Data Bootcamp vs. Data Degree: Which Is Best for You?

Will Hillier

So, you’ve decided to become a data analyst. Good call! Data analytics is a rapidly growing industry with plenty of options for career growth. In general, it pays pretty well, too, even for those new to the field. Of course, like any specialized career, data analytics requires a solid mix of soft skills and particular technical abilities. The next question then, is: What’s the best way to obtain the skills you need to land your dream job?

Historically, a college degree was the main way into a new job market. But this notion is increasingly outdated in the modern workplace. While college degrees certainly have value in the right time and place, the emergence of alternatives like data bootcamps offer a whole new route into the data analytics industry.

Whether you’re just starting out, or you’re taking a sideways career shift, this post looks at data bootcamps vs. data degrees, weighing up the pros and cons of each to help you decide which one might be right for you. Want to skip to a particular topic? Use the clickable menu:

  1. What skills do you need to land a job in data analytics?
  2. What is a data analytics degree?
  3. What is a data bootcamp?
  4. Pros and cons of a data analytics degree
  5. Pros and cons of a data bootcamp
  6. Degree or bootcamp: which one is right for you?
  7. Wrap up and further reading

Before we get started, it makes sense to explore what skills you need to land a job in data analytics. Let’s take a look.

1. What skills do you need to land a job in data analytics?

Like any technical role, the ideal data analyst has solid theoretical knowledge combined with real-world experience. However, so long as you can demonstrate the basic knowledge and skills, many employers will be flexible about your experience if you’re applying for an entry-level role. You will require a baseline, though. With this in mind, let’s look briefly at the basic technical abilities and soft skills necessary for landing your first data analytics job.

Necessary technical skills for data analytics

Required technical skills vary depending on the nature of the industry and the role itself. However, entry-level analysts of any kind require at least a basic grasp of the following:

  • MS Excel and its statistical functions.
  • Programming skills in Python (and potentially R, for statistical programming).
  • Basic SQL (Structured Query Language) for database communication.
  • Math and statistical knowledge, and related tools, e.g. SAS.
  • Data visualization theory and tools, e.g. Python libraries like matplotlib and Seaborn, or the proprietary software, Tableau.
  • Database and data warehousing, e.g. extract, transform, load (ETL), data modeling, and commonly associated systems.
  • Knowledge of data preparation, including data collection, data cleaning, quality control, storage, and data mining.
  • Machine learning expertise (for creating predictive algorithms).
  • Domain knowledge, i.e. an understanding of the industry or discipline you’ll be working in (you should at least demonstrate an interest in the area).
  • General project management skills.

Necessary soft skills for data analytics

While technical skills are vital for any data analytics role, employers also want to know you have the soft skills necessary for implementing them. These include things like:

  • Critical and creative thinking
  • Communication
  • Problem-solving
  • Attention to detail
  • Collaboration
  • Writing skills
  • Research
  • Presentation skills  

A solid portfolio

Most employers will understand that new starters don’t have a lot of practical experience. If this is the case, a portfolio is an excellent way of demonstrating that you can apply theory in practice. Your portfolio might contain personal projects where you collected data, carried out an analysis, and drew conclusions (ideally with some fancy visualizations). If this sounds like something you might be interested in, read more about how to build a data analytics portfolio.

OK, so we’ve covered the basics of what’s required to land a data analytics job. Now let’s explore two key ways you might gain these knowledge and skills…a data analytics degree or a data bootcamp.

2. What is a data analytics degree?

A data analytics degree is a formal academic qualification obtained from a college, university, or business school. A formal program of study, it’s taught by experts in the field. (Note: For this post, we’ll use ‘data analytics degree’ as shorthand for any college qualification that offers the skills you need to become a data analyst. This could be a Bachelor’s or Master’s in an area like computer science, statistics, applied math, information management, or another related subject.)

The outcome of a data degree is usually a formal diploma in your field of study. While a degree is a big cost and time investment (usually three to four years), it’s a clear way to indicate your passion for (and understanding of) data analytics. It will show employers that you’ve dedicated time and effort towards pursuing a career in this field.

While a traditional degree program is time-consuming, it gives you more in-depth knowledge of the subject. You’ll also have a level of flexibility over what you learn—as you progress, you’ll be able to choose elective modules to hone your knowledge in a particular area. An increasing number of institutions offer online study, too, which makes a formal data analytics degree a more flexible option than it once was. Next up…

3. What is a data bootcamp?

A data analytics bootcamp is a shorter, more focused course of study. A bootcamp can range in length from a week to a year. Although data analytics bootcamps are usually much more intensive than a degree, they also allow you to study in your own time. Bootcamps are also more career-focused than degree courses. For this reason, they’re sometimes offered by employers as a form of professional development or training.

Rather than a formal diploma, the outcome of a data bootcamp is a certification that shows employers you have the necessary prerequisite skills to work as an analyst. The greater value, though, comes to you personally: the best data bootcamps combine practical theory with one-to-one mentoring from an expert who works in the industry. By focusing on the core competencies you need to land a job, bootcamps offer a greater level of practical application than an academic degree. A good bootcamp will also support you with the associated aspects of job hunting, from completing a sample project, to building your portfolio, and preparing for interviews.

4. Pros and cons of a data analytics degree

Next, let’s dive a bit deeper by looking at some of the pros and cons of a data analytics degree.  

Pros of a data analytics degree

  • Expert instruction: Learn from lecturers with extensive academic experience in the field of data analytics.
     
  • Formal approach to learning: Courses are structured with lectures, seminars, exams, and formal, graded feedback.
     
  • In-depth knowledge: After a three or four-year degree, you’ll come away with unrivaled, in-depth knowledge of all aspects of data and statistical analysis.
     
  • Options to specialize: If you’re taking a Master’s, you can shape your own course of study. Even a Bachelor’s allows students to choose electives that interest them.
     
  • Develops soft skills: Longer-term study gives you more opportunity to collaborate with other students, honing those all-important soft skills.
     
  • Invaluable connections: Building relationships with tutors and lecturers over time means better connections to help you land a job when you graduate.
     
  • Possible placements: Depending on the nature of your data degree, you may get to apply your skills in the workplace, via a placement or apprenticeship program.
     
  • More specialist jobs: The more specialized your degree course, the more specialized the jobs you’ll find after graduation.
     
  • Better potential pay: With more specialized opportunities, the greater the potential to earn more money, too.

Cons of a data analytics degree

  • You have to apply: It sounds obvious, but to get onto a degree course, you have to apply…this involves a lot of hoop-jumping, with no guarantee of being accepted.
     
  • Expensive: Degrees don’t come cheap. They often include tuition fees as well as course texts and other hidden costs, meaning you’re likely to graduate in debt.
     
  • You might have to relocate: Formal degrees aren’t often available online. You may have to relocate to study your preferred data analytics course.
     
  • Material can be outdated: The rigid nature of larger academic institutions means it can take time for industry changes to trickle into degree programs. This is especially problematic in a field like data analytics, which is fast evolving.
     
  • Time investment: Even if a course is cutting edge when you start, it’s a big time investment—usually three or four years, or longer if you study part-time or for a Ph.D.
     
  • No job guarantee: Degrees don’t come with the promise of a job at the end… that said, qualified data analysts are in high demand, which should mitigate this risk.
     
  • It’s tough to change your mind: Once you’re tied into a degree course, it’s much harder to change your mind if you realize that it’s not for you after all.

5. Pros and cons of a data bootcamp

Next, here are some pros and cons of data analytics bootcamps. As with degree courses, each bootcamp varies, so be sure to check the specifics before you make any decisions.

Pros of a data bootcamp

  • Learn from industry professionals: Any good data bootcamp is taught by active data analysts with up-to-date experience of the industry.
     
  • Informal approach to learning: Unlike a degree, bootcamps are much more flexible. Learning is usually online, allowing you to fit it around other commitments.
     
  • Relatively affordable: Data bootcamps aren’t free (sadly!) but you’ll find the overall cost much cheaper than a degree course.
     
  • No hidden costs: Whatever a data bootcamp costs, you can be certain that everything is priced in: you’ll know what you’re paying, upfront.
     
  • Focus on practical skills: Learn hands-on how to use practical data analytics tools like Excel, Python, and SQL, as well as other technical skills that employers demand.
     
  • Career-focused: Beyond practical skills, a good bootcamp will ensure you are job-ready when you finish, with a solid portfolio and support for interviews.
     
  • Potential for scholarships: Your employer might be willing to pay the bootcamp fee for you, meaning you don’t have to fork out (although you’ll have to land the job first, of course!)

Cons of a data bootcamp

  • Intensive learning: While flexible, the intensity of a bootcamp can be stressful if you’re trying to juggle other responsibilities.
     
  • Isolated: some people find it hard to stay motivated without in-person support and workshops (although post-pandemic, we’re all experts at this by now!)
     
  • Certificates may hold less weight: Certain employers may insist on a formal qualification rather than a bootcamp certificate (although for entry-level jobs, this should be less of an issue). The key here is to make sure you’re studying with a reputable provider.
     
  • Quality can vary: Since most data bootcamps are private enterprises, they don’t always meet the more rigorous standards of formal qualifications. Be sure to do your research before committing.
     
  • Not all bootcamps offer job guarantees: You may have to invest a bit more for a data bootcamp with job-hunting support. It’s worth the investment, though, as you’ll have a higher chance of earning that money back.
     
  • Less customizable than a degree: Bootcamps are all about upskilling fast. The focus on career skills means you’ll have less chance to shape your own course of study. However, there should be room to explore when choosing your portfolio projects.

6. Data degree or bootcamp: Which one is right for you?

We’ve covered the pros and cons of data bootcamps and data degrees. Ultimately, though, one is not necessarily better than the other. The decision comes down to the type of job you’re seeking and which option fits your lifestyle. If you’re still not sure which one is right for you, here are a few thought-provoking questions to help. 

Do you actually require a degree for the job you want?

A degree is a big time and cost investment. If you have a specific data analytics job in mind, ask yourself: is there an alternative way in? Not all jobs require a data-specific degree. Sometimes you’ll find that any degree will do, as long as it’s accompanied by the right certification. This is especially the case for entry-level jobs. However, if you’re particularly keen to specialize—perhaps you’ve already worked in a related field and now want to progress into a data science role—you’ll likely need a more specialized degree to match, whether that’s a Master’s or Ph.D. in a data-related field.

What’s your motivation for studying?

If you’re interested in data analytics as a well-paid career path, there’s nothing wrong with that. For many, money is a great motivator. If this is the case, though, you’re better off investing in a bootcamp. This will get you up to speed and into the workplace more quickly, allowing you to start climbing that career ladder. However, if your motivation is pure passion and love for all things data-related, a degree might be the better option. Whatever your motivation, we would never recommend investing time and money in a three or four-year course unless you’re really passionate about the topic. And if you’re not sure yet? Start off with a data analytics bootcamp—you can always take your study further, later down the line. In the long-term, it’s not a binary choice.

What skills do you need to learn?

Another important factor to consider is what skills you need and whether the curriculum of any course you take covers them. So, let’s say you want to specialize in a given area of data science (e.g. you want to be a machine learning engineer or a data architect). You’ll first need to ensure the curriculum meets your specific criteria. For degree courses, especially, the curriculum will vary a lot between institutions and subjects. A computer science degree, for example, may not provide quite the same mathematical rigor as a degree in applied math. On the flip side, a degree in math is unlikely to impart the necessary programming skills. As for data analytics bootcamps, most focus on career competencies, but these are also open to interpretation. Do you want to learn Python or R? Are you more interested in deep learning or data viz? Nobody’s expecting you to decide your entire future career path right now, but it’s important to make as informed a judgment as possible.

Where do you want to be located?

A very practical issue: do you want to study from home at your own pace? Perhaps you’ve got other commitments or a job that won’t let you relocate? If so, a data analytics bootcamp could be the ideal option. But if you’re keen for a more immersive experience in a bricks-and-mortar institution, surrounded by other people and expert lecturers, a data analytics degree might be for you. Of course, these considerations aren’t mutually exclusive. Face-to-face training isn’t always better. Plus, some bootcamps offer in-person learning if you’d prefer that over online study. As ever, be sure to research before you commit!

How much do you have to spend?

Finally, and perhaps the biggest deciding factor: what’s your budget? The average degree course costs five or six figures, depending on the institution and what sort of fees you have to pay in your region. While you can split this amount over three or four years, with student loan support, it’s still a big investment. Intensive data bootcamps, meanwhile, tend to offer much more flexible pricing. While the cheapest courses can cost as little as a monthly gym membership (beware that the quality might reflect this), most fall in the four-figure range. For a shorter course, this might seem pricey, but the focus on job-readiness means you’re much more likely to earn this money back in a shorter space of time.

7. Wrap-up and further reading

Today we’ve looked at data bootcamp vs. data degrees, and which one might be right for you. We’ve explored the main difference between the two and some benefits and drawbacks of each. As you can hopefully see, there’s a place in the world for both degrees and bootcamps! It’s simply that each offers different opportunities for different people.

By now, you should be able to make an informed choice about whether a degree or a bootcamp is right for you. Regardless of which you choose, the main thing to remember is that a career in data analytics is a solid move: it’s a growing industry with plenty of career opportunities, and it pays well. Once you’ve landed that first job, there are many different paths you can choose.

To learn more about data analytics, try our free-five day data analytics short course, or check out the following posts for more data analytics related topics:

What You Should Do Now

  1. Get a hands-on introduction to data analytics with a free, 5-day data analytics short course.
  2. Take a deeper dive into the world of data analytics with our Intro to Data Analytics Course.
  3. Talk to a program advisor to discuss career change and find out if data analytics is right for you.
  4. Learn about our graduates, see their portfolio projects, and find out where they’re at now.