Data visualization

Resources and links:


Software tools for data visualization and data processing:

  • Microsoft Excel – The classic spreadsheet software, sufficient for uncomplicated data manipulation and storage, basic plots and curve-fitting. Google Docs offers many of the same features.
  • Origin – Powerful plotting software with a graphical user interface, used by many without a penchant for programming. Your lab may have a license.
  • MATLAB – The most popular computer algebra system, with a built-in plotting capabilities. As with most of the tools below, MATLAB can allow a user to very easily generate plots in batch, and regenerate them after tweaking, a workflow that is cumbersome in GUI-based tools.
  • GNU Octave – Together with gnuplot, Octave has many of the same capabilities as MATLAB with compatible syntax.
  • gnuplot * [matplotlib](https://matplotlib.org/) – Python has emerged as a competitor to MATLAB, offering all the features of a general purpose programming language and a very large community. Matplotlib is the most commonly used and supported module in Python for data visualization.
  • seaborn – A Python module, built on top of matplotlib, designed especially to plot statistical data.
  • pgfplots – If you are a LaTeX power user, give this package a try. It can be used in conjunction with TikZ to achieve very precisely specified generative plots.
  • Mayavi – A Python module for visualizing 3D data.


General purpose drawing tools:

  • Adobe Illustrator – Illustrator is well known among digital artists and designers, but is often used in the scientific community to make figures. Illustrator may be used to post-process or add illustrations to plots made in other software.
  • Inkscape – A vector-graphics editor, similar to Illustrator, available for free under an open-source license.
  • Biorender - Premade icons and templates for depicting science in presentations.


Color selection:


Books:

  • "The Visual Display of Quantitative Information" by Edward Tufte
  • "Visualize This: The FlowingData Guide to Design, Visualization, and Statistics" by Nathan Yau

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