What is Data Visualization?

The data explosion in recent years has led to a corresponding need for businesses and organizations to communicate information in a way that’s accessible and engaging. Visual content is becoming an increasingly popular means for sharing data.

Data visualization relies on visual representations like graphs or charts to convey raw data. Presenting data in a visual manner makes it easier to understand and faster to process, even for those who aren’t mathematically inclined or trained in analytics. Each visual data representation tells a story about the data, which can lead to more informed business decisions and favorable outcomes.

There are many different kinds of data visualizations, such as maps, histograms, scatter plots, and pie charts. Those who know how to present information in visually engaging stories have the power to help make sense of past events, provide insights on current trends, and offer predictions for the future. This article will explore several of the most popular libraries for data visualization in JavaScript, Python, and R.

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What is a Data Visualization Library?

Data visualization libraries are designed to help users understand complicated ideas and create visualizations that depict this information. Choosing a data visualization library plays an important role when working with large or complicated datasets, as it can affect the kinds of insights taken from the data. There are many options to choose from, so it’s important to learn about the specific features of each to decide which library is best for your data visualization needs.

JavaScript Data Visualization Libraries

JavaScript is one of the most popular programming languages, and has a variety of libraries that can be used for data visualization:

  • Highcharts: Because it is completely based on native browser technologies, Highcharts doesn’t need client-side plugins such as Flash. It performs well in all modern browsers, even mobile devices.
  • Toast UI Chart: This statistical data visualization library offers users an identical look in all browsers. It performs quickly and is easy to use, and includes options to customize themes.
  • D3.js: This flexible library uses CSS, SVG, and HTML to bring data to life. Its fast performance allows it to support large datasets as well as interactive or animated elements. D3.js has a clear API reference and comes bundled with several community-supported plugins.
  • Recharts: This compostable charting library allows users to customize charts and add effective interactions to various chart components. Its API is easy to use and can support multiple types of shapes, charts, and components.
  • Chart.js: This open-source library is user-friendly and can be easily customized based on specific visualization needs. It has over eight chart types, including built-in charts.

Python Data Visualization Libraries

Python is a high-level, object-oriented programming language that’s popular among those working with data. It has a variety of libraries that can be used for data visualization:

  • NumPy: Numerical Python, or NumPy, is Python’s basic numerical computation package. It has a general-purpose package and an object-oriented approach. NumPy’s extensive collection of high-level mathematical functions support its multi-dimensional arrays and is commonly used for data analytic purposes.
  • Pandas: Python Data Analysis, or Pandas, is commonly used in data science, but also has applications for data analytics, wrangling, and cleaning. Pandas offers eloquent syntax, as well as high-level data structures and tools for manipulation.
  • Matplotlib: Python’s first data visualization library. It is still considered to be the most popular and widely used data visualization library. Matplotlib can create a variety of graphs, such as line graphs, scatter graphs, hist graphs, and interactive 2D graphs.
  • SciPy: Short for Scientific Python, SciPy is an open-source, free Python library that is commonly used for high-level computations. Its high-level commands aid with manipulating and visualizing data.
  • Keras: This library is popular for deep learning and neural network models. It offers prelabeled datasets that can be imported directly, as well as a variety of implemented parameters and layers that enable users to configure, construct, and train neural networks.
  • Scikit-learn: This comprehensive machine learning library has applications for regression, clustering, classification, model selection, and dimensionality reduction, among others.

R Data Visualization Libraries

R is a popular programming language for graphics and statistical computing. Its data visualization libraries are particularly useful for constructing graphs, plots, and charts:

  • Ggplot2: This library allows users to construct graphs from the same components in a dataset or a coordinate system. It offers a graphics language that can be used to design intricate and intuitive plots, as well as graphs that represent univariate and multivariate data.
  • Lattice: This powerful system can be used to easily create a variety of small plots. This high-level data visualization package improves on core R graphics by offering improved defaults and the capacity to display multivariate relationships. Lattice can create trellis graphs that depict one variable or the relationship between variables.
  • Plotly: This open-source package was designed to help users create interactive, professional graphs, such as area charts, error bars, scatter plots, and line plots.
  • Ggvis: This data visualization package allows users to easily design engaging and interactive graphics that can be used for exploratory data analysis.

Hands-On Data Analytics & Data Visualization Classes

Are you interested in transforming raw data into stunning data visualizations? If so, you may want to consider enrolling in one of Noble Desktop’s data analytics classes. Courses are offered in New York City, as well as in the live online format in topics like Excel and data analytics. These unique learning experiences provide participants with relevant and timely training on some of the most popular data visualization libraries, such as Pandas, NumPy, and Matplotlib, among others.

In addition, more than 100 live online data analytics courses are also available from top providers. Topics offered include FinTech, Excel for Business, and Tableau. Courses range from three hours to six months and cost from $219 to $27,500.

Those who are committed to learning in an intensive educational environment can enroll in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely, small-class instruction. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python.

For those searching for a data visualization class nearby, Noble’s Data Visualization Classes Near Me tool makes it easy to locate and learn more about over 200 courses currently offered in the in-person and live online formats. Class lengths vary from three hours to ten weeks and cost from $119 to $12,995.