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Course & Subject Guides

Text Mining & Analysis @ Pitt

An introduction to text mining/analysis and resources for finding text data, preparing text data for analysis, methods and tools for analyzing text data, and further readings regarding text mining and its various methods.


Visualizations provide an accesible way to analyze our data. By generating a visual representation of data, they can help researchers find patterns, and outliers between concepts, themes, and more.  Examples of visualizations include word clouds, graphs, charts, and maps.




  • Tableau
    For creating interactive data visualizations for the web

  • RAWGraphs
    For creating custom visualizations as vector-based (SVG) and raster (PNG) images

  • Piktochart
    For creating professional-looking, data-driven infographics, presentations, and reports

    For producing dynamic, interactive data visualizations in web browsers

  • Gephi
    For network analysis and visualization

  • Google Charts
    For creating interactive charts and data tools



  • Matplotlib
    For creating static, animated, and interactive visualizations

  • Seaborn
    For drawing attractive and informative statistical graphics

  • ggplot
    For creating plot-based data visualizations incrementally and simply;  based on the 'Grammar of Graphics

  • Bokeh
    For creating interactive visualizations for modern web browsers (powered by JavaScript), from simple plots to complex dashboards with streaming datasets

  • Pygal
    For creating interactive and highly customizable graphs and charts that can be output as an SVG (Scalable Vector Graphics)

  • Plotly
    For creating interactive, publication-quality graphs and charts

  • geoplotlib
    For visualizing geographical data and making maps

  • Gleam
    For building interactive web visualizations of data

  • missingno
    For creating missing data visualizations and utilities to get quick visual summaries of the completeness (or lack thereof) of a dataset

  • Leather
    For creating basic charts as SVGs


  • ggplot2
    For 'declaratively' creating graphics, based on "The Grammar of Graphics"; you provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details

  • Lattice
    For visualizing multi-variate data; inspired by Trellis graphics

  • highcharter
    For creating dynamic charts and schematic maps with built-in and easy-to-customize themes 

  • Leaflet
    For building interactive maps

  • RColorBrewer
    For manipulating colors in plots, graphs, and maps color schemes

  • Plotly
    For making interactive plots

  • RGL
    For producing interactive 3-D plots

  • dygraphs
    For charting time-series data

  • plotrix
    For plotting data; includes various labeling, axis and color scaling functions

  • vcd
    For plotting categorical data


  • Weka
    For data preprocessing (e.g., stemming, data resampling, transformation), classification, regression, clustering, latent semantic analysis (LSA, LSI), association rules, visualization, filtering, and anonymization


Helpful Resources