I noticed when I was in college that, although I was a Philosophy major, any of the other pre-requisite classes I was required to take (California History, Psychology, Anthropology) all seemed to overlap with each other. There was always a core theme behind the education of one subject that philosophically aligned with that of another. Whenever I saw this overlap, I was able to piggyback off of the information I acquired from one to benefit the other. It was like I was studying twice as much.
Now that I've graduated college and have spent several years in the workforce, I find it hard to draw these overlaps. More importantly, it was hard for me to define just what subject I was actually learning.
Sure, as a Product Manager I can say "I'm doing PM things, we focus on a bit of data, a bit of user experience design, and a bit of coding," but this was vague and not precise. Anytime I pursued a new vertical, I would ask myself two questions: âWhat does this remind me of?â and "Why does it remind me of it?â This was crucial for me to ensure that I understand what it was I was getting myself involved in. Essentially, I reverse engineered what I wanted to learn and broke it down to its First Principles. In the reverse engineering process, you maintain a high-altitude view of the task at hand and break it down much easier. The examples I'll give involve my supreme path to learn machine learning.
Anytime I pursued a new vertical, I would ask myself two questions: âWhat does this remind me of?â and âWhy does it remind me of it?â
I use Asana's List board to create my learning principles, i kept it high level at "Data Science." You can see that I broke down all the core components of data science so that I know how to tackle this subject (what resources do I need, are there any prerequisites, etc).
We can then click into one of the principles, which is Bayesian Inference. In the description section I write in as much content as I need for Bayesian Inferences and if I can break this down any further, then I leverage the sub-tasks to list out its sub-components, in this case: Bayesian Optimal Error.
From here I can, just like the last learning principle, write in as much content as I need to about the subject. Taking notes is vital and I prefer using it here in Asana rather than Evernote. Controversial, I know, but if you notice on the bottom of the screen I can invite users to collaborate on these boards with me. Friends can contribute to my boards with notes which makes the acquisition of information even faster; this is totally optional.
Now that I can optimize my learning by identifying the learning principle and seeing how many ways it overlaps with other subjects, I can leverage the Pomodoro technique. To focus on one thing at a time in 30-minute, distraction-free increments, I'm able to engage in deep, meaningful work.
Now that I know I'm learning Bayesian Inferences (and maybe in that Pomodoro session I'm focusing on Bayesian Optimal Errors), I'm tasked with finding where else in my learning plan I can use this. One of the subjects aside from Data Science that I'm learning, is Product Management. In there, we sift through a lot of analytics and engage in the exploratory data analysis process. I can link the two subjects and while I'm learning Bayesian Optimal Errors in Data Science through the Python Programming Language, I can relate that to my actual work with real-world examples and use those learnings to engage in that learning principle from the Product Management perspective.