled statistical charting. Seaborn is committed to maintaining the accuracy and reliability of the statistical models it utilizes.
This commitment ensures that it can handle a wide variety of statistical data and display the results of numerous data analysis methods, from simple linear regression to complex time-series studies, among others. For those who are truly dedicated to creating visually stunning charts that also serve as effective data analysis tools, Seaborn is the go-to choice.
Plotly, another tool for visualizing data in Python, shares many similarities with Seaborn. However, what distinguishes Plotly from Seaborn is its ability to produce interactive graphics, unlike Seaborn's static visuals. This feature, which allows for actions like hovering over data points or zooming in on charts, is beneficial for various purposes, such as identifying outliers in scatterplots.
With its wide range of customization options, Plotly has become a favorite among data scientists. However, the complexity of its customization can be seen as a downside. Mastering the intricacies of tasks like labeling and text positioning, which necessitate manual coding, can be challenging. Although Plotly might not be the best choice for beginners, for those looking to enhance their foundational Python skills, it's a tool worth getting to know in the realm of data visualization.
JupyteR is a highly-rated online tool for data visualization, allowing users to generate and distribute files with charts, formulas, stories, and active code. It's perfect for tasks like data cleaning and transformation, statistical analysis, numerical modeling, interactive computing, and artificial intelligence.
Google Charts stands out as a leading force in the field of data visualization, utilizing SVG and HTML5 technologies. It is renowned for its ability to create visual representations of data through graphs and images. With its zoom feature, Google Charts delivers an unparalleled experience across various platforms, including iOS, Android, and even the older versions of the Internet Explorer web browser.
Visual.ly stands out among data visualization platforms due to its extensive network for showcasing project results. It uses a specialized team of creatives for its data visualization offerings, making the data import and outsourcing process more efficient, including working with external vendors.
RAW, also recognized as RawGraphs, deals with structured data like TSV or CSV files. It acts as a bridge connecting data visualization with spreadsheets. Offering both unique and standard designs, RawGraphs ensures strong data protection despite being an application accessible over the internet.
These top 10 free tools for data visualization offer a range of functionalities, from simple chart creation to complex interactive visualizations, and cater to users with varying levels of technical expertise.
Tableau is one of the most widely used tools for data visualization. It offers robust features for creating interactive and shareable dashboards, enabling users to visually analyze their data. Its user-friendly interface and ability to handle large datasets make it a popular choice among professionals.
No, SQL is not a visualization tool. SQL (Structured Query Language) is used for managing and querying relational databases. While it excels at extracting and manipulating data, it does not provide data visualization capabilities. However, SQL can be used alongside visualization tools like Tableau or Power BI to display data insights.
The five steps in data visualization are: Data Collection, where relevant data is gathered from various sources; Data Cleaning, involving processing and cleaning the data to remove errors and inconsistencies; Data Analysis, which uncovers patterns and insights within the data; Visualization, where visual representations such as charts, graphs, and dashboards are created; and Interpretation, where the visualized data is interpreted to make informed decisions and share insights effectively.
Data visualization tools are software applications that help create graphical representations of data. These tools, such as Tableau, Power BI, and Google Data Studio, transform complex data sets into intuitive visual formats like charts, graphs, and dashboards, enabling easier analysis, interpretation, and communication of data insights.
The five data visualization techniques are bar charts, line graphs, pie charts, scatter plots, and heat maps. Bar charts compare quantities across categories, providing a clear visual representation of differences. Line graphs show trends over time, making it easy to observe changes and patterns. Pie charts display proportions within a whole, helping to visualize the relative sizes of parts to the entire dataset. Scatter plots illustrate relationships between variables, highlighting correlations and distributions.
