Why unstructured learning is the only way to build a lucrative career quickly

Photo by Wes Hicks on Unsplash

Data science as a term was coined back in 2008 by DJ Patil and Leff Hammerbacher. In 2012, D.J. Patil and Thomas Davenport called “data scientist” the sexiest job of the 21st century in an article they wrote for HBR

This new dream job, combined the coding skills of a programmer with a statistician’s ability to build mathematical models in order to create massive value for companies. The case of LinkedIn’s “People you may know” provides a striking example of the power of data science.

The speed of innovation far outpaces college curriculum’s ability to keep up with it

There were no college courses on data science or machine learning when that article came out, there were not even any online courses. You could of course take programming courses in college, and you could take statistics courses, but there was no curriculum for a degree combining the two and nothing that was designed to teach data science specifically.

It was a new emerging field and if you wanted to get into it you had to study obscure books on data mining, and knowledge discovery (which is what data science used to be called) You had to be willing to deal with a lot of ambiguity, uncertainty, lack of feedback and you had to figure a lot of things out by yourself. This was around the time that I got into data science. I wrote about my experience here

Although I didn’t want to become a data scientist, I prefer to be a more of a generalist, the knowledge I learned through my project still allows me to talk intelligently to data scientists as well as explain things to non-data scientists in a way they can understand.

Companies care more about your experience than your degrees

My company was hiring for a senior business data analyst position recently. This career has become increasingly more lucrative as businesses continue to accumulate data and the need for analysts to extract insights out of that data is skyrocketing. As such, many colleges have begun offering masters degrees in business data analytics as a way to fill the skill gap. The success of these degrees however is still lacking.

The hiring managers reviewed hundreds of applicants for the position, many of them recent graduates with one of these new masters degrees in the field of analytics. But, they didn’t hire any of them. Instead they hired a junior analyst (who by the way had an unrelated degree) because he had experience doing data analytics in a business setting and solving real world problems.

This may be a single data point but I bet If a company had a choice between a fresh graduate with a masters degree but no experience in the field and a person without a related degree but with a few years experience, they will hire the person with experience 9 times out of 10. Experience is a more direct (and thus better) way to demonstrate that you can create value.

You can only get that experience through unstructured learning and solving real world problems in a the appropriate context. That’s why whenever people ask me for advice on how to advance their careers in a new, unfamiliar area, I always suggest they take on a project in that area as part of their job, or even on their own time.

Many lucrative careers require skills you cannot learn in college

YouTube millionaires is a regular feature of many business magazines and websites (see here, here, here or here)

These people managed to leverage a new platform to build a following and a steady stream of income. Talk about a lucrative career!

How did they manage to build careers without a college education? They figured it out on their own, through trial and error. There’s no college curriculum on how to become a millionaire YouTuber, how to become an Instagram Influencer or a Social Media marketer.

Maybe becoming a top Instagram Influencer is not what you want in your life, maybe you don’t want to be a YouTube millionaire, but if you think about how these people learned the skills that garnered them all this attention (and all the money), you can’t help but realize the power of unstructured learning.

Not only did the YouTubers and Instagramers build a following, made a lot of money, but they also learned incredibly valuable evergreen skills; skills that will serve them for life. Because if tomorrow one of these platforms disappears, the skills will still be valuable and can easily be transferred onto new and as of yet undiscovered platforms.

They learned skills in building a following, making full use of a the tools that platforms afforded them, skills in creating valuable content, ancillary skills such as video and audio editing, presenting information in interesting ways, speaking in front of a camera or microphone, connecting with others, etc.

More importantly they learned how to learn in unstructured ways, the most powerful, evergreen skill of all.

If there’s a college curriculum, someone younger, hungrier and cheaper than you can be trained to do your job

I love this tweet by Naval Ravikant:

“Specific knowledge is knowledge that you cannot be trained for. If society can train you, it can train someone else, and replace you.”

This quote provides the most compelling reason for unstructured learning.

Because this “specific knowledge” that Naval is referring to cannot be trained for, you have to learn it yourself, by solving real world problems through projects. College simply cannot cover everything, despite what the term “university” seems to imply. Some knowledge has to be acquired through self learning.

And of course, if there’s a curriculum, then the knowledge has already been commoditized, which means that your job can be shipped “offshore” or be given to younger, hungrier and cheaper workers. In order to prevent that, your best bet is to build a rare and valuable skill stack which cannot easily be replaced, and the only way to do that is through unstructured learning.

How I taught myself data science in 90 days

Photo by Avel Chuklanov on Unsplash

I first encountered data science (aka data mining, machine leaning, artificial intelligence, etc) back in 2014. I was a newly minted analyst trying to expand my expertise beyond the basics and my company had just hired its first data scientist.

I got curious about what she did, so after talking with her for a while, I was sobered to realize that I would need two more years of education and a graduate degree in statistics if I wanted to properly to call myself a “data scientist.”

The only problem was that after my experience getting an MBA, I had sworn off any kind of academic style learning. While I made a number of connections and friends, the degree itself had not taught me anything useful. In fact, I have learned more about business by reading books in my own time than I ever did in college.

If I was going to learn data science quickly, I was going to learn it on my own. I also realized that with the exception of a few select fields, companies don’t care much about your degrees, they care about what you can do for them, what kinds of value you can provide, and in many cases, you can provide incredible value without needing another college degree.

What I really wanted was to understand enough key concepts of data science that I could apply them in the real world to produce something useful.

So I thought “hey, I’m a smart guy, I can probably figure this out.”

Yes, I was quite conceited back then.

The challenge for me was that I absolutely hated statistics, and when I took the class in college I found it incredibly hard to understand, nonetheless I began my quest.

I started by looking for a few books that taught the key concepts of data mining with a bend towards applicability. They were quite hard to find and the few books I did find were dull and boring. But, I did my best to get through them and I managed to learn a few theoretical concepts.

Next, I searched for videos on YouTube. There weren’t many, but what I did find was very interesting. There were some videos that demonstrated the use of a free visual data mining tool called RapidMiner.

The author had many examples with the code and data easily downloadable so I could get them and try them on my own. It was exactly what I needed because it allowed me to see the concepts I was learning applied in the real world.

JIC vs JIT learning

I believe that the best way to learn is to solve a problem that you care deeply about or are strongly motivated to solve. It could be a personal problem, or a professional one and it should allow you to apply theoretical concepts to a concrete problem.

But why?

Almost all colleges apply the same framework towards learning. I call it Just In Case (JIC) learning.

You start by learning all the fundamental concepts first, you then apply these concepts to artificial problems in the book (which you don’t really care about) that are very clearly laid out and where there’s usually only one correct solution.

You then continue to learn more concepts that build on the fundamental ones you learned previously, you continue to apply them to even more artificial problems you don’t care about, in the hopes that some day you’ll need this knowledge to solve problems.

This theory of learning believes that knowledge builds on top of itself like a pyramid. In fact, many text books are set up this way. Fundamental concepts first, more specific knowledge later.

In real life, however, you start with a very specific problem you’re trying to solve, you search for the solution and once you find it, you can generalize that solution to other similar problems. I call that Just In Time (JIT) learning. You only learn things just before you actually need them, which maximizes both usefulness and retention.

That’s why I chose a specific project at work to apply data science on, that would both benefit the company, and teach me how to do data science in practice. As I struggled with the project, I learned another dark secret nobody tells you about in college.

The problems you solve during your classes are artificially set up to be easily gradable not to maximize learning.

For example in pretty much all data science books and courses, the data you work with has already been selected, cleaned and staged in order to make it easy for you to build the model. In real life it’s never that easy.

The hardest problem I struggled with during my project was figuring out what data to choose for my model in such a way that made sense, and I had no one to ask. No articles or online courses to follow. I happen to find the answer in an obscure book written by a practitioner vs theoretical books written by professors.

That’s how I managed to teach myself data science in just three months without having to go back to school and get a statistics degree. As a side benefit, I now understand and enjoy statistics. It makes sense to me because I have seen it applied in the real world.

Years later in my new job, I repeated the same process to learn another aspect of data science/ machine learning this time by doing a hackathon project at work. It cemented the lessons I had learned and taught me even more valuable skills.

It is because of these, and other similar experiences that I believe that unstructured learning is the key to an amazing career. Many valuable things can only be learned the hard way through experience not by going to school.