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The best subject to teach is a subject that you apply yourself daily yourself.
And the best thing to learn is something that you’ll actually use daily.
My mix of data tools has shifted substantially from Excel to R and Python as of late. I still use Excel, but not for several hours every day, like I did before. Instead, I spend nearly as much time learning Python.
When I started with Python, I panicked. It’s the first non statistically-focused program I have used. The whole Python universe confused me so much to the point where I did not even know how to install and run it on my machine!
Today I am finding Python an essential tool for my research goals, and one that combines nicely with Excel. This is a great blog topic — it’s something a savvy analyst should know about.
This post will get you started, by installing Python on your machine.
I suggest that you download the free version of Anaconda from Continuum Analytics here.
Pick your operating system and the latest version of Python and you’re ready!
What’s next? Where *is* Python?
When I was starting out with Python I was so disoriented at the number of applications used to actually run it.
I’m used to a single-source place to work through my code. Something like the VBA editor in Excel or RStudio. Where is my RStudio for Python?
Turns out what I wanted is called an integrated development environment, or IDE. Let’s just say it’s a one-stop-shop for your coding.
I like the PyCharm IDE. Download the community version for free. Some advanced programmers find an IDE limited, but I think it is a great tool for a “citizen coder.”
Looking forward to covering Python on the blog. Questions, concerns, suggestions? Leave ’em below…
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