Are Data Science Bootcamps Worth It?

If your goal is to learn enough about data science to embark upon a career in this growing, popular, fascinating—and competitive—field, you have a number of options for acquiring the knowledge you need. One method is the so-called bootcamp that, while it doesn’t involve doing a hundred push-ups in the snow at five o’clock in the morning, will nonetheless put you through your paces as you learn a complex subject in the shortest possible length of time. Although not for everyone, a good bootcamp will do what it sets out to do, and have you ready to assume a role in data science in far less time (and at far less expense) than securing a college degree in data science, statistics, or computer science.

What is Data Science?

Data science, rather obviously, is the science of processing and interpreting data, often enormous quantities of them. The amount of data generated and digitally collected in a single day is almost terrifying

Consider Disneyland: once upon a time, turnstiles were used to track the number of people who visited a given attraction. The turnstiles continue to exist, but alongside a mobile phone app that everyone in the park basically has to use, and which feeds Disney myriad gobs of information, not only about which rides were visited, but who visited which rides in which order, where traffic is heaviest in the park at a given time, and how many Dole Whips and churros they sell. Disney then takes this surfeit of information and uses it to determine everything from how many churros they need at the Fantasyland churro stand at 3:00 p.m. on a Saturday in July so they neither run out of nor waste churros (it’s by counting pennies that you end up with a company worth in excess of $185 billion) to refining the algorithm that governs access to the expedited queues available (at a fee) for the most popular attractions.

There’s a whole lot more to data science than pineapple soft-serve and fried pastries rolled in cinnamon and sugar. But that microcosm illustrates just how much there is to be gained from the analysis of data, both looking to the past (what’s known as data analysis) and into the Magic Mirror to determine the future (what’s known as data science, strictly speaking.) Data are a valuable and much-gathered commodity today, precisely because they can be used to predict the future, sometimes with alarming accuracy.

What Can You Do with Data Science?

According to statistics website Statista, by 2025, there will be some 180 zettabytes of data floating around (literally, as most data resides in the cloud.) That’s 180 x 1021 bytes of data, which is more bytes of data than the 7.5 sextillion grains of sand there are on Earth. That’s also as compared to the paltry 72 zettabytes of information that were floating around in 2020. A lot of data science professionals are going to be required to draw conclusions from all that data, conclusions that can show organizations big and small what kinds of business decisions they should make. 

Data science training will prepare you for a variety of professional roles, the three principal ones being Data Analyst, Data Scientist, and Data Engineer. Together, they slice up the data interpretation pie, with Analysts keeping their eyes firmly on the past, Scientists theirs on the future, and Engineers on the software necessary to perform those interpretive miracles. All these roles are in high demand: Forbes predicts more than a 30% increase in data professionals by 2030. India’s Economic Times, the second most-read business newspaper in the world, predicts that 96% of all companies intend to hire job-seekers with big data skills. (That’s skills in so-called Big Data, the enormous datasets that require something beyond traditional data-processing software, not large skills.)

As far as data science is concerned, the future is now, and company upon company is harnessing the power of data to gain an all-important edge over the competition. While structured data -- the kind that can fit into neat fields such as customers’ names, addresses, and purchase histories -- still exists, a whopping 80% to 90% of data today is unstructured, meaning that it’s all little bits of unarranged information (like what Disneyland is able to gather with its app) that require special handling (called, appropriately enough, data cleaning) before it can be used for statistical purposes.

Data science is also a field that makes regular use of machine learning and artificial intelligence (AI) to handle tasks that are beyond the capabilities of a single human being, such as cleaning all that messy, unstructured data. Machine learning is also employed to handle the analytic heavy lifting and to perform data operations that are beyond the abilities of traditional means of data processing. The data professional of today must, therefore, understand these still-developing technologies.

A great deal can be done with data science today: it is a thriving industry without which many a corporate decision wouldn’t be made. That covers small decisions (how many churros do they need in Fantasyland at 3:00 p.m. on a Saturday in July?) and very large ones (where should enormous hedge funds invest their investors’ hard-earned money?). No, it’s not Madame Leota’s crystal ball, but, at times, data science can come close.

Why Learn Data Science in a Bootcamp?

Learning data science can be done in a number of ways. You can go to college and get a bachelor’s, master’s, or doctoral degree in data science, you can get your degree in a related field, you can attend an in-person or (more likely) online bootcamp, you can sign up for a self-paced on-demand course, or you can even try to learn about it from free video tutorials. Of these methods, the first two are the most tried-and-true means of preparing yourself for a career in the interpretation of data.

A bootcamp is a relatively new learning modality, and was designed to impart the greatest amount of learning in the shortest amount of time, and to concentrate the learning on the declared matter at hand. A data science bootcamp is going to teach you data science with an eye towards making a career in the field: you’ll learn what you need to know to land a job. For people seeking a quick path towards a career, be it a first one or a new one, a bootcamp offers an efficiency of learning that’s not to be found elsewhere. Most bootcamps live up to their name and put you through very rigorous paces. If you sign up for a bootcamp, you should be prepared for a serious intellectual challenge and work harder than you may have ever worked in your educational life.

Bootcamps Compared to College

That said, the most usual way to approach a career in data science is to go to college and get a degree in data science, statistics, mathematics, or computer science. Some people with degrees in the latter three fields continue their studies to obtain master’s degrees in data science. Indeed, people with graduate degrees are frequently to be encountered in the highly competitive data science job market

College is undoubtedly an excellent way of learning data science, but it comes with a number of drawbacks, most obviously the substantial outlays of time and money involved. Four years of full-time study during which your opportunities for income are few is a very long time. It’s an even longer time if you attempt to do it part-time. That’s often more than some people can afford. 

Then there’s the sticker shock: on average, students attending in-state state universities come away $104,000 poorer at the end of their studies, while private university bachelor’s degrees carry an average price of over $230,000 for four years. Of course, you can borrow money to attend college, but that sets you up for a lifetime of debt that makes questionable fiscal sense in either the short or the long run.

A bootcamp will cost you around a tenth of what a college education would cost and takes a fraction of the time. True, your bootcamp won’t cover all the academic territory covered in a college data science program, but you’ll still learn a great deal, and it will be information you’ll be able to use in the workplace. Not to cast aspersions on higher learning, but, even if you go into a scientific field, you’re probably not going to end up using everything you learned in college in your professional activities

Additionally, a bootcamp eliminates the required classes that form part of a college program. Thus, bootcamp curricula omit a lot of the math that computer science majors learn, along with the distribution requirements that round out what is traditionally termed an education. As a result, a bootcamp won’t have you writing a paper on the Canterbury Tales, puzzling over differential equations, or poring over medieval history. On the other hand, it will keep its eye on the proverbial prize and teach you what you need to know in order to secure employment. 

Bootcamps Compared to Self-Paced Courses

An alternative to a bootcamp that puts you in the same moment in time as your instructor is the self-paced or on-demand course in which you pay a fee that gives you access to a library of video tutorials that you can watch at your leisure. These can be quite extensive and cover all the material you might find in a bootcamp, with the advantage that you can study at your convenience rather than having to be at your computer at a particular time. A further advantage is that, as a general rule, self-paced classes cost less than live ones. If your schedule can’t accommodate a live class, self-paced learning may be your only alternative, although it needs to be approached with some circumspection, as it comes with its fair share of pitfalls.

Data science is a fast-moving field in which discoveries are constantly being made. As a result, curricula can become outdated very quickly, and pre-recorded video tutorials can pass their expiration dates very quickly. Whereas a live class can always keep up-to-date with the latest trends in data science, self-paced courses don’t have that advantage, and may well have you learning things that are no longer current.

The other major drawback to a self-paced course of study is that you don’t have an instructor to whom you can address your questions. You can thus learn something completely wrong and have no one to set you right. Lacking a living, breathing instructor, you also will be without someone to offer encouragement and to nudge you to keep up with your studies. You’ll, therefore, need considerable sticktoitiveness if you’re going to complete a self-paced class. It’s very easy to lose interest, fall behind, and just not see yourself through the course.

If you do come through an asynchronous class, ticking all the boxes and finishing all your assignments, you’re not going to have a curated portfolio and job-search materials that have received the kind of input they need in such a competitive field. Finally, you’ll also not have a live bootcamp certificate (your bootcamp diploma in all but name) on your resume, meaning you’ll be asking the HR people considering you for a job to take your word that you succeeded in teaching yourself the fundamentals of data science

Of course, there are technical interviews that are designed to determine whether you have the knowledge you say you do, but you have to get to the technical interview in the first place, and a self-paced credential isn’t going to be your biggest help in that respect. Thus, ultimately, if you can manage a live bootcamp, you’ll likely have much better chances of breaking into the field.

Bootcamps Compared to Free Training Options

A final option for learning data science using the internet is availing yourself of the free training options available. These begin with YouTube, where you can find “Data Science in 5 Minutes” (clearly not going to get you a six-figure salary working as a Data Scientist), and, a bit more seriously, something that claims to be a complete Data Science Class that lasts ten hours. The last-named also happens to be four years old at the time of writing, which means that the caveats above about out-of-date self-paced learning apply. Although, in the best of all possible worlds, free knowledge that will get you a job in an in-demand field would be available, this, alas, isn’t such a world, and, although some of the free materials out there are good (such as sample classes from schools that go on to offer live bootcamps), you can’t put them together to obtain a complete data science education.

That said, free video tutorials aren’t to be wholly despised. The price certainly is right, and they offer an excellent means of getting a small taste of what data science is like. From these videos, you’ll be able to make the important decision as to whether you’re suited to data science in the first place. Free videos may even give you a head start when it comes to your first day of bootcamp. You very arguably should begin your data science journey without any outlay of cash. A little bit of knowledge is a dangerous thing, but, in this case, a free sample is a very good way to avoid investing in something you’ll end up not liking. 

Learn Data Science Skills with Noble Desktop

If you’re looking for a bootcamp that will give you the most information about data science and analytics in the smallest amount of time, you might look into Noble Dekstop’s Data Analytics Certificate. It’s an extensive program that goes into predictive and prescriptive data analysis, starting off by teaching essential statistical functions using Excel, then moving along to some theoretical essentials before taking a deep dive into Python and the many ways it can be used for data science. Standard Query Language (SQL) for extracting information from structured databases is also included, and the bootcamp wraps up with a unit on how to use Tableau to create data visualizations that will enable others to understand the fruits of your labors. 

The certificate program is available full-time (it takes upwards of a month to complete) or part-time in the evenings (it takes over six months to complete this way), and tuition includes an assortment of useful extras, including a free retake option (valid for a year), classroom recordings (to catch up on something that you may have missed), and, most valuable of all, over half a dozen 1-on-1 mentoring sessions with an experienced guide who can coach you in technical matters as well as prepare you for the job market with such things as resume review and portfolio curation.

Of somewhat briefer duration is Noble’s Data Science Certificate program, a nonetheless extended course of study designed, like the above certificate program, to prepare you for a career in data science. The curriculum is again based on Python for fundamental data science operations, data visualizations, and machine learning. The syllabus closely follows that of the Data Analytics Certificate, except that it omits the instruction in Excel and Tableau. This shaves about a week off the duration of the bootcamp, and is a good option if you already know your way around Excel. The bells and whistles described above are all included with this program, save for the fact that the number of mentoring sessions is an exact half-dozen.