Free checklist: 30 days to Python programmer

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Python has become in many ways the lingua franca of computing, with the open source language being used in everything from web development to artificial intelligence to… why, yes… data analytics.

If you’re a heavy Excel or Power BI user, Python is a great tool to add to your toolkit — it’s officially supported in Power BI now and an official integration with Excel is coming (although there are already cool ways to use Python with Excel.).

The interoperability is to the point where it’s about learning Python instead of Power BI, Excel, etc. They are meant to be used together.

Moreover, data analysts already know a fair amount about computing. It’s a lot easier to learn Python with this skillset than for the average person off the street. With that lessened learning curve, it’s easy to reap big rewards as an analyst with relatively modest Python skills. In fact, Burning Glass found that jobs requiring coding skills pay $22,000 per year more than those that don’t.

You may not get that kind of bump from this checklist… but it’s a start!

Sign up below for the checklist and access to my resource library: 👇

If you’re already subscribed, you’ll find this resource in the learning-guides-and-checklists folder of the library.

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With this checklist, you’ll read, watch and download your way through some of the top Python resources on the web. In particular, it’s designed for the aforementioned data analysts looking to add Python to their toolkit (if that’s you, I hope I convinced you earlier!).

With this checklist you’ll learn how to:

  • Download and launch Python from your computer
  • Work with functions and objects
  • Perform basic arithmetic and conditional logic
  • Create, manipulate and analyze core data structures
  • Locate essential packages for data analytics

Get the checklist here 👇

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If you’re looking to continue learning Python for analytics, be sure to check out my book Advancing into Analytics.

What are you hoping to do with Python? What questions do you have? Favorite resources to share? Drop ’em in the comments.

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