Three Things You Need to Know About Data Science
What the “gatekeepers” don’t want you to know
Data science is an extremely unique field for three key reasons:
- It’s a meta-skill
- It’s permission-less
- You can create your experience
Let’s dig into this a bit more.
First, how is data science a meta-skill?
You’re probably wondering what the hell a meta-skill is.
A meta-skill is essentially a higher-order skill that enables and empowers other skills to happen.
They are the foundation on which you are able to engage with new skills effectively.
In the case of data science it’s a combination of several different knowledge bases and skill sets:
- Critical thinking
- Problem solving
- Coding
- Engineering
- Math and statistics
- Business acumen
- Communication
- Project management
Just to name a few.
A lot of university programs and boot camps focus primarily on acquiring technical knowledge and teaching you the individual tools of the trade — because the rest of those skills are difficult to teach.
Meta-skills become a permanent part of you — they’re skills that are woven into the tapestry of your psyche.
These are the skills that enables you to achieve more.
For example, while learning a Python is a skill, the ability you develop to learn how to write code, thus making it easier for you to code in any language, would be a meta-skill.
And perhaps the most important meta-skill for success in this field is learning how to learn.
And most bootcamps or university programs certainly don’t teach you these meta-skills.
Second, how is data science permission-less?
Let’s contrast data science to some other professions out there:
Especially those careers where you’re “legally required” to have a specific certification or degree to do the job.
To become an accountant you have to enroll in the CPA Professional Education Program, complete 30 months of relevant accounting experience, and finish four education modules during full-time work experience;
To become an actuary even after completing a degree in math or stats, you still must pass a battery of exams and additional coursework required by one of the governing societies.
To become a financial analyst you have to pass a series of exams, achieve qualified work experience, submit reference letters, and apply to become a charter holder.
To become a doctor you must be a board-certified physician with a medical degree, plus experience working in the field to practice as a doctor.
There are just a few examples of the many countless career paths where you have to have permission by some governing body in order to call yourself one of the in crowd.
We don’t have this in data science.
Third, you can create your own experience
The way that we can prove that we have what it takes to do the work of a data scientist is by having a portfolio of amazing projects.
In those fields that we just discussed, the concept of having personal projects to demonstrate your understanding and command of the field doesn’t really exist.
Probably because none of those careers have been certified as sexy and have hundreds, or thousands, of people vying for the same opportunity.
The truth is that you don’t need anyone’s permission to become a data scientist.
Anyone can get set up with Python, R, or Java on their machine.
Anyone can download VS Code or their IDE of choice and start writing code.
Anyone can set up a SQL database on their local machine, or in the cloud.
Anyone can scrape the web for data, get data from an open data portal, or even purchase data.
Anyone can enrol in one of the many free open courses online.
Anyone can become a data scientist.
We live in a day and age where you have all of the requisite means at your disposal.
Entirely free.
And there for you to leverage.
But, because anyone can learn the fundamental tools of data science it means it’s highly competitive.
In summary
Data science is unique because it’s a meta-skill, permission-less to enter the field, and you can create your own experience.
Data science can’t be taught by just passively watching videos or sitting in class.
But it can be learned.
The principles and the process of solving data problems can be learned.
Like all of the most interesting careers, you learn data science by doing it.
And this is why you MUST create portfolio projects — because it will transform you into a practitioner!
By creating well thought out, well planned, and superbly executed projects you can recreate the experience of working in a job.
The experience you gain doing projects will help you clear take home assignments, it will give you something to talk about in interviews, and it will give you an opportunity to learn how to do data science.
But to get noticed in the first place, you MUST create projects that help you stand out from your competition.
Let me know what you think. Leave a comment below, let’s open this up for conversation.
That’s it for this rant. I’ll see you all in the next one.
And remember my friends: You’ve got one life on this planet, why not try to do something big?
