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10 Visualizations Every Data Scientist Should Know

This article is by Jorge Castañón, Ph.D., Senior Data Scientist at the IBM Machine Learning Hub.

Data visualization plays two key roles:

1. Communicating results clearly to a general audience.

2. Organizing a view of data that suggests a new hypothesis or a next step in a project.

It’s no surprise that most people prefer visuals to large tables of numbers. That’s why clearly labeled plots with meaningful interpretation always make it to the front of academic papers.

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This post looks at the 10 visualizations you can bring to bear on your data — whether you want to convince the wider world of your theories or crack open your own project and take the next step:

  1. Histograms
  2. Bar/Pie charts
  3. Scatter/Line plots
  4. Time series
  5. Relationship maps
  6. Heat maps
  7. Geo Maps
  8. 3-D Plots
  9. Higher-Dimensional Plots
  10. Word clouds

Read the full article, with descriptions and illustrations for these visualizations, here.

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