A modern guide to getting started with Data Science and Python by Thomas Wiecki.
From the post:
Python has an extremely rich and healthy ecosystem of data science tools. Unfortunately, to outsiders this ecosystem can look like a jungle (cue snake joke). In this blog post I will provide a step-by-step guide to venturing into this PyData jungle.
What’s wrong with the many lists of PyData packages out there already you might ask? I think that providing too many options can easily overwhelm someone who is just getting started. So instead, I will keep a very narrow scope and focus on the 10% of tools that allow you to do 90% of the work. After you mastered these essentials you can browse the long lists of PyData packages to decide which to try next.
The upside is that the few tools I will introduce already allow you to do most things a data scientist does in his day-to-day (i.e. data i/o, data munging, and data analysis).
A great “start small” post on Python.
Very appropriate considering that over sixty percent (60%) of software skill job postings mention Python. Popular Software Skills in Data Science Job postings. If you have a good set of basic tools, you can add specialized ones later.