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Data Science

Yes, Data Science Bootcamps Are Still Worth It!

10 minute read | May 14, 2024
Laura Parker

Written by:
Laura Parker

Ready to launch your career?

Data science is one of the most in-demand skills, and for good reason; with the world drowning in data, companies desperately seek skilled professionals to help them stay afloat. But let’s face it: not everyone has the luxury of spending years in school to get a fancy data science degree. A great alternative to this is to pursue a data science bootcamp.

In this article, we’ll look at why data science bootcamps are worth it. We’ll talk about the benefits, challenges, and everything in between.

What Is a Data Science Bootcamp?

A data science bootcamp is a short-term, intensive training programs that help you develop some of the most industry-relevant skills (both technical and soft skills) you need to succeed in data science, all within a timeframe that typically ranges from a few weeks to several months.

One of the great things about data science bootcamps is their flexibility; if you prefer an in-person experience, a few programs offer in-class training at reputable institutions. On the other hand, if you like the convenience of online learning or a hybrid model that combines the best of both worlds, various formats can accommodate your needs. This makes it easier to balance your learning with your existing commitments.

You may be wondering, “Who enrolls in these bootcamps?” The answer is: People just like you! 

Many bootcamp participants already hold degrees in fields unrelated to data science and want to transition. At the same time, you might want to advance in your data science career and perfect your already-existing skills.

But what exactly will you learn in such a bootcamp? These courses prioritize hands-on experience through project-based learning. You’ll explore topics like Python programming, machine learning, statistics, and databases. These all help you build the perfect portfolio by the end of the bootcamp.

While bootcamps, compared to other types of courses, are shorter and often more affordable than traditional degree programs, they still usually require an upfront investment, both in terms of time and financial commitment.

Investment vs. Reward

The average cost of a data science bootcamp is around $15,000. While that might seem a significant investment, the potential return is far greater. Before looking into your personal ROI, let’s break down the costs further.

Tips to understand your financial commitment

Data science bootcamps are way more cost-effective than your traditional degree program, with some bootcamps even being free of cost (but usually not offering extra benefits like a data science certification).

The exact cost of a bootcamp varies depending on factors like the program’s format and its reputation—the instructors that teach the courses and the success stories of the alums. While in-person bootcamps can range from $5,000 to $18,000, budget-friendly options can cost between $4,000 to $10,000.

However, you need to consider other financial commitments besides the tuition itself. For example, you might need access to specific tech, such as a new computer, or buy and install specific software, as well as the potential income losses during the program’s duration if you cannot juggle study and work.

How to estimate your return on investment

Now, let’s talk about the potential payoff.

The average salary for an entry-level data scientist is $89,611 per year, a baseline for the financial benefit of entering this field after finishing college or completing your bootcamp. With more experience comes a bigger salary, with numbers reaching $108,758 for those with 4-6 years of experience.

To calculate your personal return on investment, you’ll want to evaluate your current financial situation, the total investment in the bootcamp (including tuition, additional costs, and potential lost income), and your expected salary after graduation.

What You Should Expect to Learn In A Bootcamp

The beauty of a bootcamp is that you’ll learn by doing, with plenty of support from instructors and peers along the way.

Regarding the curriculum, first and foremost, you’ll learn the basics of programming languages like Python, R, and SQL. These are the bread and butter of any data scientist’s skills, and you’ll get hands-on experience using them to organize, analyze, and visualize data. Also, on the technical side of the bootcamp, you’ll learn all about analytical tools like SAS and Hadoop and how to use them to tackle complex datasets, such as handling unstructured data and doing web scraping to gather your own data. You can also expect to learn AI and machine learning.

Of course, this is only possible with a solid foundation in mathematics and statistics.

Some beginner bootcamps give you introductory modules on these subjects, covering topics like probability, linear algebra, optimization (like going from A to B using gradient descent or linear programming), or multivariate calculus.

Employment Outcomes for Bootcamp Graduates

The ultimate goal of any educational program is to help you land a job in your chosen field. And when it comes to data science bootcamps, the news is excellent!

According to recent polls, 74-90% of data science bootcamp graduates find work within 180 days of completing their program. That’s a pretty impressive success rate, and it speaks to the high demand for data science skills in today’s job market.

Some data science bootcamps, such as Springboard, are so confident in their ability to help you find a job that they offer job placement guarantees or even tuition refunds if you don’t land a gig within a specific timeframe. And if that’s not enough to put your mind at ease, consider this: 99.8% of employers who have hired bootcamp graduates say they would do so again. 

So what kind of data science jobs can you expect to land after completing a data science bootcamp? There are many opportunities, but some typical roles include junior data scientist, machine learning engineer, data engineer, and data analyst. 

And here’s the cool part: you don’t necessarily need a computer science or statistics background to succeed in these roles. Bootcamp graduates come from all walks of life, from lawyers to marine biologists to marketing professionals. What matters most is your passion for data and your willingness to work hard to learn the skills needed to succeed.

How Employers View Data Science Bootcamps

The good news is that, in general, employers appreciate bootcamps. Let’s look at some concrete examples.

Companies like Facebook and Capgemini have hired graduates from data science bootcamps. These are big names in the tech and consulting industries, and their willingness to bring on bootcamp alums speaks volumes about the industry’s acceptance of these credentials.

Even tech giants like Google and Amazon have publicly endorsed bootcamps and recognized their graduates as equally prepared for jobs as those with traditional CS degrees. Why? Because bootcamp grads bring unique skills and experiences to the table. They’re often more focused on practical, real-world data science applications and bring diverse backgrounds and perspectives to their work.

Of course, it’s not all just sunshine and rainbows. Some employers have concerns about the level of knowledge and problem-solving skills of bootcamp graduates compared to those with traditional computer science degrees. And that’s a valid concern—after all, a 12-week bootcamp can’t cover everything a four-year degree can.

But here’s the thing: many bootcamps include input from industry partners (such as Coursera’s IBM Data Science Professional Certificate), and the curriculum aligns with real-world employers’ needs. Graduates passionate about data science will continue to learn and grow on the job, just like any other employee.

How to Select the Right Data Science Bootcamp

Choosing the right data science bootcamp is a big decision that should not be taken lightly. After all, you’re investing your time, money, and energy into this program, so you want to ensure it’s the right fit for you.

Think of your long-term carer goals. 

  1. Are you looking to break into the field of data science for the first time? 
  2. Are you hoping to level up your skills and pursue a promotion in your current job? 
  3. Are you looking to pivot into a new career path entirely? 

The answers to these questions will help guide you toward the right bootcamp for your needs.

Another critical factor to consider is a bootcamp’s track record of success. Look for programs with a strong reputation for producing job-ready graduates who land great roles in the industry. Read reviews and testimonials from past students, and feel free to reach out to alums to ask about their experiences.

Next, consider your learning style and preferences. See if you prefer a fast-paced, immersive environment or more of a self-paced approach. The same goes for what you’re comfortable with – an in-class bootcamp or learning from home.

Total immersion or flexible learning?

When choosing a data science bootcamp, one of the most significant decisions you’ll need is whether to opt for a full-time, immersive program or a more flexible, part-time option. Both approaches have pros and cons; your choice will depend on your circumstances and learning goals.

  • Full-time, in-person bootcamps require significant time and energy for the program. They usually require 6-8 hours per day in class, making it challenging or impossible to maintain a job or other commitments. However, the benefit of this approach is that you’ll be fully immersed in data science, with ample opportunities to collaborate with classmates, work on real-world projects, and receive personalized instructor attention.
  • A part-time bootcamp takes two to three times longer than a full-time program, but they offer the advantage of maintaining a job while pursuing education. Plus, you’ll still have the opportunity to network and be able to access campus resources.
  • Online bootcamps, meanwhile, offer the most flexibility, allowing you to complete coursework on your own schedule from the comfort of your home. These programs typically require around 40-60 hours of classroom time, plus additional hours for 

Finding a program that aligns with your learning style, career goals, and practical constraints is most important regardless of your chosen path. Take the time to research your options, talk to alums and instructors, and trust your instincts.

Aligning bootcamp choices with career ambitions

Now that you’ve decided what type of bootcamp you’re most interested in, it’s time to find the right program for your career ambitions.

What do you hope to achieve in your data science career

Are you looking to enter the field at an entry-level position, or are you a mid-career professional hoping to advance your skills? 

Who knows, maybe you even aspire to transition into a leadership role.

Once you have a clear picture of your career aspirations, it’s time to start evaluating bootcamp programs with a critical eye. Look for programs offering a curriculum and focus that aligns with your goals. 

If you’re just starting out, you should prioritize programs that offer a strong foundation in the fundamentals of data science. If you’re further along in your career, look for programs that provide more advanced or specialized training.

In either case, look at the gap between your current skill set and the skills you’ll need to achieve your desired career path. Be honest with yourself about where you’re starting from, and look for a bootcamp that can help you bridge that gap and acquire the specific knowledge and expertise you’ll need to succeed.

Maximizing Bootcamp Benefits: Strategies for Success

Enrolling in a data science bootcamp is a big commitment, and it’s not enough to simply show up and go through the motions. To maximize the benefits of your bootcamp experience, you’ll need to approach it with the right mindset and strategies for success.

Be prepared to work hard! Bootcamps are fast-paced, and you must be dedicated and focused to keep up with the curriculum.

Here’s how you can do that:

Engaging fully in the bootcamp journey

One key strategy for success is to engage in the bootcamp journey fully. Adopt a humble attitude and be willing to acknowledge when you’re struggling or confused. Be bold, ask questions, seek help from instructors and peers, and embrace the challenges of learning complex new skills.

Make sure to also focus on developing your problem-solving skills. Data science is all about tackling complex, real-world problems, and the more you can practice breaking down these problems and finding creative solutions, the better you’ll be for your future career.

To stand out, you could work on personal projects that allow you to apply what you’re learning practically. Also, take advantage of the personalized support and resources available during the bootcamp. Attend office hours with instructors, collaborate with classmates, and seek additional resources and tutorials.

Beyond the classroom: extracurricular efforts

While most of your learning will happen during class time and through assignments, you can also do much outside the classroom to maximize your bootcamp benefits.

One simple but effective strategy is to set aside a few hours each week to review previous lessons and concepts. This can help reinforce your understanding and prevent you from falling behind as the curriculum progresses.

Don’t postpone building your professional network and online presence. Consider creating a digital portfolio showcasing your data science projects and skills, and link to it on your LinkedIn profile.

Navigating the Job Market Post-Bootcamp

Congratulations, you’ve completed your data science bootcamp! Now it’s time to put your newfound skills to work and land that dream job. 

But where do you start? How do you make yourself stand out in a crowded job market?

Start by doing the following:

  • Build a LinkedIn profile that highlights your data science expertise. Include a professional headshot, a concise summary, and relevant projects and accomplishments.
  • Work on your experience section and emphasize transferable and data science-specific skills like machine learning, data visualization, and programming languages.
  • Connect with bootcamp classmates, instructors, and other data science professionals; join relevant groups and participate in industry discussions.
  • Use LinkedIn’s job search tools to find and apply for data science positions.
  • Seek mentorship and feedback from experienced professionals in the data science field to guide you through the job market and provide valuable insights on refining your profile, resume, and interview skills.

Always remember that landing your first data science role requires patience and persistence. Pursuing a bootcamp is a significant first step towards your career goals, but remember to keep learning, networking, and having the courage to take risks and seize opportunities as they come your way.

About Laura Parker

Laura is Springboard's Managing Editor. She lives in New York City, and is currently learning how to become a better cook and master her fear of heights.