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Making Data Useful
Becoming a “real” data analyst
10 differences between amateurs and professional analysts
Previously, I introduced you to a few analytics tasks disguised as everyday activities to prove that you’re already a data analyst. For example, consider the image below. Digital photos are stored as a bunch of numbers (left) that make no sense to your brain until you open them with suitable tools (right).
Ta-da! You’ve just done data visualization. The music swells as you discover that the power of data analysis was inside you all along.
But does this mean you’re ready to work as a professional analyst?
Not quite. There are some big differences between an amateur and a professional analyst.
Data pro vs amateur difference #1 — Software skills
Unlike most amateurs, the pro knows how to use software (e.g. Python and R) that allows them to interact with more data formats all in one place. While MS Paint only works for images, analytics software can handle images and tables and sounds and text and and and… and the kitchen sink.
Here’s what it looks like when you open that same image with Python:
