How to gain expert intuition from self learning and why it impacts employability

When I was in university, I had a peculiar observation when I reached 3rd (junior) year. The courses abruptly became very difficult, but were much more interesting, to a point where I felt like the introductory/100 level courses bore little resemblance to what actual applications in the field were like.

This phenomenon continued after formal education. To use data science as an example: there are plenty of data science courses online, at the introductory/100 level, but the most I learned was from applying what I learned to projects, either at work or self-directed, and discussing research papers with the folks at Aggregate Intellect (ai.science).

I’ve questioned this when I was trying to recommend online courses to aspiring data scientists. Shouldn’t it be the case that taking more Coursera/Udacity/Udemy courses, is always helpful (positive)? But in reality I’ve found that past a certain point, it “doesn’t hurt” (neither positive nor negative) or at the extreme, is downright detrimental (negative).

The same logic holds for self learning anything: be it musical instruments, public speaking, or programming. How one chooses self learning materials can make the difference between gaining mastery (and employability), or simply going in circles.

In this article I will walk through how to focus on gaining mastery while self learning, and how “expert intuition” makes one employable as a by-product. I’ll also show how to break out of going in circles while self learning, and reach “expert intuition”.

Expert intuition should be an explicit goal of self learning

When people self learn, there is usually a goal. It can be simply satisfying curiosity, but even so, there is often the aim of gaining understanding, or improving at a topic. Oftentimes, it can be explicitly stated, such as “I’ll learn enough to get a job.”

In this article, I will use “getting a job” as the recurring example of one’s motivation to self learn, but again, I encourage you to apply it to anything such as “wanting to perform live on stage”, or “write my own book”.

Now, I invite you to take a step back: I argue that the above, “learn enough to get a job” is too vague and not constructive enough at its core to structure one’s self learning, especially since it involves many parties and factors such as timing and luck. An improvement to that goal statement might be “Learn enough to be able to build an web app from an open ended business question”, which specifically and closely mimics what one would do in a workplace.

It follows that being able to complete a project independently is precisely what employers need, and helps one “get a job”. But “completing a project” isn’t the single final goal of self learning, but rather a step of many towards the real goal, which I call gaining expert intuition. Expert intuition is usually the accumulation of learning many techniques and completing many projects, and is what I argue, a better approximation of mastery and employability.

The term “expert intuition” has appeared in several fields and literature, but for the sake of simplicity I will define it as follows, independent to how it is used elsewhere.

Let’s examine why “expert intuition” is important and why it makes one extremely employable. To use an analogy of finding one’s way in the city of Toronto, Canada:

Intuition is like having an idea of where north is in the city, so if a restaurant is relatively south-west and you forgot your phone, you are able to walk in the correct direction without having Google Maps. The person who has intuition can save a lot of time to navigate the streets and is better able to recognize when they have walked too far or are on the wrong track.

To a person new to the city and thus lacks the intuition, this navigation could be very difficult. They might waste time walking in the opposite direction and not be able to recognize that there’s a landmark at the south, the CN Tower. They have to build that intuition up by asking locals or by first making mistakes.

Following this analogy, intuition in a workplace, to use a data scientist example, is like knowing one’s way around databases, or what families of machine learning algorithms to use for a given business problem. Intuition isn’t necessarily knowing all the answers all the time, just like we can’t memorize every restaurant in the city, but generally being able to avoid expensive mistakes when exploring for the real solution.

Overcome “needing work experience to gain experience” with expert intuition

I might have painted a lofty picture above, but now let’s connect it directly to why this makes one immediately employable. Let’s take the following types of candidates, and how they react to being given a brand new problem:

  1. The candidate requires extremely granular, step by step details, to be given to them to execute (entry level or unqualified)
  2. The candidate can independently generate ideas on how to achieve the implementation, and can be trusted to improve the ideas based on feedback, and deliver the part they are responsible for (very high quality entry level or above)

Which type of candidate will employers value more?

The main difference between the two types of candidates requires building that expert intuition. The employer can tell that candidate 2 can be relied on to explore solutions in a generally relevant direction. This can be true even if candidate 2 doesn’t have many years of relevant work experience; having completed previous projects, or demonstrating they have a wealth of knowledge, can help candidate 2 generate good, educated ideas and solutions.

Meanwhile, candidate 1 is equivalent to someone needing a step by step on Google Maps in a new city. Without expert intuition, they may make wander in wildly opposite directions and make costly mistakes. It doesn’t matter how many “certifications” or diplomas one has done; if they give off the impression they lack the intuition, these might be the thoughts the employer has.

Personally, I believe in taking a chance on a candidate, and I believe in learning on the job, and in fact, internships and entry level jobs can do wonders to push one from candidate 1 to candidate 2. I frame this problem in this perhaps lofty way, because of the prevalent you need work experience to get work experience criteria in today’s job market. I want to show how one can pragmatically work towards being candidate 2 with self learning.

How to develop expert intuition from self learning

We’ve walked through why expert intuition is important to keep in mind as the macro goal, and it follows that taking online courses or completing side projects are more of micro goals that accumulate into expert intuition.

But if you don’t happen to be one of those lucky people that are learning on a job, how do you make sure that gaining expert intuition is what you are working towards in your self learning journey?

Let’s go back to the opening of this article, where I mentioned how my 300 level courses were much more difficult yet rewarding, compared to 100 level introductory courses, which I felt weren’t representative of the real field at all.

Namely, the choice of self-learning difficulty matters. Is a self learner picking the equivalent of 100 and 200 level introductory courses over and over again, or are they pushing through that steep difficulty curve, and self-learning past the 300/junior and 400/senior year level?

Self learners and bootcamp grads: are you pushing past introductory level?

In the example of online courses for data science, I have seen countless candidates do the equivalent of 100 level courses again and again, not breaking out of the loop and doing unique projects. This is therefore not building up that employable intuition we’ve discussed in the previous sections. This limits their employability, as well as the types of positions they can go for.

I get it; when self-learning, it is easier to not push oneself mentally when there is no real immediate consequence such as failing a course in university. After all, for self-learning you are only accountable to yourself. If you are in a data science or programming bootcamp that only covers 100 and 200 level difficulty as well, I also suggest you beware this trap. You’ll need to put in additional effort and push yourself to learn or do “upper year difficulty” projects.

No matter the situation, a self learner needs to break out of the introductory level cycle in order to gain expert intuition. I cover how one gets over this mental block in “taking your self learning to the next level”, and “do the difficult thing”.

Conclusion

While self learning, it is easy to get caught in a cycle of repeating introductory/100 level difficulty materials. One should keep in mind that the goal of self-learning is to gain mastery or expert intuition, regardless if your goal is to switch careers, gain employment, or general exploration.

Breaking out of the cycle requires making the difficult choice to push past the mental blocks and continue to 300 or 400 level difficulties of self-learning, as well as completing independent projects to build and demonstrate expert intuition.

If you have been self-learning for a while, but feel that there has only been incremental change, or going in circles; I hope that this mindset is able to help you. For those that have accidentally stumbled across this pattern, as I did initially, I hope that providing a framework and vocabulary to think about it, can help you continue to build your expert intuition intentionally.

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