Data Science Degrees vs. Courses: The Value Verdict

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If you want to get a job in data science and didn't get a degree in computer science, data science, or mathematics the first time, you may be considering 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 the degrees are an order of magnitude. further Expensive and more time-consuming than most courses or bootcamps. Is that price worth it to employers? Let's break it down by what each type of curriculum offers.

The Traditional Path: Data Science Degrees

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

One of the benefits of a degree is that it allows you to learn the subject in a solid and complete way. It offers real depth and a deep understanding of theoretical concepts that you wouldn't get from an intensive bootcamp or online course.

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

The benefit of being both broad and deep means you really understand the fundamentals. You're not just a coding monkey; you understand how and when to use specific statistical tools or run particular analyses.

Not only that, but a title has weight. Many universities are like well-known brands that employers recognize and admire. A job candidate with a degree in mathematics from MIT, for example, stands out positively.

However, as I mentioned, a degree typically lasts four years, although shorter, more focused options are available. For example, if four years is too much, you could opt for an accelerated program or a specialized master's degree in data science, which typically lasts between one and two years.

These alternatives are like a fast track to college degrees, offering a more concentrated curriculum with a focus on what you would need on the job: data science, machine learning, and statistical skills. They can be an attractive alternative for people who have already graduated and want to pursue data science jobs now without having to spend four years to do so.

The Modern Route: Online Courses and Bootcamps

As you may know, the field of data science is strong and growing (no, no bubble). The number of graduates in those fields does not match the number of vacant positions. That means that while it's not easy to get a job without a degree, it's not impossible either: employers just want you to demonstrate your skills.

One way to do this 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 of these programs is more practical and designed with current job market demands in mind. They include hands-on projects that approximate real data science work, teaching specific skills you might see in standard job descriptions, such as proficiency in Python, R, machine learning algorithms, and data visualization tools. This approach can be especially useful for anyone who prefers direct application rather than sitting in conference rooms.

Many bootcamps last only a couple of months, often with some type of job placement offer at the end. They are expensive, sometimes running into tens of thousands of dollars, but if they can help you land a six-figure job in less than a year, they can have a high return on investment.

The problem is that this route does not offer a complete and holistic vision. You may be able to fill your resume with great portfolio projects, but stumble in the interview because you're asked about a basic fundamental question that the bootcamp didn't cover.

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That's why a single bootcamp is not enough; you often have to supplement it by auditing (or paying for) Coursera or EdX courses, or doing your own learning, research and practice at the same time.

Filling the gaps

The titles definitely offer unmatched depth and prestige. But the agility and practical skills gained through courses and boot camps are not only a valuable alternative, they can also better prepare you for the job market. Degrees, while traditional, also have more inertia: courses and boot camps can change things much more quickly in response to changing job markets than a degree. Additionally, the focus is on theory with less emphasis on skills such as interview preparation.

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That said, if you choose to go for the hybrid course/bootcamp combination, you'll miss out on that in-depth knowledge and confidence in the subject that you gain from spending a year or more dedicating yourself to a topic.

Fortunately, we recommend a few resources that can help you close that gap and ensure you present yourself as a well-rounded and qualified candidate, regardless of whether you went the college route or the boot camp direction.

Learn more about a data science topic

There are two ways to do it. First, you can look at data science career curricula and make a list of everything you want to learn. Second, you can work the other way around: Choose your dream job opening and write everything you want to learn in the job requirements. Either way, compile a list of topics you want to learn.

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

• Coursera and edx: If you don't want to pay the course price, you can audit the course to learn the material, although you won't get a certificate at the end. Coursera and edX offer tons of comprehensive courses on theoretical and fundamental topics in data science and mathematics.
• khan academy: Free classes, including college-level classes for topics like statistics and probability.
• WITH OpenCourseWare: There's no reason you shouldn't take advantage of the MIT brand too! This is a valuable resource for free MIT lectures and course materials, covering advanced topics in computer science and data science.
• Academic journals and articles – This may seem a bit esoteric, but reading research articles is a great way to really deepen your understanding of advanced data science topics and, more importantly, stay ahead of research trends current. Some have a paywall, but many are available online for free. Begin with Academic google.

Practice skills for a topic.

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

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You must apply your practical data science skills, from coding to project implementation, and have projects to prove it. Here are some resources to add extra shine to your resume. Note: These can be especially useful for candidates with degrees, as degrees often have fewer opportunities for hands-on projects than courses and boot camps typically have.

• Data Camp: Offers interactive courses focused on practical skills such as programming, data analysis, and machine learning.
• GitHub: Allows you to participate in real-world projects and collaborate with others to gain hands-on experience and demonstrate your coding and project management skills.
• Kaggle: Provides a platform to compete against other newbies, work on real-world problems, access data sets, and collaborate with a global community.

Nailing the interview

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

• StrataScratch: Have you ever wanted to know in advance what the interviewer is going to ask you? StrataScratch (which I founded) collects over 1,000 real-world interview questions, both coded and uncoded, as well as best answers, allowing you to practice and prepare for anything an interviewer can throw at you.
• Meetings and Conferences: Connections and networking cannot be underestimated. Attend these, either in person or virtually, to learn about the latest trends, network with professionals, and possibly even find mentors who can give you interview tips and ideas.
• Leet code: Offers a wide collection of coding challenges and problems to improve your algorithmic and coding skills, crucial for technical interviews.
• Glass door: Provides information about company-specific interview questions and processes, as well as candidate reviews of their interview experiences.

Final thoughts

If you are an aspiring data scientist, the best thing you can do is evaluate your position. If you have the time and money to book a degree, that's a great option, as long as you complement your deep theoretical knowledge with hands-on practice and interview preparation. If you need to go the bootcamp or course route, that is becoming a more competitive option each year; just make sure you fully understand the concepts.

Both options are viable, but one will probably suit your needs better than the other. Hopefully, this values ​​guide will help you choose the right one for you and still fill the gaps you need to land your dream job.

twitter.com/StrataScratch” rel=”noopener”>twitter.com/StrataScratch” target=”_blank” rel=”noopener noreferrer”>Nate Rosidi He is a data scientist and in product strategy. He is also an adjunct professor of analytics and is the founder of StrataScratch, a platform that helps data scientists prepare for their interviews with real questions from top companies. Nate writes about the latest trends in the career market, provides interview tips, shares data science projects, and covers all things SQL.