A web-based tool to find darker and lighter variations of colors, in order to make coherent palettes and color schemes. Designed to generate colors that are safe for web use.

A web-based tool for generating palettes of colors. Options include the ability to select sequential, diverging, or discrete palettes, as well as number of colors, and hue families. Also allows selection of palettes that are color-blind compatible, photocopier safe, etc.

This website allows you to select color swatches into a group (or enter custom RGB values) and see how they appear next to one another. You can also simulate how the selected colors are perceived with eight types of color vision deficiency, assuming that you yourself have typical color vision.

D3 is a JavaScript library developed by Michael Bostock. It allows you to bind arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document. D3 is based on ProtoVis, a graphics toolkit developed by Michael Bostock and Jeff Heer that uses JavaScript and SVG for web-native visualizations. ProtoVis is no longer under active development as of September, 2010. Available under the BSD License. Free to use.

A suite of programs written in Java and developed by the Databionics Research Group at the University of Marburg, Germany, to perform data mining tasks like clustering, visualization, and classification. Available under the GNU General Public License (GPL).

A web app for drawing Sankey diagrams and modeling relationships among categorical data. Free to use.

Interactive graphical software for exploring high-dimensional data. It provides dynamic graphic “tours” of the data, as well as scatterplots, barcharts and parallel coordinates plots. Formerly XGobi. Available under the GNU General Public License (GPL).

A plotting toolkit for the R statistics and analysis language. It is based on the grammar of graphics. Available under the GNU General Public License (GPL).

A portable, command-line driven graphing utility that can draw using lines, points, boxes, contours, vector fields, surfaces, and various associated text to plot functions and data points in 2D and 3D plots. Free to use.

Useful for representing structural information as diagrams of abstract graphs and networks, with six different graph formats and many options for colors, fonts, line styles, hyperlinks, and custom shapes. Available under the Eclipse Public License (EPL).

An open source JavaScript toolkit developed by Nicolas Garcia Belmonte. The website includes a gallery with dozens of examples and the code behind them.

A suite of graphical tools designed for exploring raw data and studying multivariate features, produced by the RoSuDa group at the University of Augsburg. Specializes in datasets with missing values. Free to use.

A website that lets you visualize data and explore galleries of other people’s visualizations to comment, explore, and share. An experimental project from IBM Research and IBM Cognos software group.

A general-purpose statistical visualization system written in Java and also produced by the RoSuDa group at the University of Augsburg. Particularly useful for working with categorical data, geographical data, and big datasets. Available under the GNU General Public License (GPL).

The best diagramming tool on MacOS. Easy enough to get started on quickly, with plenty of control available. OmniGraffle also incorporates the GraphViz engine and can import and lay out DOT formatted data files. Many templates and shape stencils available at http://graffletopia.com/. Commercial. MacOS only.

A quick, lightweight, inexpensive tool for sketching graphs. No need to manually draw axes or enter data (though you can)—just start sketching the graph you want. Excellent for ideation and prototyping. Commercial. MacOS only.

An open source tool suite based on IBM’s Visualization Data Explorer, it uses a GUI based on X windows and Motif. It can handle overlapping grids, data with non-uniform step sizes, and missing data. Tools include cutting planes, vector line traces, volume rendering, and isosurface/isocontour tools.

An exploration tool for categorical data written in Java and developed by Robert Kosara and Caroline Ziemkiewicz. Available under the New BSD License.

A programming language and development environment initially created by Ben Fry and Casey Reas to serve as a software sketchbook and therefore specifically for working with graphics. It has matured into a powerful tool for creating all kinds of professional visual images. Available under the GNU Lesser GPL.

A scientific data visualization package based on Python and C, using the GTK+ and GtkExtra libraries. Available under the GNU General Public Licence (GPL).

User-friendly tools that let you drag-and-drop to visualize data and create interactive dashboards. Great for exploring (just please avoid the temptation to just shove it into all your presentations—like all these tools, it is no replacement for thoughtful, custom explanatory design). Commercial with a public version. Windows only.

This tool for creating word clouds with adjustable fonts, colors, and layouts was developed by Jonathan Feinberg while at IBM. Great for exploring text-based data (like the Daily Congressional Record or any large corpus), but won’t tell the whole story for you. Free to use.

An open source C++ toolkit that supports automated wrapping into Python, Java, and Tcl, developed by Will Schroeder, Ken Martin, and Bill Lorensen. Specializes in 3D computer graphics, modeling, volume rendering, and scientific visualization. Available under the Creative Commons Attribution-NoDerivs 3.0 Unported License.

Semiology of Graphics: Diagrams, Networks, Maps. ERSI Press: 2010. (Gauthier-Villars: First Edition, 1967—in French.) This foundational classic on the theory of visual communication has recently been re-released, and we recommend you take advantage of its availability if you’d like to get serious about developing solid design chops.

Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann: 1999. The first chapter is recommended. Newer research has supplanted various later portions of the book.

Thinking Visually: Business Applications of 14 Core Diagrams. Thomson Learning: 2000. This book is exactly what the title says it is; its scope is limited to diagrams in a business setting. But if that describes what you work with, this is a helpful resource for thinking about context and application.

Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press: 2004. A beginner-friendly introduction to business presentations and other numerical designs for explanatory visualization.

Information Dashboard Design: The Effective Visual Communication of Data. O’Reilly Media: 2006. This book has strong focus on communicating efficiently and minimizing visual noise. A must-have if you’re designing for a Business Intelligence context.

Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press: 2009. The companion volume to Show Me the Numbers, this book focuses on exploratory visualization.

Information Graphics: A Comprehensive Illustrated Reference. Oxford University Press: 2000. A remarkably complete catalog of graph and visualization types. Excellent as a source book for ideas.

How to Lie with Statistics. WW Norton & Company: 1993. Concise, funny, useful, classic.

Graph Design for the Eye and Mind. Oxford University Press: 2006. Excellent and concise book on best practices and cognitive principles for many standard graph types and implementations.

The Practical Guide to Information Design. Wiley: 2007. A wonderfully rich yet simple-to-read text encompassing all kinds of visual properties and how to use them well.

Visual Language for Designers. Rockport: 2011. A good source for learning about more aspects of graphic design as applied to all kinds of informational visualizations. This book will help take your presentations to the next level.

The Design of Everyday Things. Basic Books: 2002. A classic, down-to-earth look at practical design for things used by human beings: it takes into account the psychological side of our interaction with everyday objects. Required reading for anyone designing artifacts for other humans to use.

Getting Started with Processing. O’Reilly Media: 2010. A concise introduction to a programming language specifically created to make visual design simple, written by the language’s creators. Appropriate even for those with little programming experience.

Beautiful Visualization. O’Reilly Media: 2010. A collection of case studies from practitioners working on all kinds of visualization projects. A great (yes, we’re biased, but with much gratitude to the wonderful contributors) behind-the-scenes look at decisions and trade-offs made, and the resulting displays.

Designing Interfaces: Patterns for Effective Interaction Design. O’Reilly Media: Second Edition, 2010. This book focuses on user interface (UI) design, but contains a lot of valuable insight into things like color, visual hierarchies, and alignment. Especially useful if you plan to design interactive visualizations.

The Visual Display of Quantitative Information. Graphics Press: Second Edition, 2001. (First Edition, 1983.) Another seminal classic that is as much a design artifact itself as an instructional volume. While many kinds of quantitative data visualizations are treated, there is a focus on statistical graphics.

Envisioning Information. Graphics Press: 1990. Widely regarded as Tufte’s best work, this book rewards those who invest time in reading and understanding it, not just flipping through it casually.

Visual Explanations: Images and Quantities, Evidence and Narrative. Graphics Press: 1997. This volume focuses on dynamic data, and contains some compelling and memorable examples, such as the chapter on the Challenger disaster.

Beautiful Evidence. Graphics Press: 2006. It’s not necessary to own all four Tufte volumes; much of the key information is repeated. But this one is notable for introducing sparklines.

Information Visualization: Perception for Design. Morgan Kaufmann: Second Edition, 2004. A definitive reference for the understanding of vision, perception, and related cognition. This is the book many visualization professionals turn to when we need to answer fundamental questions of perception.

Visual Thinking for Design. Morgan Kaufmann: 2008. A more concise treatment of visual processing and perception. Not nearly as comprehensive as Information Visualization.

Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley: 2011. A beginner-friendly book that walks you through both design theory and implementation. It includes ample illustrations and code tutorials that you can reuse and riff on.