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If you’re looking to get a job in data analytics and didn’t get a degree in computer science, data analytics, or math the first time around, you may be wondering about your options now. You can go back to school to get this degree, or try to complete an accredited data science course or bootcamp.
Both are pricey and time-consuming, but degrees are an order of magnitude more more pricey and time-consuming than most courses or training camps. Is this price worth it to employers? Let’s break it down by what each type of curriculum offers.
Time-honored Path: Data Science Degrees
The standard path is to earn a degree (or even two) in data analytics, computer science or mathematics. This type of structured training will teach you everything you need to know to do your data science job well.
One of the advantages of a diploma is that it allows you to learn the subject thoroughly and comprehensively. It offers real depth and a deep understanding of theoretical concepts that you won’t get in an intensive camp or online course.
Degrees cover a wide and detailed 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 technology large data, which has become more widely used in recent years.
The benefits of going so wide and so deep means you really understand the basics. You’re not just a coding monkey; you understand how and when to exploit specific statistical tools or perform specific analyses.
Not only that, but the degree matters. Many universities resemble brands that employers recognize and admire. For example, a job candidate with a math degree from MIT stands out favorably.
However, as I have already mentioned, studies usually last four years, although there are shorter and more focused options available. For example, if four years is too long, you can choose an accelerated program or a master’s degree specializing in data analytics, which typically takes one to two years.
These alternatives are like fast-tracking college degrees, offering a more focused curriculum that focuses on what you need on the job – data analytics, machine learning, and statistics skills. They can be an attractive alternative for people who have already graduated and want to start working in data analytics now, without having to spend four years on it.
The newfangled route: online courses and training camps
As you probably know, the field of data analytics is hearty and growing (no, no bubble). The number of graduates in these fields does not correspond to the number of job offers. This means that while getting a job without a degree is certainly not uncomplicated, it’s not impossible either – employers just want you to prove your skills.
One way to achieve this is through a combination of online courses, certificates, and boot camps. This path is more adaptable. You can even do it part-time, alongside your existing job.
Compared to standard degrees, the curriculum in these programs is more practical and designed to meet current labor market requirements. These include hands-on projects that introduce you to real-world data science work and teach 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 lecture halls.
Many bootcamps last only a few months and often end with some sort of internship offer. They’re pricey, sometimes running into the tens of thousands of dollars, but if they lend a hand you land a six-figure job in less than a year, they can provide a high return on your investment.
The problem is that this route does not give a complete, holistic picture. You may be able to fill your resume with great portfolio designs, but you’ll stumble during the interview because you’ll be asked a basic, foundational question that the bootcamp didn’t address.
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This is why a single bootcamp is not enough; you often have to supplement them by auditing (or paying for) Coursera or EdX courses, or participating in study, research and exercises.
Filling in the gaps
Associate degrees definitely offer unparalleled depth and prestige. However, the flexibility and practical skills you gain from boot camps and courses are not only a worthy alternative, but can actually make you better prepared for the job market. Studies, although time-honored, are also characterized by greater inertia – courses and camps can change the situation much faster in response to evolving labor markets than a diploma. Additionally, there is an emphasis on theory and less emphasis on skills such as interview preparation.
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That being said, if you choose a hybrid combination of course and bootcamp, you will miss out on the deep knowledge and content confidence you would gain by spending a year or more dedicated to the topic.
Fortunately, there are several resources we recommend that will lend a hand you bridge this gap and ensure you present yourself as a well-rounded, qualified candidate, whether you’ve chosen the degree or bootcamp route.
Learn more about data science
There are two ways to do this. First, you can look through data science degree curricula and make a list of everything you want to learn. Secondly, you can work backwards – choose your dream job offer and write down everything you want to learn in the requirements. Either way, make a list of the topics you want to learn.
With this list, you can exploit the following resources to complement your learning:
- Kursra AND 10th edition: If you don’t want to pay for the course, you can check out the course to learn the material, but you won’t get a certificate at the end. Coursera and edX offer plenty of comprehensive courses on theoretical and fundamental topics in data science and mathematics.
- Khan academy: Free classes, including college-level classes on topics such as statistics and probability.
- From OpenCourseWare: There’s no reason you shouldn’t exploit the MIT brand name! This is a valuable source of free lectures and training materials from MIT covering advanced topics in computer science and data analytics.
- Journals and academic articles: This may be a little esoteric, but reading scientific articles is a great way to really deepen your knowledge of advanced data science topics and, more importantly, stay ahead of current research trends. Some of them are paid, but many of them are available online for free. Begin from Google Scholar.
Practicing skills related to a given topic
As you know, it’s not enough to write “proficient in statistics” on your CV and hope for the best.
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You need to apply your practical data science skills, from coding to project execution, and have the projects to prove it. Here are some resources you can exploit to enhance your resume. Note: These may be particularly useful for candidates who have completed studies, as diplomas often provide fewer opportunities for practical projects than are typical of courses and training camps.
- DataCamp: Offers interactive courses focusing on practical skills such as programming, data analysis, and machine learning.
- GitHub: allows you to engage 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 with other novices, work on real-world problems, access datasets, and collaborate with a global community.
Arranging an interview
Regardless of whether you decided to go to college or bootcamp, you must pass an interview to get the job. You should prepare for a data science interview by focusing on both the technical aspects and the presentation of your work on the project. Here are some resources that can do this:
- StrataScratch: Have you ever wanted to know in advance what the interviewer will ask you? StrataScratch (which I founded) collects over 1,000 real-world interview questions, both coding and non-coding, as well as the best answers so you can practice and prepare for anything your interviewer might throw at you.
- Meetings and conferences: Contacts and networking cannot be overestimated. Attend them, in person or virtually, to learn about the latest trends, network with professionals, and perhaps even find mentors who can provide advice and insight on job interviews.
- LeetCode: Offers a wide collection of coding challenges and problems to improve your algorithmic and coding skills, necessary for job interviews.
- Glass door: Provides insight into company-specific interview questions and processes, as well as candidate feedback on their interview experiences.
Final thoughts
If you’re a beginner data scientist, the best thing you can do is summarize your position. If you have the time and money to devote to studies, this is a great option, as long as you supplement your deep theoretical knowledge with hands-on practice and job interview preparation. If you want to attend a boot camp or course tour, this becomes a more competitive option each year – just make sure you fully understand the concepts.
Both options are feasible, but one will probably be more suitable for you than the other. We hope this values guide will lend a hand you choose the right job for you while filling in the gaps needed to land your dream job.
Nate Rosidi is a data scientist and product strategist. He is also an adjunct professor of analytics and the founder of StrataScratch, a platform that helps data scientists prepare for job interviews using real interview questions from top companies. Nate writes about the latest career trends, gives interview advice, shares data science projects, and discusses everything related to SQL.