|Data Science Bootcamp||$13,500||Typically 12-16 weeks||Guided|
|Data Science Degree||$32,000-$200,000||Two to Four Years||Guided|
|Data Science Online Course||$0-$50||Days to weeks||Self-directed|
There’s a wealth of information in the world for anyone who wants to learn data science. The hard part is choosing the right format for it. Do you go for a data science bootcamp? Do you aim to get a degree? Do you try an online course instead?
The answer boils down to: it depends. It depends on how much time and money you have, whether you love being a self-taught learner or not, and if you have any pre-existing knowledge about data science. Do you want to be a freelancer? Is data science just a hobby? Do you want to dive into a data science job?
A data science bootcamp typically runs for 3-6 months. It’s an intensive deep dive into data science. Some bootcamps only cover one language, others run through all the basics. Although there are part-time options, you should expect to spend around 40 hours per week as a minimum doing this.
A data science degree is a more holistic, slower, and expensive option. You can go for the two- or four-year option from an accredited university, although these days, most folks go for an option like EdX or Coursera to do a course from a university that way.
An online course is the most flexible option. There are tons of different options, covering beginner to advanced, Ruby to Python to Perl, video, cohort-based, free to thousands of dollars, and every other flavor of learning you can imagine.
Even if you want to get a job at the end of this, any option will do the trick. Most employers don’t care about your credentials that much – they want proof that you can do what you say you can.
Let’s break down each of the three options: the pros, the cons, the cost, and what you can expect to get out of them in the end.
A data science bootcamp is typically run live by an organization with a whole bunch of students all at the same time, cohort-style. They started getting popular around ten years ago, and today you can find one for just about any type of data science learning you want to do.
Pros of a Data Science Bootcamp
The biggest benefit – and greatest downside, but we’ll get into that later – of the data science, bootcamp is the sheer intensity.
Want to be fully immersed in a learning environment until you can eat, sleep, and breathe code? A bootcamp is the right choice for you. It’s a lot of work, but it’s rewarding. These boot camps will be as time-intensive as a full-time job, plus homework. It’s a great way to learn quickly.
Boot Camps are a systemized learning experience. It’s like doing college on steroids. You have lectures, office hours, homework, and even career guidance sessions. They’re the full package. Many even offer job guarantees at the end, because they’re that confident in their ability to teach you employable skills.
And because they’re cohort-based, they remain very agile. Unlike colleges, which pivot slowly to incorporate new languages and techniques, boot camps will teach cutting-edge technology, which helps you compete for jobs.
Cons of a Data Science Bootcamp
The downsides of a data science bootcamp are, like I mentioned above, similar to the pros. It’s intense. You will spend a lot of money. The average cost of a data science bootcamp in 2020 was about $13,500.
In addition to money, you’ll also spend a lot of another precious resource: time. Many boot camps require 40-hour weeks, not including the time that it takes you to complete assignments and do your homework. It’s not the kind of thing you can do if you have a job unless you have a very understanding boss.
Simply put? It’s inflexible – if it doesn’t fit your situation, there’s not much you can do about it.
Another pro also turns into a con: because the focus of a bootcamp is on employability, rather than teaching you data science, many boot camps skip the foundations of data science. Most boot camps are not going to teach you a lot of the basic math and statistics that go into data science.
Sure, you’ll be able to code like a wizard, but you will lack crucial knowledge that may become relevant. Also, if you lack the foundations, it’s harder to build out your knowledge later on. YOu may be able to memorize Python syntax, but if you don’t understand what a two-tailed T-test is, you might struggle. There’s always a chance that you’ll be questioned on that kind of thing at an interview – employers don’t want you to just spit code out, they want to know you understand it too. If you can’t prove that, you might find yourself without a job.
A data science degree is when you go to college for years to learn how to do data science. For this article, I’m not going to include the Coursera or EdX options because those go in under data science online courses. I’m talking about picking a major, living in dorms, and sitting in lecture halls for your data science degree.
Pros of a Data Science Degree
A data science degree provides the perfect foil to a data science bootcamp. It’s almost the complete opposite. The very best part of the data science degree is that you get a full, comprehensive understanding of data science. And it’s not just going to be data science – you’ll get cryptography. You’ll understand the math behind algorithms. You’ll know how to run statistical analyses. And, bonus, although most employers don’t need a data science degree, it doesn’t hurt!
You’ll get exposure to many more topics because a degree isn’t narrow in scope like a bootcamp is. And you’ll get more soft skills, too. Bootcamps are trying to shovel in as much employable info as fast as possible. A data science degree is teaching you to learn, too.
Finally, you’ll be rigorously tested. A degree requires you to pass – that’s what’s so valuable about the diploma because it proves you learned the material well enough to score points on a test. This test will show you exactly where your strong and weak points are, which can help you shore up your knowledge even further.
Cons of a Data Science Degree
The cons are very similar to data science bootcamps, only worse: if you thought a bootcamp was expensive at $13.5k, you won’t be pleased to learn that data science degrees are even more pricey. Beyond the cost of education, you’ll also be paying for room and board for two or four years. A public university degree – the cheapest option – will set you back around $14.9k per year if you’re off campus, living at home. The most expensive option, being on-campus at a private nonprofit university, costs $54.4k per year.
There’s also the time component of spending two or four years. It’s quality education, yes, but you’ll spend literal years of your life getting it.
And finally, while degrees focus on foundational aspects, they lack employable aspects. The goal is to teach you, not employ you. While this means you get a firmer grounding in the basics underlying data science, it also means you may not get the same advantages in terms of employability.
A data science online course is the third option. It’s by far the most flexible and affordable. As I mentioned earlier, you can take an online course in any data science subject under the sun. It’s also the option that requires the most internal discipline.
Pros of Data Science Online Course
The main benefit of a data science online course is that it is what you make of it. You can find amazing free data science online courses run online on YouTube. You can get certified by Google. If you want to get good at Ruby on Rails, there’s a course for that. If you prefer to get into cryptography, you can go in that direction instead. If you want to prepare yourself for any particular big company like FAANG, there’s a platform “StrataScratch” that can get you interview ready for that company.
Another pro here is also a con: it’s self-paced. If you are holding down a job and you have two kids at home, a boot camp or a degree might not be great for you. An online course would be a much better solution. It’s more easily fit in around your existing lifestyle.
An online course also is typically cheaper. Because it’s neither trying to get you employed in the next 12 weeks nor trying to teach you every single bit of data science knowledge under the sun, it tends to be more affordable.
Cons of Data Science Online Course
The cons of data science online courses?
It’s self-paced, as mentioned above. If you want to learn data science, you have to be the one driving the train – nobody is going to hold your hand.
It’s also harder to find a comprehensive course. While you can create your data science curriculum with a bit of Googling, you’ll have to put it together yourself rather than having the information spoon-fed to you. Plus, there’s always a chance you’ll miss something.
You’ll probably struggle with motivation, because there’s less community (although some online courses are trying to address this with Discords and community events). Both bootcamps and degrees give you cohorts and companionship. With courses, you’re alone.
Finally, you miss out on both learning the foundations that a degree would give you, plus the employability tips and tricks the bootcamp gives you.
It’s impossible to say which option is best as it all depends on your situation. And in many situations, it’s best to use a combination. For example, after you graduate, you may want to buff up your knowledge with a course or bootcamp.
Here’s a quick recap so you can make up own mind:
Data Science Bootcamp: Around $13,500, three months, employment-focused and intense.
Data Science Degree: Between $32,000-$200,000, two to four years, foundational knowledge, intense.
Data Science Course: Many free options, and many cheap options around $100 or less. These courses can take a week, or several months to complete. They range from employment-focused topics to the basics. They’re mostly self-paced and self-motivated.
Nate Rosidi is a data scientist and in product strategy. He’s also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Connect with him on Twitter: StrataScratch or LinkedIn.
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