# Any career is possible: What I learned from multiple industries unrelated to my formal education

When you were a child, you might have believed this: “When I grow up, I can become whatever I want!” I’m curious: do you still believe this of yourself, right now? If you have already become who you wanted to be, do you think it is possible to shed that identity and become something else? Is there mental resistance as you ask yourself these questions? If so, can you identify where that mental resistance is coming from?

I ask because it seems that somewhere along the way, we realize that there are constraints in reality. “Whatever I want to be” turns into “Whatever makes money so I can pay rent”. Constraints can also manifest as difficulty or barrier to entry. If I can’t pass some requirements or exams (e.g. CFA Level 3), I can’t become a CFA - it’s black and white. If I can’t pass interviews to be hired as a data scientist, I can’t become a data scientist - it’s clear-cut like that. The list goes on.

I argue that this couldn’t be further from the truth, yet it seems to be an ingrained belief. We can all afford, in terms of time and effort, to “become” anything - be it a new identity or a better version of an existing one, such as a better sister/daughter/friend. For simplicity, in this article I focus on using this mindset to enter or transition into any career, but it can be interpolated to other types of professional or personal development.

In this article I’d firstly like to discuss with you how to recognize self-imposed constraints, and how to avoid them. Then, once that’s out of the way, we’ll plan how long it will take you to achieve that transition.

## You can afford to enter any career, in terms of time and effort

“Whether you believe you can or you can’t: You’re right” - Henry Ford

Time is money, and everyone is rich. However, you can only “purchase” any career path with time and effort, not time alone. You also cannot purchase a career path with real money. This part will be a thought exercise, so I encourage you to follow along, substituting the thought process with your personal goals.

I use my personal example of “becoming” a data scientist to lay out the setting.

The goal:

• I want to become a data scientist straight out of school.

Here is where a lot of resistance might come in. Here are real thoughts I have had:

• My formal education is in economics, not computer science or statistics.
• I don’t know Python for data science. I’ve never used NumPy or pandas.
• Because of the above reasons and many more, I am at a disadvantage.

Here, I was letting those perceived deficits be a blocker to achieving my goal, which is simply incorrect.

The simple explanation to refute all 3 of the above is:

• If it costs a computer science or statistics major 1,000 units in time and effort (henceforth simply denoted as effort) to achieve the same goal of becoming a data scientist straight out of school, it might cost me 1,200 units in effort. 1,200 units seems like a lot at the moment, but I can afford it by dedicating the time and effort to it.

However, my initial resistance to a difficult goal was that because of my deficits, I can’t do it; it’s too difficult. Wow, I really should have chosen a different university major when I was 17 years old. This is, to translate, thinking I can’t afford 1,200 units of effort. But this is wrong. Remember that everyone is rich in time, and effort is free. I could easily afford 1,200 units of effort.

It was possibly more expensive for me since I needed to fill in the gaps between myself and those people I thought have a leg up (by 200 units of effort), but going into any career that was “unrelated” to my formal education still wasn’t going to cost as much effort as I thought.

So here’s the conclusion: No matter what your current work experience is, or your formal educational background, you can afford to enter any new field or improve in your current one.

To give another illustration: If I had believed that in order to work in the gaming industry, I would need to earn a game development related degree, then I would have gone back to school for 4 more years. That would have blown up the effort and opportunity cost (more on that in the next section).

Instead, I made my own game, which took lots of units of effort and unpaid labor, but I saved on tuition and 4 years of time. Now I am hiring people who did do those 4 year degrees and I am arbitraging my data scientist salary. A bonus is that the studio is profitable now.

From personal experience, it saves units of effort to simply strike out on one’s own. For example, building my data science project portfolio while studying Economics formally, or making my own video games, was much cheaper in total units of effort compared to if I had gone back to school for either one. I elaborate more on how I apply efficient self-learning in this article.

I suspect that the advertisement of formal education as a product reduces the perceived cost of effort of the formal education route, compared to doing it yourself, but my point stands. You can purchase any career with effort and time, but not with real money. Everything is possible, but one is deserving of the results only after one commits to putting in effort. Hence, the fact that DIY seems harder shouldn’t be a hard limitation.

This brings us to the next point:

## Turn your deficits into time-saving discounts

Anything is affordable in terms of effort, but use all the wits that you have to reduce that price point. After all, who doesn’t love saving?

Previously, I mentioned my guess that if it takes a computer science or statistics major 1,000 units of effort to become a data scientist, then it might have taken me, an economics major, 1,200 units of effort. The deficit might be to improve my coding skills or whatnot. This has to be an educated guess, so it is important at this stage to identify your strengths and weaknesses for a better estimate of the units of effort needed to achieve your goal.

For those interested in entering the data science field, I’ve written about how to estimate how to prioritize building up your statistics and programming skills in the two pillars of data science.

Keep in mind that we have to approach this exercise with the mindset that we can afford the units of effort, since time is something everyone has, deficits or not. The next step, is to make use of what you think are your deficits, and turn them from something negative to positive.

In my case, it was that I already have statistics training from econometrics courses, and programming knowledge from making games (seemed totally unrelated, but can be used to discount effort). So I might use this to discount 50 units of effort. In reality, since I was working on a master’s degree, it was more like a 100 units discount, which put me at barely a deficit to those that I considered having a “leg up”.

This is similar to when you have guests coming over to your apartment, but you don’t have enough nice chairs. You might dig around and find some flimsy fold up chairs, camping chairs, or even use some storage boxes as a chair. What doesn’t seem to work at first, you will find you already have in possession if you look hard enough.

An example is recently while in a mentoring session, I spoke to an aspiring data scientist working on a master’s degree in English. She was learning Python on the side. I immediately suggested that she work on one of her papers in her master’s degree that makes use of Python and data science, no matter how far of a stretch it might seem. She could shoehorn some NLP models into an “analysis of genre of literature” paper. This has the effort-saving effect of making progress in her master’s as well as building a unique piece for a data science profile.

It seems like it would be more effort at first, or even risky: trying to add an NLP component to a paper that is completely uncalled for might require 10 more units of effort, and get the same “grade”. But what we are concerned with here isn’t the battle, but the war. This approach saves overall effort compared to writing the paper in the master’s program without Python (30 units) and a separate data science project (30 units), for 60 units total.

By combining them, it takes only 40 units to have a very distinct and stand out data science project. For me as a resume reviewer, that immediately stands out 10x more than the Nth “cat and dog classifier” from Andrew Ng’s ML Coursera course I’ve seen.

As a summary, if you have a PhD in a field unrelated to the industry you seek to enter, don’t throw away all those years of effort. Use it as a time-saving discount by getting creative. If you studied engineering and paid those high tuition fees, don’t neglect how that can help your career transition instead of hinder it. Embrace your past experience and how it makes you unique - use the skills you thought were deficits to your advantage.

## Research comparables to estimate the effort needed to achieve your goal

Now that we’ve looked at how we can afford to enter or transition into any career, and how to save time and effort to do so by turning deficits into time and effort saving discounts, let’s dig into how to estimate the effort it would actually take.

After all, we do need to consider the opportunity cost. If you want something very badly, but it is too expensive, you might need to prepare over more time. Or, if there are other, better uses of your units of effort, you can choose to trade the effort for something else. Note that in this case, you can still afford to pursue that career, but you can choose not to. It is not because you can’t afford it, which is a large and important distinction.

### Look up people on LinkedIn and construct your upper bound and lower bound

My favorite approach at this effort estimation stage, is to search for people on LinkedIn who had a certain type of major or educational background, but now their job title is what the goal is to become. To use my past examples, I would search for people who currently hold the role of data scientist or game developer. I’ll observe when they graduated and when they were able to gain the job title of choice.

In many cases, it could be non-linear, which increases the timeline, so gathering more samples is essential. I’ve seen people who studied economics, founded multiple startups, and now are in data science. So it would have been a 7 year journey since they have some detours.

Some might have taken a slightly more direct route - doing a consulting role for 2 years then entering data science. Or maybe they earned another master’s degree and transitioned into data science (this I found was more rare, and like I mentioned before, holds a high opportunity cost.)

What is important here is to get some realistic timeline for yourself and an upper bound and lower bound of effort. Assume you will not take longer than N years. Are you willing to spend that time to enter the field?

If you estimate and divide the timeline by the amount of effort each week you can allocate towards the goal, it becomes not a question of whether you can afford it, but simply how long it will take to pay off the purchase.

The next step is to identify options for self-education, side projects, or part time coursework to spend your units of effort on. You could even get paid in real money - seek out an internship or contract role.

## 8 years as a lifetime - plenty of time and flexibility

Here I want to call out a defining experience that inspired me to expand what I saw as “possible”. It was a talk by Vivienne Ming.

She spoke about how her life had once been at a point of no return, experiencing homelessness and suicide ideation. (Link to an article on her experience).

Now she is an accomplished neuroscientist and leader in the AI field, and gives away patents to charitable organizations because she believes that it will help more people in need. She has contributed heavily to research in the intersection between behavioral economics and machine learning, and also founded multiple companies.

After hearing her talk, it made me think: Many dreams that I had thought impossible, suddenly seemed closer. I have not experienced the difficulties Vivienne Ming has, not even close. The fact that she could come back from the brink of death and such a dark despair, and continue on to provide so much to science and humanity, showed me that there was more in me to give.

She also mentioned that she sees 8 years as a lifetime. It was ambiguous in the way that I recall it, but I personally interpreted it was that we can feel free to, for example, become a chef and climb to expert status in 8 years or so. Once there, we are free to explore elsewhere, such as going from chef to engineer, instead of feeling locked down, simply because we are in a profession that was determined by a choice we made in adolescence (commonly, choosing a university major).

An accumulation of my interpretation of her talk, as well as my personal experiences, yield the framework we’ve walked through together in this article.

## Conclusion

In this article we explored how we can afford to achieve many options with effort and time. We discussed how we can take what we thought were deficits in our past experience and turn it into time-saving discounts. What is important is that we can choose to pursue or not to pursue a career path by choice, but not because we cannot make it happen. For most cases, in terms of time and effort, we are blessed with an abundance, not a lack of it. It only depends what we consciously decide to spend it on.

Practically, we can observe those that have achieved similar career pivots or transitions, and work backward from those people’s timelines and effort to make our plan. Our options are more flexible than what is commonly perceived; nothing says our past experience or education locks us in.

I leave you with the above thoughts: I am curious if what you thought impossible or difficult, seems more within reach now? Or, if any of this wasn’t persuasive enough, why was that? Feel free to leave your thoughts at hello@susanshu.com.