Are Data Science Bootcamps Worth It?

Yes, data science bootcamps and courses are an increasingly worthwhile investment. Because many organizations now value demonstrable skills and experience over mere credentialism, enrollment in data science bootcamps—with their emphasis on focused, hands-on, immersive learning—has surged.

Bootcamps and courses teaching data science and analytics skills have quickly grown in popularity because they offer the kind of focused, accelerated and immersive learning best suited to equip people for careers in data with the field-specific, job-ready skills they’ll need. Most importantly, being enrolled in a bootcamp means someone else is invested in your success, ready to offer support when you need it, provide feedback on your progress, resume and portfolio, and set your job search on the right track.

The best of these programs help students learn a tailored selection of field-specific languages and platforms that can open a number of doors on the job front:

• Python
• R
• SQL
• Hadoop
• Spark

They also provide hands-on experience with:

• Data collecting
• Data analysis
• Data visualization
• Statistical analysis
• Predictive analytics
• Programming

Data science bootcamps are short, immersive educational programs that promise to prepare graduates for entry-level positions in only three-to-six months of intensive study. Graduates come away armed with technical skills in data visualization, data analysis, predictive analytics, statistical analysis, and programming.

Data science makes use of predictive causal analytics, prescriptive analytics, and machine learning to help us make predictions, and more importantly, decisions. To put it even more simply: it uses math and technology to find hidden patterns (and ways to be more productive and profitable) in raw data.

So Data Scientists spend a lot of time collecting, cleaning, modeling, and examining data, from numerous angles (some of which have not been looked at before).

Data science bootcamps help students learn a variety of languages and frameworks to accomplish that, including Python, Pandas, Hadoop, R, SQL, and Spark. Shorter than traditional degree programs, data science bootcamps also usually offer more opportunities for hands-on learning than most post-secondary educational programs.

These programs provide many benefits, including networking opportunities, up-to-date curricula, and ultimately the potential to land high-paying Data Scientist jobs after a short three-to-six month course.

A Data Scientist spends significant time collecting, extracting, cleaning, modeling, and analyzing data before using an array of techniques to come to meaningful conclusions, including predictive causal analytics (or predicting the possibilities of an event in the future), prescriptive analytics (conjuring a range of actions and the related outcomes), and machine learning.

To prepare students for that, data science bootcamps teach students to learn a wide variety of languages and frameworks, including Python, Pandas, Java, Scala, Hadoop, R, SQL, Julia, MATLAB, and Spark. Some of those newer languages aren’t widely known and would give a job-seeker a leg up on the competition. Unlike traditional colleges, bootcamps can be quick on their feet in responding to a constantly shifting industry.

And it’s become standard for networking to be a focal point of life while attending a bootcamp. Schools hold networking events, they bring in guest speakers from top tech companies, and most instructors are well-connected industry pros. Not only that, but the students around you will be aspiring tech professionals too, and they’ll help you form the beginnings of your professional network in the data field.

While there are certainly many potential benefits to attending a bootcamp, it’s important to be realistic.

Here are the pros of the bootcamp learning experience:

Perhaps the best selling point is that you will be up and running and job-ready incredibly quickly, especially if you were to compare it to a traditional college degree (even more so if you tack on any post-graduate work at the end of that). Within three to six months, you can be ready to ace an interview for an entry-level position.

When you consider that the average entry-level Data Scientist brings home roughly $85,000 a year according to PayScale, it’s clear why that’s a persuasive proposition.

One major selling point is the number of networking options it can provide. Most schools hold networking fairs, invite guest speakers from top tech titans to campus, host graduate project showcases, and have faculty who are pros in the industry with wide networks of contacts.

Your fellow students will also soon be important contacts as they too venture out into the job hunt.

Data Scientist has been called the most promising career by LinkedIn and the best job in America by Glassdoor for a reason. Right now, demand – and salaries – are high and poised to rise.

MIT research found that companies in the top third of their industry in the use of data-driven decision-making were five percent more productive and six percent more profitable than their competitors. Consider that data science is a relatively young field and many companies have been slow to truly realize the potential insights and income that they could reap with an investment in data.

Here, however, are the cons:

Data science is a big field, what type of job you’re looking for will have a huge impact on whether it might be best to go to a bootcamp, get a master’s degree, or try other online learning resources.

For the field of machine learning, data science bootcamps can be the perfect fit, teaching you all the programming languages needed to build and implement models.

Sometimes, however, a bootcamp might not be the right choice. For work in research, you might need a graduate degree. The same might be true if you’re aiming to work in the financial sector.

Have a look at some job postings for positions you would find desirable. See whether or not they require an advanced degree. That could help you make your decision.

While they pale in comparison to the cost of higher education in the United States, Data science bootcamps aren’t cheap. Even putting aside the tuition – let’s say $15,000 – and the cost of any required tech (a laptop?), you have to factor in the lost income from being in a full-time program for 12 weeks.

You can soften that blow by applying for scholarships and exploring which payment options the school offers.

The cost varies from school to school, but you can expect to pay roughly $15,000 for an intensive, in-person data science program.

While most of the best-known programs start around $15,000, some schools also offer cheaper part-time or self-paced options that cost anywhere from $4,000 to $10,000.

If cost is an issue, have a look at which scholarships are available. Many institutions have scholarships aimed at women, veterans, and other groups underrepresented in tech. There are also employer scholarships, where your workplace foots the bill for your tuition. Most bootcamps also offer flexible or monthly payment plans.

If you’re concerned about making things work financially, reach out to a school representative and ask for a breakdown of what you can expect to pay and what scholarships you might be eligible for.

A bootcamp can be the right path for career changers who want to acquire an in-demand new skill in a hurry, but if you’re not sure it’s the right direction to take your career, you can calculate your return on investment (ROI).

First, take a look at your current financial situation and jot down your monthly income after taxes and your current expenses.

Next, look at the total time and money you’d be investing in this program. Calculate the cost of tuition, the time it will take to graduate, your cost of living while you take the course, the cost of financing your tuition (if applicable), and any other upfront costs – like a new computer.

Finally, let’s get realistic about your post-graduate expectations. What salary do you expect to make? Although the average salary for a Data Scientist is $123,000 in the U.S., let’s be conservative and put in a lower figure. Then factor in expected income taxes and the amount of time you expect it will take to find a job placement.

Then, you just need to weigh the total investment against the difference in your expected income after taxes.

According to PayScale, the average salary for an entry-level Data Scientist is just over $85,000 per year.

As you climb the ladder in your career, you can expect that number to climb significantly. The average Data Scientist in the United States brings home $123,000 with Senior Data Scientists earning an average salary north of $150,000.

The earning potential for bootcamp grads is quite high, given that entry-level Data Scientists make an average salary of $85,000 and seasoned industry veterans make much, much more.

Given that data is in its relative youth as a field, Data Scientists with a lot of experience are rare and they’re bringing home salaries to reflect that.

Tuition for in-person programs can range anywhere from $5,000 to $18,000. Some schools charge less for self-paced options. Others change more or less based on how thorough a program the student wants.

If you’re wanting to avoid the sticker shock of a hefty tuition bill upfront, most of the best-known schools offer monthly payment plans or other flexible options. To offset the cost of tuition, most bootcamps offer generous scholarships aimed at groups of people who are underrepresented in tech. You could also explore employer scholarships, where employers cover the cost of tuition.

Chances are, the bootcamp you’re eyeing has people to help you get a handle on what your expenses will be. Remember not to forget that tuition isn’t the only expense of a bootcamp. Check into whether the program has any other required or recommended costs (books, a new computer, any programs, etc.)

Yes, it is very likely to help you get a job, with the vast majority of graduates from data science bootcamps reporting that they have found work in the field. BrainStation, for instance, reports that more than 95 percent of its data science bootcamp graduates found work within 180 days, with alumni landing jobs at top companies, including Google, Microsoft, Amazon, and Facebook.

Here are a few more job titles you might have after completing a data science bootcamp:

• Data Engineer
• Machine Learning Engineer
• Big Data Analyst
• Business Analyst
• Database Administrator

That said, it’s worth noting that there are people who complete bootcamps and fail to find jobs in the industry. It’s not necessarily an easy thing to master and perhaps not everyone is cut out to be a Data Scientist.

Yes, graduates of data science bootcamps are actually getting jobs in droves, as employers who have been starved for data talent are scooping up alumni not long after they graduate.

That demand for data professionals has created an environment where it’s quite rare for a bootcamp graduate not to get a job. Check the outcome report from any well-respected school and it should reflect that.

To make sure you find a job after, you should apply yourself as much as possible during the course and, upon graduation, lean on your newly formed professional network as you look for an entry-level job.

At a well-respected data science bootcamp, you will learn your craft under the tutelage of respected industry pros. It’s crucial that you seek their constructive criticism as you go through the course building models and crafting visualizations. Most graduates of data science bootcamps say their interactions with faculty were one of their favorite parts of the course, and making the most of that opportunity to learn from someone in the know is an important step in ensuring you get the results you want.

The same goes for your classmates. View them as future data professionals and colleagues and take the opportunity to build a professional network. This includes attending networking events.

You could also consider internships and apprenticeships, which are often a gateway to a job. But make sure to keep your LinkedIn profile up-to-date so recruiters can find you.

Speaking of LinkedIn, it can be a good strategy to send a friendly message to a data professional working at a company you’re interested in. Offer to take them out for coffee or lunch so you can pick their brain; you’d be surprised how many will say yes.

Employers view data science bootcamps very favorably, believing bootcamp alumni to be motivated, dedicated, and trained on the most current possible systems, techniques, and platforms. You’re also showing them that you’re a committed learner, a very attractive quality in an industry that changes rapidly. That said, not all programs have an equally positive reputation among employers, and your success on the job front will be impacted by the reputation of the school and the quality of the projects a candidate can show them.

Most bootcamp alumni find that employers are eager to engage even if a data science bootcamp is the only thing on your resume. That’s why such an overwhelmingly high percentage of bootcamp grads report getting jobs within three-to-six months; when it comes to data science skills, it’s a seller’s market and employers are competing to hire the most talented Data Scientists.

That’s largely because those who graduate from data science bootcamps usually complete projects over the course of their program that they can show potential employers to prove their talent. Further, many institutions have relationships with top tech companies, which often sponsor scholarships and events.

There’s also the fact that almost every tech company already has a bootcamp alumni working for them.

But it is important to make sure you select the right bootcamp, one with a proven track record of producing skilled, job-ready grads. Check outcome reports or review sites like Course Report or SwitchUp to see what grads and students are saying. If you’re bold, contact a couple hiring managers and ask directly what they think of the bootcamp you’re considering.

Yes, a data bootcamp is worth it, but your success does depend on the strength of the school, your dedication level(both learning and networking), and your background and past experience.

If you attend a bootcamp that has a strong reputation for turning out skilled grads, gives you the chance to work on at least one live project, and helps you build your professional network through things like networking events and other methods, you would be a strong candidate for an entry-level position.

Over the course of your bootcamp, you would learn to build and implement machine learning models, become familiar with a variety of programming languages (including Python, R, Ruby, JavaScript, C++, and Java), and find out how to make striking visualizations.

Those are the skills employers are looking for in most data science positions, and if you can land a data science job after taking only a 10 to 16-week course, it would definitely be worth it to most, depending where you are in your career now.

Another reason most grads seem to feel a data science bootcamp is a worthwhile investment? It’s a wonderful field to work in at the moment. It was forecast that the field would grow by 28 percent in 2020, equivalent to about 2.7 million new jobs. That’s more openings than new graduates will be able to fill—meaning tech workers in other fields will have to brush up their skills and transition into data to meet this demand.

In fact, our BrainStation Digital Skills Survey suggests this is already happening. Roughly four out of five data pros began their career doing something else, and around two-thirds of all Data Scientists have been working in the field for five years or less.

But there are other factors to consider when looking at whether a data science bootcamp is worth it.

Where you study makes a big difference, so do your homework on any program you’re considering. See where their alumni have wound up – are they the types of companies and roles you’d covet? And ultimately, succeeding in a data science bootcamp depends entirely on how you apply yourself while taking the course. Work and network hard if you want to hit the ground running upon graduation.

That said, we should mention that there are some highly technical roles within data science where an advanced university degree in math, statistics or computer science would be required. Check out job postings for the types of roles you’d be interested in and see what their requirements are to get a better sense of what to expect.

When it comes to bootcamps, your success will ultimately boil down to the effort you put, your approach, and your level of dedication. Here are our tips to making a data science camp worth it.

• Do your homework and choose wisely. While employers do covet bootcamp graduates, not every school has a sterling reputation. Before you begin your bootcamp journey, you’ll want to make sure that the program you’re considering is held in high regard. Read online reviews, contact current students or past graduates, or reach out to a recruiter or hiring manager in data science for their opinion on the best programs and schools. Review the bootcamp’s curriculum and prerequisites in detail. If it’s an in-person course, take a campus tour or check out a virtual tour. And if possible, read the bootcamp’s outcomes report to see how their grads are doing.
• Get yourself out there and network. One thing bootcamp alumni consistently rave about is the networking opportunities presented by on-campus (and virtual) networking events, not to mention the guest speakers from top tech companies who visit the classrooms of the best data science bootcamps. Your classmates could be future colleagues too, so those bonds you’re forming could prove valuable. That’s true of your instructors too. At a good bootcamp, you’ll learn from industry leaders with vast professional networks. Take this opportunity to impress them.
• Start working on live projects right away. One reason such an overwhelming number of data science bootcamp alumni get hired soon after graduating is that they’re able to work on live projects over the course of their programs that they can later show employers to prove they know what they’re doing.
• Seek feedback. As we mentioned, a worthwhile data science bootcamp will have instructors with plenty of experience doing what you hope to do professionally. Take their opinions on your projects and visualizations very seriously; a potential employer is likely to see the same things they see. Having the ear of an industry pro is one of the best parts of attending a bootcamp, so be sure to take advantage of that.

Before you decide which data science bootcamp is right for you, you have to do some self-reflection. What are your goals and what is the level of time commitment you’re comfortable with?

First, let’s consider which delivery option might work best for you:

When you think “bootcamp,” this is probably what comes to mind. This would be an immersive, focused program where you would spend anywhere from 40 to 80 hours a week in class while devoting some of your own time to working on your projects. The benefits of this model? There’s no quicker way to reach your goals. The downside? Juggling a job could be hard or in some cases, impossible.

There’s probably a tendency to believe that these courses are easier. They’re not. Most full-time online data science bootcamps will still require 40-60 hours per week of classroom time, while you’ll need to use evenings and weekends to complete your coursework. Don’t go in expecting to coast.

This could be a nice compromise for those not comfortable committing to a full-time schedule. You still get some of the benefits of attending an in-person course – perhaps better networking opportunities, the ability to attend events on campus, and, at a good bootcamp at least, top of the line equipment that you can use after hours. Of course, there’s a downside – you won’t be up and running as a Data Scientist quite as quickly. Most part-time courses take two-to-three times as long to complete compared to the full-time courses.

For the most possible flexibility, you can take a flexible online course. This option might be most palatable for those people who are already employed and simply looking to upskill. But like the in-person programs, it will take longer to complete the course – especially if it’s self-paced.

To find which data science bootcamp is right for you, you need to decide what’s most important to you. To do that, we recommend asking yourself the following four questions:

Knowing which bootcamp is right for you has a lot do with knowing yourself and where you are in your career. Are you a total newbie to data science and tech more broadly or have you worked in similar roles before? (If you have worked in similar roles, or you have a background in tech, you can expect a higher salary upon graduation.) Do you have a university degree in math, statistics, or computer science? Are you a coding novice? Do you have any experience with data modeling, creating visualizations, or using Python, R, or C++, for instance?

If you are a total beginner and your goal is to become a Data Scientist in a matter of months, you’ll really want to find a proven, immersive, challenging bootcamp to help you reach your goals.

With the demand for data professionals and all the buzz about Data Scientist being one of the best jobs in the United States right now, data science bootcamps are popping up all over the country. While it’s good that more people have access to this type of education, it also means that there’s a wide variance in the quality level of each program. Before you choose where to study, do your homework. Any reputable school should have been reviewed plenty of times on sites like Course Report or SwitchUp. It’s also nice to hear things straight from the horse’s mouth, so to speak – with a little poking around on LinkedIn, you’ll surely find some chatty alumni who would be happy to discuss their experience with you. If you’re too shy for that, at least have a look at the profiles of some alumni to see how they’ve done since graduation.

This is also a good time to probe the different schools’ curricula. Do you have specific goals, like say, mastering a tool like Jupyter Notebooks or Anaconda? If you can’t figure it out from looking at a course schedule, almost all bootcamps have representatives who are more than happy to answer any question you might have.

Let’s be clear: we know data science bootcamps aren’t cheap.

Tuition for in-person bootcamps can range anywhere from $5,000 to $18,000, but the most respected bootcamps mostly charge around $15,000. That’s a hefty bill, especially if you’re planning on taking a couple of months away from work as you complete the course.

Simply put, you might not be able to afford it.

But most bootcamps will do everything they can to make it work for their students financially. Most have flexible options, where you can make regular payments instead of one huge lump sum. There are lots of scholarships available – especially if you’re a member of a group that is underrepresented in tech – including employer scholarships, where your work foots the bill.

Again, it’s a good time to reach out to a bootcamp rep and see what you might be able to do to make the financial side a bit easier for you.