Data Science has been a hot field for a couple of years now, there’s no point in even discussing that. As a newcomer, you might be wondering what’s the best way to get into the field, and how quickly you could acquire some worthy knowledge.
In today’s article, I want to discuss two of the most popular ways of getting into any tech-related field, and those are:
- By being self-taught
- By getting a degree
And furthermore, I want to explore if those two options are somewhat different for a data science jobs, opposite to a let’s say front-end developer job.
Before starting, let me share a little background with you. I’m not a self-taught data scientist, I decided to go the college route. But before enrolling in my master’s, I learned to code on my own, a little bit with Java and Android, and a little bit with Python and data in general.
Because I have tried both, this article shouldn’t be biased. However, if you are making the decision right now, it should be based only on this article. I strongly encourage you to do further research as some stuff will differ from person to person, from country to country, and from college to college.
As I’m on college right now, the college route will be the first one to cover. Without further ado, let’s dive in.
The College Route
The college route is the one most people make, and there’s a good reason why.
If you’re thinking about enrolling into a Data Science master’s degree as I did, here are the 3 big questions you should know the answer to:
1. Does your country even offer a Data Science degree?
This one is kind of obvious, but Data Science isn’t just yet well recognized and represented in most universities. Sure, maybe you’ll dive a bit into machine learning on general Computer Science degree, but that’s not a Data Science specialization in any way, shape or form. Developing countries maybe still don’t offer a Data Science degree, so make sure to check this first.
2. Are you ready to commit to it?
Before I enrolled in the master’s degree, I jumped from Android to Python to Web development all the time. I would literally jump from one technology to another just because that day I just felt like it. As the end result of that quite idiotic strategy, a year later I ended up not knowing anything about anything.
Now I’m committed only to Data Science, and I can guarantee that in the last year or so I’ve learned a sh*tload of stuff that I wouldn’t be able to grasp for 5–10 years with the old regime. That’s what it means to commit to one thing. Make sure you’re able to do so too.
3. Can you afford it?
It’s not just the question can you afford it, but also if you’re willing to afford it. I mean, you could find a web-developer or web-designer job without any degree in some small company by being self-taught. That wouldn’t cost you a dime (if you know how to google), and you’ll also end up doing a good-paying job.
Pros and Cons of the College Route
Have you answered honestly to every question from above? Good. You should see things more clearly now.
In this part, I want to provide you with my opinion on being a data science college student. I will share with you what’s good and what’s not so good, hoping you can relate. Let’s start with the good stuff first:
PRO: Connections
You’ll get to meet so many amazing people whose knowledge by far exceeds anything you can find in a book or an online course. And the best part is – that people know people in places that matter. It really helps to show yourself in the best light, you never know when it might come in handy.
PRO: Cool projects
This will vary from college to college. The one where I study is heavily project orientated, so doing a couple of complete, from zero projects per semester is the best thing you can do to boost your knowledge. Those projects vary in difficulty and technologies used, but hey, you are delivering a fully working solution at the end.
If your college offers only a theoretical introduction to everything, with no real-world application, I wouldn’t consider it to be worth the money and time.
PRO: Learning from the experts
I know, books and online courses got like 99% of stuff covered. But data science can sometimes be abstract and having someone who can answer your dilemmas is a gold mine. I mean, yeah, you could google stuff, but I found abstract stuff to just get more abstract after googling because people who don’t know how to teach and explain like to use big words. They do so mainly to sound smart and show expertise in the field when the reality is exactly the opposite.
CON: Might be expensive
The degree may or may not be expensive, and you may or may not care about it. It depends from person to person, and there’s no point in discussing it further.
CON: Somewhat fixed schedule
I like to learn stuff in advance. If I know we’ll cover Linear regression in a week on college, I’ll learn the hell out of it before that class. The whole point of that class then is to recap everything and to ask the professor anything I didn’t find 100% clear.
I’m just like that, and it can be problematic sometimes. For example, I might be sitting there in class bored to death because I know more than enough on the topic that I’m ready to move forward. But no, we stick to the schedule. That can suck sometimes.
The Self-Taught Route
While this route is a way cheaper it also has some downsides. For example, you are more likely to fail when something isn’t due Monday, and in general, being self-taught requires a lot of discipline. As with the college route, let’s break it down into some pros and cons.
PRO: Learn at your own pace
There’s no one telling you to proceed further. You do what you want, and how you want. Now when I think about it, this could also be a con, at least if you’re not well organized and disciplined.
PRO: Learn what matters to you
You’re more into Machine Learning and less into social network analysis? That’s great, just spend more time learning machine learning. College students will have to learn both, even though they might have no interest in some areas.
PRO: Save money
This one is obvious. Instead of spending money on tuition, you could buy a GPU or two, making deep learning tasks perform faster. The amount you’ll save will differ from college to college and can be substantial. I don’t think that you should save money on education, but hey, if you can’t afford it then you can’t afford it.
CON: Lack of structure
If you’ll be the one constructing the learning curriculum, and you don’t know what’s important and what’s not, it probably won’t end well. You could copy the curriculum colleges use, and go through materials faster, focusing mainly on topics you’re interested in. However, the lack of some formal structure can make you feel unmotivated, which leads me to the next con.
CON: Easy to quit
When there are no deadlines it’s easy to quit the first time things get a bit complicated. Maybe you’ll quit that project, or maybe the whole idea. It’s easy because you don’t have to answer to someone. It will take a whole lot of determination to get through when things get tough.
The Job Market
So you’ve read through this article and preferably through some other articles. Most importantly, you’ve decided what’s important to you, and what isn’t. But what about the job market? Can you get the same job in the same company with and without a college degree?
The short answer is no, you can’t. Let’s discuss it a bit more.
If you want to work for the bigger companies which have HR departments, and you don’t know anyone who works there, you’re screwed without the degree. Let’s face it, if there will be 100 applicants for the job position, the HR department will immediately discard you, because the other person has a degree. It’s not fair, I know, but that HR person just doesn’t care for your GitHub profile, however amazing it might be.
On the other hand, if you’re applying for a job in a smaller company, preferably without the HR department, you might stand a good chance. If your resume is on point, GitHub showcases your coding and thinking capabilities, you’ll probably land the job interview. And they’ll probably ask you why didn’t you go to college, so be prepared to answer that.
In a nutshell, if you don’t have a degree it’s much easier to get a job at a small company because your CV won’t automatically get discarded. And that’s what you should be shooting at, at least when you have no working experience.
And the data science jobs are pretty much filled with persons who have a master’s degree, if not Ph.D. I don’t think that formal education level perfectly correlates with your ability to code and think, but most of the bigger companies, unfortunately, do.
Before you leave
Picking with which route you’ll go is by no means an easy decision. I hope this article helped you to see some things more clearly, but once again I’ll stress that you should do far deeper research on your own.
I went the college route after being self-taught, and I find that working perfectly. I’ve spent some time exploring the field of interest, and seeing if that’s something I would like to do in the long run. Once the classes started, everything switched to turbo mode. And I like it that way.
Thank you for reading. If you have any questions or concerns, do not hesitate to go to the comment section.
Loved the article? Become a Medium member to continue learning without limits. I’ll receive a portion of your membership fee if you use the following link, with no extra cost to you.
