The premise for picking up the language is a melting pot of tangential reasoning, including having a shiny new Macbook Pro to fiddle with (yes, old reliable is gone), and that Coursera was conveniently offering a beginner’s class in the language. We can feasibly toss in that R has physical memory limitations when it comes to dataset size – not that yours truly will ever hit that ceiling, but this IS the excuses part of the story after all – and that there are plenty of data analysis tools available for Python, without such constraints. Finally, the fish barely bite in the winter (plus I need new waders), and every time I think about hitting a bucket of balls it starts snowing. I reiterate – plenty of excuses.
Inside of two weeks I was learning a bit of Python, but seeing as daylight savings time is still a dream it was easy to get ahead of the pre-defined pace of Coursera’s Programming for Everyone. So I started reading the related textbook Python for Informatics (PDF download) and doing the exercises within, figuring I could just fill in the blanks as the assignments came due. High quality, engaging material.
Passed chapter ten, where the related course actually ends, but when I attempted to use Python to
model all protein-coded genes in the human body against cancer incidents replicate a historical trust transactions analysis I’d previous done in R, failing at the twelfth … sixth … second if/elif loop, I realized I still knew jack squat about Python. Almost dumb.
Long story [cut] short, Codecademy wound up the perfect solution for getting the skills tuned towards par while impatience loomed mightily nearby. Knocked off their entire Python segment in just a few evenings and found it well worth the time. If a blitzkrieg pace can be considered time.