Let’s talk about result-oriented learning.
Simply put, it’s similar to what most of my fellow in college did while they “didn’t feel like learning”. Through this way of learning they are able to squeeze more time for more important things. Ironically this is probably the most efficient way to learn that I’ve seen.
In the university, what they would do is to:
- Confirm the valuation standard.
- Make special mark if there’s requirement on attendance or essay question, and mark them on calendar.
- Reverse calculate the minimum required efforts to achieve at least 60% in final evaluation.
Regardless of the motivation, their way of learning did require the least amount of time to achieve the (mostly identical) result - the requirements of graduation.
In order to learn more, what we can do is to maximize the efficiency. I’ll update more if I find better methods, but for now, I’ll try to follow these steps:
- Determine the expected result.
- Plainly saying “to learn” won’t give any instructional direction. Make it perfectly clear of to what extent you need to know about a certain subject.
- For instance, let’s say that you’re trying to learn about Machine Learning. Are you trying to get a grasp of the basic concept, or trying to implement a computer vision model into your product? Is there a clear standard of leveling in general?
- If there’s a well-recognized certification, should you consider of getting one, or at least follow the advised route of learning by that certification vendor? Sometimes it’s not the cert itself you want, it’s the path the learn something systematically that’s valuable.
- For instance, in project management, the PMP certification is well-recognized and highly-valued, and in order to even apply for it, you’ll need around 3500 hours of experience of leading a project, and then take a pre-course predetermined by the PMI. Let’s say that you want to learn project management, and this certification would be a very important milestone for you, at least in the early times of learning, so make efforts in getting this certification would be an excellent way to learn to be a better project manager.
- Mark the important milestones, or modernly, KPI of reaching your goal.
- Real-life learning is more complicated than take a 60 points in an exam, it involves collecting and consuming resources, making sacrifices over more “presently pleasing” actions and re-routing constantly.
- Mark the most important things can help to keep on the right track and maintain motivated.
- Let us say that I’m trying to prove to future employers that despite my liberal arts background, I’m fully capable of being a data scientist. When looking up online for the recruitment details of a data scientist, I noticed some repetitive wordings like “Statistics background”, “Python programming” and “distributed computing database system experience”. Normally one would need to graduate a university with a degree in math statistics, or quantitive economics in order to “fit” these requirements, but there are plenty ways to showcase one’s ability over certain area than degree.
- For statistics, there’s a Japan Statistical Society Certificate ins Japan, that tests you for your knowledge in statistics. For programming language like Python, instead of a certification (there aren’t really any good exam that can evaluate “programming skills” since most of it is purely way of thinking), a portfolio would be much appreciated. A simple GitHub account would do, or a dedicated website if what you do involves other products. Articles on Medium would help recruiter to identify your ability to articulate and the interests you hold as well.
- So on the road of being a data scientist, I would mark taking grade 1 of JSSC, attending Kaggle’s competitions and reach Master as
IMPORTANT.
- Break down your targets and apply step 2 again.
- “Becoming a data scientist” is almost as big as “earn lots of money”, it’s too abstract. Through step two, most of the KPI should be marked by now, but how to reach those KPIs in the first place?
- Breaking down the targets using MECE principle, and again, mark out the most important thing there is to achieve such KPI.
- The repetitive successes of tiny steps are what keeps us motivated towards, especially, the
HARD things.