Data Science

• Chartbuilder

Generate polished, static charts. Using the tool is straightforward!

• Excel

  Excel is still a commonly used platform for data visualization, especially for those in business, marketing, economics, and finance. Duke University Libraries provides an introduction to the visualization capabilities of Excel.

• RAW Graphs

  RAW Graphs is a free and open source tool for data visualization. an online platform to make data visualizations. Users can select graph type (i.e., scatterplot, bar chart, dendrogram, etc.) based on type of input data (i.e., numeric, categorical).

• Datawrapper

  Datawrapper is a free online platform to create PNG charts, maps, and tables with no coding required. Users include New York Times, Thomson Reuters, Moody's, Spiegel, and World Economic Forum.

• plotly

  Plotly is an entirely web-based interface for making graphics. It does not require any coding knowledge, but can interface with both R and Python. The community version of plotly is free to use.

• Tableau

Tableau works with numeric and categorical data to produce advanced graphics. Browse the Tableau public gallery to see examples of visuals and dashboards.

• Gephi

  Gephi is a free software for visualizing networks, comprised of "nodes" and "edges". "Like Photoshop but for graph data, the user interacts with the representation, manipulate the structures, shapes and colors to reveal hidden patterns." 

• Voyant

  Voyant Tools is a web-based text reading and analysis environment. It is a scholarly project that is designed to facilitate reading and interpretive practices for digital humanities students and scholars as well as for the general public.

• Corpus-specific tools

Certain corpora have built-in visualization tools, such as Google Books ngram viewer, HathiTrust Bookworm, or JSTOR for Research.

• Canva

  Canva is a an online graphic design platform. There are a variety of templates users can start from, including for infographics.

• R 
  R is not only a standard statistical analysis tool, but also a powerful visualization platform. The ggplot2 package is the primary graphic-making package. There are also numerous packages meant to extend the functionality of ggplot2. From animations to maps to other advanced graphic options (check out shiny to make interactive plots!), these extension packages help make publication-worthy graphs. For those working with text data, the tidytext and tm packages are good options for cleaning, analyzing, and visualizing text data.
   
• Python 
  Like R, Python has libraries to make impressive visualizations. While matplotlib is the main graphics library, there are additional Python libraries focused on visualization, including making interactive plots/charts, 3D images, maps, and more. (Read here for a more in-depth discussion of how the Python visualization libraries fit together.) When working with text data, the nltk and TextBlob libraries are useful for analysis and visualization.

One of my favorite websites, Information is Beautiful, takes data and information and turns them into infographics and beautiful data-visuals.