Data scientist has ranked among the top jobs on the open market for the past decade, with no signs of an impending fall off. According to Fortune Education, the starting salary for many 2022 data science graduates can surpass $120,000. While not all companies offer such generous salaries—the mean annual wage for data scientists is around $110,000, according to the US Bureau of Labor Statistics (BLS)—top organizations like Meta and LinkedIn shell out considerable amounts to secure top talent.
While employers pay handsomely for in-demand data science talent, they're not willing to sacrifice competency simply to fill an open position; you have to be able to deliver the goods and then some. The job application process can be quite challenging, even for qualified candidates. You need demonstrable technical skills and years of work experience to be competitive for a well-paying data science position.
That means it's hard to cut corners in your education and still build a great data science career. Data analytics bootcamps or open courseware data science courses may seem worthwhile but may not qualify you for the job you seek. That doesn't make them worthless or even bad; on the contrary, bootcamps offer real value to the right candidate. Good bootcamps allow you to augment and improve your data science skill set, and picking the right one can significantly benefit your career.
This article explores the benefits and drawbacks of master's programs and bootcamps. It discusses:
This section provides an overview of the pros and cons of a master's versus a bootcamp.
Compared to a master's degree program (which can take two years or longer to complete), bootcamps are fast and relatively inexpensive. That's not to say they are cheap: the average coding bootcamp runs about $13,500.
Are they worth the investment? That depends on the bootcamp. Unfortunately, the Higher Learning Commission does not accredit data science bootcamps, so it's not always easy to sort out the most effective programs. University-affiliated bootcamps may seem the safer bet but there's no hard and fast rule here. Some for-profit bootcamps are great, some university-affiliated bootcamps are only so-so.
There's always open courseware, which has the benefit of costing nothing. According to a CNET article "Online coding bootcamps: 4 things no one tells you" the best students utilize free online resources to supplement their education. How much you benefit depends on your ability to self-teach and self-motivate.
According to an article from a University of Chicago blog, completing a bootcamp can be an excellent introduction to a data science career. Bootcamps can lead to entry-level positions, but a more likely outcome is they give the necessary data science skills to pursue a full master's degree. However, if your career goal is to become a data scientist, a bootcamp on its own won't qualify you.
Data scientists typically have at least a master's degree, and frequently a PhD. Becoming a data analyst may be more viable if you complete a bootcamp; these professionals can also earn good livings. According to Glassdoor, the average total pay for an entry-level data analyst is around $65,000. This isn't close to what a data scientist earns (around $125,000), but it's still quite good. An entry-level data engineer, another potential career for bootcamp graduates (who work hard), earns closer to $80,000.
Still, it isn't a given that you'll earn a data science job with a bootcamp. Several posters on a reddit thread discussed how difficult transitioning careers can be. Even with a bootcamp education, you'll likely need a master's degree or another form of higher education to compete for better jobs. According to the thread, bootcamp graduates who did land positions often had to supplement their education and work hard to get interviews.
Bootcamps may be more beneficial for experienced professionals who want to specialize their skill sets without completing an entire two-year graduate program. CIO lists its 15 favorite programs, including at least one geared towards people with a master's degree. Experienced professionals in this program graduate with more work samples and a refined skill set, helping them earn more money.
The obvious cons of data science master's degree programs are they can be expensive and time-consuming. According to Education Data Initiative, master's degrees typically cost between $30,000 and $120,000, with the average degree priced at $66,340. Top programs typically cost the most but also can carry more cachet with potential employers, leading to better jobs at higher salaries.
It's impossible to quantify the ROI of a program before you graduate and start your career, though schools do try. If you attend a top program, develop a focused skill set, and work hard, you'll likely earn more than someone who hasn't taken those steps. Successful data scientists earn back their education investment many times over.
Accredited master's programs teach far more than even the best entry-level bootcamps offer. Master's programs break coursework down into core and elective classes, covering a wide range of topics and preparing you for a variety of career options. Bootcamps, on the other hand, typically focus narrowly on one or two skills.
So, are master's degrees unequivocally better than bootcamps? That depends on your goals and where you go to school. Attending a top master's program improves your chances at landing a top job. If your goal is to dip your toe in the data science waters or learn enough to qualify for a data science degree, consider a bootcamp. Bootcamps are a stepping stone to a data science career, while master's degrees are a bridge.
A Master of Science in Data Science (MSDS) is a graduate degree that prepares you for careers like data scientist, big data architect, computer and information research scientist, and machine learning engineer. It covers the theory, skills, and knowledge required to excel in data management and interpretation. While you will learn some data science in programs focused on computer science, informatics, or cybersecurity, a data science master's provides the deepest dive.
Most full-time data science master’s programs take two years to complete, though some offer an accelerated track. Online programs are typically geared towards working students and offer many of the same opportunities as in-person degrees. Hybrid programs, which can be full or part-time, include both in-person and online courses. Part-time programs can take three or more years to complete.
Many master's programs look for experienced applicants or those with established programming skills. Even so, programs often admit inexperienced students, requiring them to complete bridge coursework in programming and mathematics (typically calculus and statistics) before beginning their master’s coursework. Other admissions requirements typically include submitting a resume, personal essay(s), bachelor's degree transcripts (showing a 3.0 GPA or higher), standardized test scores, and letters of recommendation.
A master's in data science covers algorithms, artificial intelligence, machine learning, and statistical modeling. Programs also cover data, of course, including data mining, data visualization, data warehousing, data analysis, and data structures.
Specializing allows you to further develop your skill set in a specific area through elective coursework. Top specializations include artificial intelligence, big data, and machine learning. Alternatively, you may explore a subject that isn't included in the core curriculum, such as computational finance or bioinformatics.
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