Data Science
A Framework For Learning New Data Science Concepts Quickly
Time is limited. Potential knowledge is infinite. Here’s how I learn new models and concepts
Success in data science and software engineering depends on our ability to continuously learn new models and concepts.
Both domains are infinitely large umbrellas of nested ideas.
While you can spend decades in a specific branch of NLP, many of use are generalists and need to constantly acquire new knowledge.
Here’s my framework for doing this quickly.
Do not start with math or scientific papers
Math is overrated.
It’s useful for 2 things:
- It gives an intuition for how things work.
- It allows coding algorithms without using an existing package
Anecdotally:
The former is useful when tuning models.
The later is useful when I need an algorithm in a Ruby but packages supporting it only exist in Python.
Math is never the first step.
Watch a youtube video to grasp it at a high level
Watch a couple videos. You can find them covering most concepts.
Don’t try to understand specific technicalities at this point. Just develop a high level idea of what it does and how it’s different from related concepts.
Videos are great because images cut through the technical jargon that often makes written explanations seem more complicated than reality.
Get to the point where you can describe the concept in a few sentences. At this point, it’s useful imagining that you’re explaining it to a non-technical colleague.
Get some code working
Find a code snippet online.
You can often google “concept” + “python tutorial” to find code. Otherwise, find a related library and review the docs, or search related tags on Stack Overflow.
