Data Science Degrees vs. Courses: The Value Verdict

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If you want to get a job in data science and you didn’t get a degree in computer science, data science, or mathematics the first time around, you might be looking at your options now. You could go back to school to get that degree, or you could try to complete an accredited data science course or bootcamp.

Both are expensive and time consuming, but degrees are an order of magnitude more expensive and time consuming than most courses or bootcamps. Is that price tag worth it to employers? Let’s break it down by what each kind of curriculum offers.

The Traditional Path: Data Science Degrees

The standard route is to get a degree (or even two) in data science, computer science, or mathematics. This kind of structured learning will teach you what you need to know to perform well at a data science job.

One of the benefits of a degree is that it lets you learn the subject solidly and comprehensively. It offers real depth and a profound understanding of theoretical concepts you wouldn’t get from an intensive bootcamp or online course.

Degrees cover a wide and deep range of themes, including topics like advanced mathematics, statistics, computer science fundamentals, data structures, algorithms, machine learning, data visualization, and perhaps even specialized areas like artificial intelligence, deep learning, and big data technologies which have become more applicable in recent years.

The benefit of going this broad as well as this deep means that you really understand the fundamentals. You’re not just a code monkey; you understand how and when to use specific statistical tools or run particular analyses.

Not only that, but a degree carries weight. Many universities are like name brands employers recognize and admire. A job candidate with a mathematics degree from MIT, for example, stands out in a positive way.

However, as I’ve mentioned, a degree is normally four years, though there are shorter, more focused options available. For instance, if four years is too long, you could opt for an accelerated program or a master's degree specializing in data science, which typically spans one to two years.

These alternatives are kind of like a speed-run of college degrees, offering a more concentrated curriculum with a focus on what you’d need in the job – data science, machine learning, and statistics skills. They can be an attractive alternative for folks who already graduated and want to pivot into data science jobs now without spending four years to do so.

The Modern Route: Online Courses and Bootcamps

As you may know, the data science field is robust and growing (nope, no bubble). The number of graduates in those fields is not matching up to the number of job openings. That means that while it’s certainly not easy to get a job without a degree, it’s also not impossible – employers just want you to prove your skills.

One way to do that is through a combination of online courses, certificates, and bootcamps. This path is more flexible. You can even do it part-time, alongside an existing job.

Compared to standard degrees, the curriculum in these programs is more practical,and designed with the current demands of the job market in mind. They include hands-on projects that approximate real data science work, teaching the specific skills that you might see on standard job descriptions, like proficiency in Python, R, machine learning algorithms, and data visualization tools. This approach can be especially useful for anyone who prefers direct application versus sitting in lecture halls.

Many bootcamps run for just a couple of months, often with some kind of job placement offer at the end. They are expensive, sometimes running into the tens of thousands of dollars, but if they can help you land a six-figure job in under a year, that can have a high ROI.

The problem is that this route doesn’t give you the whole, holistic picture. You might be able to stuff your resume with great portfolio projects but stumble in the interview because you get asked about a basic fundamental question the bootcamp didn’t cover.

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That’s why a single bootcamp isn’t enough; you often have to supplement it by auditing (or paying for) Coursera or EdX courses, or doing your own learning, research, and practice alongside.

Filling in the Gaps

Degrees definitely offer unmatched depth and prestige. But the agility and practical skills acquired through courses, and bootcamps are not only a worthy alternative but might actually make you better prepared for the job market. Degrees, while traditional, also have more inertia – courses and bootcamps can change things up much faster in response to evolving job markets than a degree can. Plus, the focus is on theory with less emphasis on skills like interview prep.

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That being said, if you choose to go for the course and bootcamp hybrid combo, you miss out on that deep knowledge and subject matter confidence you get from spending a year or longer dedicating yourself to a subject.

Luckily, there are a few resources we recommend that can help you round out that gap and make sure you present as a well-rounded, qualified candidate no matter if you went the degree route or the bootcamp direction.

Learning more about a data science topic

There are two ways you can go about this. One, you can look at curricula from data science degrees and make a list of everything you want to learn. Two, you can work backward – pick a dream job listing and write down everything in the job requirements you want to learn. Either way, compile a list of topics that you want to learn.

With that list, you can use the following resources to round out your learning: 

• Coursera and edX: If you don’t want to pony up for the course price, you can audit the course to learn the material, though you won’t get a certificate at the end. Coursera and edX offer tons of comprehensive courses on theoretical and foundational topics in data science and mathematics.
• Khan Academy: Free classes, including college-level classes for topics like statistics and probability.
• MIT OpenCourseWare: There’s no reason you shouldn’t take advantage of that MIT brand name, too! This is a valuable resource for free lectures and course materials from MIT, covering advanced topics in computer science and data science.
• Academic Journals and Papers: This may be a little esoteric, but reading research papers is a great way to really deepen your understanding of advanced data science topics and, more importantly, stay ahead of current research trends. Some are paywalled, but many are available online for free. Start with Google Scholar.

Practicing skills for a topic

As you know, it’s not enough to just say “proficient with statistics” on your resume and hope for the best.

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You need to apply your practical data science skills, from coding to project implementation, and have projects to prove it. Here are some resources to add extra polish to your resume. Note: these can be useful especially for degree-based candidates since degrees often have less opportunity for hands-on projects than courses and bootcamps typically do.

• DataCamp: Offers interactive courses focused on practical skills like programming, data analysis, and machine learning.
• GitHub: Lets you engage with real-world projects and collaborate with others to gain practical experience and demonstrate your coding and project management skills.
• Kaggle: Provides a platform to compete against other newbies, work on real-world problems, access datasets, and collaborate with a global community.

Nailing the Interview

Whether you opted for a degree or a bootcamp, you need to nail the interview to land the job. You should prepare for the data science job interview, focusing on both technical questions and showcasing your project work. Here are some resources to do that:

• StrataScratch: Ever wished you could know what the interviewer is going to ask you ahead of time? StrataScratch (which I founded) collects over 1000 real-world interview questions, both coding and non-coding, as well as the best answers, letting you practice and prep for anything an interviewer could throw at you.
• Meetups and Conferences: Connections and networking cannot be overstated. Attend these, either in-person or virtually, to learn about the latest trends, network with professionals, and possibly even find mentors who can provide advice and insights on interviewing.
• LeetCode: Offers a vast collection of coding challenges and problems to improve your algorithmic and coding skills, crucial for technical interviews.
• Glassdoor: Provides insights into company-specific interview questions and processes, as well as reviews from candidates about their interview experiences.

Final Thoughts

If you’re an aspiring data scientist, the best thing you can do is take stock of your position. If you have the time and money to set aside for a degree, that’s a great option, so long as you supplement your deep theoretical knowledge with hands-on practice and interview prep. If you need to go the bootcamp or course route, that’s becoming a more competitive option by the year – just make sure you fully grasp the concepts.

Both options are viable, but one will probably be a better fit for you than the other. Hopefully, this value guide helps you pick the right one for you and still fill in the gaps you need to land your dream job.

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. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.