DataPad

DataPad

DataPad is a data analysis tool for researchers to easily visualize, explore, analyze and publish datasets. Its intuitive drag-and-drop interface allows users to quickly generate charts, maps, tables and dashboards.
DataPad image
data-visualization analysis dashboards research

DataPad: Intuitive Data Analysis Tool

DataPad is a data analysis tool for researchers to easily visualize, explore, analyze and publish datasets. Its intuitive drag-and-drop interface allows users to quickly generate charts, maps, tables and dashboards.

What is DataPad?

DataPad is a user-friendly data analysis and visualization software designed for researchers of all levels. With its intuitive drag-and-drop interface, DataPad allows users to easily import, clean, analyze and visualize complex datasets without coding.

Key features include:

  • Import data from CSV, Excel, databases and other sources
  • Interactive drag-and-drop workflow to clean, analyze and visualize data
  • Library of 30+ chart types including maps, histograms, heatmaps, scatter plots
  • Collaboration tools to share analyses and data with team members
  • Auto-generate interactive dashboards and reports
  • One-click export and publishing of charts, dashboards and underlying data

Whether working with survey data, financial data, network data or any other dataset, DataPad provides researchers powerful self-serve analytics to gain rapid insights. Its ease-of-use and versatility makes DataPad suitable for users across industries like academia, marketing, business intelligence, public policy and more.

DataPad Features

Features

  1. Drag-and-drop interface
  2. Visualize data
  3. Explore datasets
  4. Analyze data
  5. Generate charts, maps, tables
  6. Create dashboards
  7. Publish and share

Pricing

  • Freemium

Pros

Intuitive and easy to use

Great for visualizing and exploring data

Lots of customization options

Can handle large datasets

Good for non-programmers

Cons

Steep learning curve

Limited advanced analytical capabilities

Can be slow with extremely large datasets


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