They Apply to Developers Too
People who visit my LinkedIn profile are greeted with a sleazy summary detailing how I went from making my First-team debut in Professional Football (Soccer for the American readers – We are going to have to arrange an argument about this sometime) match against Tottenham Hotspurs FC to becoming a Postman.
Those brave enough to continue snooping around on my profile soon come to find that I’ve got 3+ years of experience with Python and Machine Learning – Oh, and that I haven’t got the strongest academic background for my current role. My excuse is "I didn’t know I wanted to a Machine Learning Engineer when I was 10" (or 16, or 18, or 21) so I don’t beat myself up about it.
Because of this, people are often intrigued by what wisdom I could impart on them for their self-taught Data Professional journey.
Data Professions cover a large scope of roles (Check out Overview of Data related roles for an idea). While each role under the data professional banner has its own technical requirements, of which my so-called wisdom does not extend to, there are some universally applicable laws I’ve learned along the way which have helped me to get me to where I am today.
1 Take Ownership
Let’s start by calling a spade a spade; Being self-taught is not easy or easier than other routes – in fact, if you don’t feel lost in the early stages, I’d be concerned.
If you decide you’re going to take the self-taught path, you are inadvertently accepting all the responsibilities that come with being self-taught. For example, nobody is going to design a curriculum tailored to your goals unless you do. You must take ownership of your development if you are to reach your goals.
The best example of not taking ownership is something I was very guilty of myself earlier on in my career; When a topic was too difficult for me to grasp after a few iterations through, I’d put it off. My justification for doing so was always "I’ll learn it from a senior Machine Learning Engineer when I get a job".
Whilst it is true there are some things you simply won’t be able to fully grasp on your own and you’ll learn a lot from senior team members once you break into the field, by placing your development on the shoulders of others, you are fanning to flame a bad habit which will hinder your progress!
Instead, be sure to take control of all the levers that you have control over (i.e. how you spend your free time, and your learning). If you know there’s something you ought to learn but it’s a little bit difficult, ASK FOR HELP. There are a number of communities in places like Reddit, Quora, Stackoverflow, etc. that are solely dedicated to people with questions who need answers, and people with answers who have questions.
Note: Try to avoid asking someone random on LinkedIn for help unless it corresponds with work they’ve done or you’ve built that repore.
2 Don’t Overthink Things
When you’re self-taught, you don’t have the luxury of a set structure. You’re going to have to figure things out for yourself which means making mistakes is inevitable.
A popular example of overthinking is the language debate. So many people remain stagnant for long periods of time because they do not know whether to learn Python or R – the same phenomenon occurs when deciding between Tensorflow and PyTorch (I could go on forever).
I don’t think it helps when experienced Data Scientists act as gatekeepers and make posts like "Every Data Scientists must know x" – I smiled as I wrote this because I’ve definitely been guilty of this, however I know who I am speaking to when I say things like this and I know it’s not everybody.
As long as you have a good foundation in what your role is as a Data professional then you’ll be fine. Learning new tools become easier with experience with exposure to one, and it’s also important to keep your filters on when consuming content online by asking yourself this one question…
"Is this person where I want to be in the future?"
3 Sell Yourself Right Off the Bat
Readers of my blogs would I am massive on building a network, the right way! I never apply to Data Science jobs anymore, why? Because of my network. I got my first job as an intern Machine Learning engineer through my network and I’ve landed every single freelancing gig I’ve done through my network.
I don’t know if it’s called the law of attraction – I’m honestly not bothered what it’s called – but I am big on putting your energy out into the world then allowing it to attract what it attracts. By this I mean, get consistent about sharing what you are doing, learning, working on, etc because there is someone else in the world that’s interested in your capabilities, however, they will never know you have them unless you let them know.
You are absolutely not going to appeal to everyone but to those you do appeal to, they would go above and beyond for you – this could mean getting on the phone to HR and insisting a new role is created so you could be hired.
Bear in mind this is what has worked for me, but I know it’s not for everyone. You’ve got to find your unique way of selling yourself.
4 Jump in the Deep End
I’ve got a secret to tell you…
You’re never going to feel completely ready!
There is no right moment to take action. You’ve just got to take action.
The most common example I can think of is on the job hunt; When seeking a job, many people seek to fulfill 100% of the requirements in the job specification which in my opinion is extremely unrealistic because if you do, it means you’re overqualified for the role. There must be room for you to develop when taking on a new role so if that’s the case, you may as well go for it (especially if you meet 70% or more of the criteria).
Whenever you’re about to take a leap in your career, there will never be a moment where you are ready, you’ve just got to jump.
5 Never Lie to Yourself
People lie on their resumes, people lie to their friends, people lie to their family. While I do not recommend you lie in any scenario, there is one scenario in which it completely should not be tolerated and that’s when it comes to you!
Hear me out…
I genuinely believe anybody can self-learn everything to become Data Professional if they make the commitment to becoming one. Yes, it would take hard work, but some people are simply not cut out for doing it on their own – AND THERE IS NOTHING WRONG WITH THAT.
Focus on the goal; If your goal is to become a Data Scientists then it’s your responsibility to do whatever it takes, without breaking your values system, to become one. Yes people, it may mean that the self-taught route is not for you, thence instead you should enroll in some form of formal education.
It doesn’t make you weaker or dumber than others, in fact, what I get from it is that you’re extremely self-aware.
Don’t try to force yourself to be self-taught when you know it’s not working for you but you feel as though it sounds good. Instead, be goal-oriented. Do what you’ve got to do to achieve your goals – I can assure you, you’ll feel more fulfilled this way.
Wrap Up
Being self-taught is by no means the easier route. By enrolling in this path, you’ve automatically accepted there will be times where you lack clarity which can be overwhelming. There is absolutely no reason why someone can not transition from an unrelated field into the data fields. Faith that you’ll arrive at your destination and the grit to get you there is going to be vital.
Thanks for Reading!
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