In our new, data-driven landscape, it’s useful for people who work in tech or those looking to switch to a different industry to consider upskilling on their own. Some data science bootcamps can help you fill knowledge gaps, but other programs teach basic fundamentals and build up to advanced topics. Use this guide to compare data science bootcamps and potential careers and salaries, and see if enrolling in a data science bootcamp might be worth it to you.

Data science bootcamps are a useful way to gain skills in data science quickly compared to earning a four-year degree. Bootcamps focus on marketable skills that may help you land an entry-level data scientist role quickly. However, curriculum, cost, and structure of data science bootcamps are some considerations to think about as you explore your options.

A four-year degree is useful, but typically comes with a higher price tag than a bootcamp. From 2018-2019, the average cost of a four-year degree was $28,123, according to data from the National Center for Education Statistics. Fully immersive online data science bootcamps may cost up to $18,000. Be sure to look into scholarships and other financial aid to help lower your costs. Other bootcamps may offer income-share agreements so there are no upfront costs—you only pay for the program if you land a job in data analytics or data science.

It is also important to consider your career goals along with other factors when assessing bootcamps. Are you interested in becoming a machine learning (ML) engineer? Try searching for bootcamps with instructors with real world experience in machine learning and programs that focus on teaching ML techniques. Ask the admissions teams questions about common career outcomes for their graduates. If they’re mostly working as ML engineers, that program may be worth it.

Another question you may want to ask yourself is how much time can you commit to the program? Some bootcamps offer part-time learning options so students can continue their education while working a full-time job. Even with a part-time bootcamp option, you will likely complete your studies before wrapping up the first year of a four-year degree, allowing you to explore new career options or earn a promotion in your current role.

Before you commit to a specific program, consider the bootcamp’s instructor and mentor quality, cohort makeup, curriculum, portfolio work, and outcome rates and support. Data science is skills-based, so you should be looking for instructors who have serious, hands-on experience in your preferred fields. You may want to look for bootcamps that focus on group learning and admit a full cohort of students with diverse backgrounds.

It’s also important to decide how much structure you want. Are you comfortable with self-paced learning? Or do you prefer deadlines, assignments and homework? If you’re new to data science, check out beginner and intermediate levels, which often include lectures and projects in fundamentals such as Python. If you’re looking at advanced fellowships, you’ll have more independence to focus on what you’d like to work on and build your portfolio.

For data science jobs, your portfolio is your real resume: Employers want to know what you’ve done, why you did it and how it’s original. With the ASI Data Fellowship, you have the chance to work with industry partners on real-world problems. A strong bootcamp will help you build a diverse portfolio that’s also geared toward what employers look for in their candidates.

Many boot camps have set themselves up as talent pipelines, funneling trained data scientists to eager partner companies. If you’re hoping to land a job after you graduate, look for programs that provide plenty of career training. For example, some programs set up mock interviews, company site visits and consulting projects. Others provide an in-house career coach and instruction in salary negotiations. You may also want to look for a bootcamp that provides post-graduation support, which sometimes includes alumni networking or emails with relevant jobs for recent graduates.

Most bootcamps advertise job placement rates (some boast rates of 100%), but these percentages are only part of the story. You may only receive job offers from the bootcamp’s partner companies, not end up in a “true” data scientist role, or wait three months before receiving an offer. It’s also possible the starting salary is lower than you expected. Take a careful look at what job placement entails and make sure the career they’re preparing you for is the career you want.

Where do you want to be in five years? Do you want to get a data science job straight out of bootcamp or do you just want a skill set that will allow you to work on independent projects? Knowing your goals will help you narrow your options. If you’re lost in a dead-end data role and not sure what your next step is, you might want to explore the uses of data science in different industries and check out our profiles of data science careers.

LinkedIn, Twitter, and meet-ups are a great place to start! Ask them what they do during a normal day. Get recommendations on skill sets (e.g., R vs. Python). Talk to them about your education options. You may not need a bootcamp to land a job. A few months with Coursera or other MOOCs and a good textbook could do the trick.

But don’t take them too much to heart. Sometimes, HR departments create a huge list of requirements into the skills section and hope for the best. When in doubt, ask your mentors what knowledge is critical to have.

This will help when it comes to deciding whether you need a part-time course in Python, a crash course in Hadoop, or a full three-month immersion in major data science technologies. Think about soft skills too. Do you need practice developing your own projects and public speaking? Do you need some experience leading a team?

Many bootcamp organizations list their alumni on the website, but you can also do a LinkedIn and Twitter search. Ask for an inside take on coursework, instructors, job preparation and career support after graduation.

That program fee is just the beginning; you’ll also need to think about food, transport, lost wages and accommodation. In a place like San Francisco or New York City, housing can be very expensive. Some bootcamps offer merit, need-based scholarships, and scholarships for women; be sure to ask about options.

You can start with our List of Data Science Bootcamps, but you may also want to do your own research. What kind of reputations do the instructors have? Can you commit to full-time work? Some bootcamps are highly selective; what are your realistic chances of getting in?

Let’s say you have a bachelor’s degree in quantitative science (or a related field) and you’re thinking of becoming a data scientist. Perhaps you’ve done a few online courses (e.g., Coursera, Udemy, etc.) and are ready to invest in more education. You may be considering four options: data science bootcamp, a data science bachelor’s degree, a data science master’s, or PhD in data science. Which one do you choose?

We don’t have the definitive answer. Unlike, say, medicine, there is no tried and true path to a career in data science. Some data scientists hold a PhD in statistics and have built up an arsenal of data tools; others have a B.S. and an incredible portfolio of projects. Some entrepreneurs have created startups after minimal time in an academic setting.

Data science is a broad field with a variety of roles that encompass the skills you may acquire in a data science bootcamp. O*NET OnLine projects job growth of at least 8% for data scientists from 2019 to 2029, higher than the average for all jobs . The median salary for data scientists in 2020 was $98,230 per year. Below are some related job titles:

Data Architect
Data architects develop the framework for data management systems. They create a vision for how data will flow through the business, define standards, and collaborate with multiple stakeholders to translate business specifications into technical solutions.

Data Engineer
Data engineers develop, test and maintain databases, data reservoirs and any other data management systems. They build out pipelines so their organization has easier access to raw data, ensuring optimized retrieval. Some data engineers are more focused on databases and work within data warehouses to develop table schemas.

Data Analyst
Data analysts uncover insights from large datasets to solve business problems. Similar to data scientists, data analysts commonly use programming languages such as R and SQL to retrieve and manipulate data. They also use statistical tools to interpret data and reveal trends.

Business Intelligence Analyst
Business intelligence combines data analytics with business acumen. A business intelligence analyst typically uses data from the company’s past performance to help management make informed decisions. They use a number of tools and techniques to pull data, identify trends and create reports to help guide the company’s strategy.

Quantitative Analyst
Quantitative analysts (quants) develop complex mathematical models that financial companies use to make decisions. Some quants have generalized knowledge, while others are experts in a specific area. Quants may research and analyze market trends to make modeling decisions, test new models, products and analytics programs. They may also collaborate with stakeholders on trading strategies, market dynamics and trading system performance.

Below is a list of data science bootcamps delivered online, in-person and hybrid.

Last updated: October 2021

This page includes information from O*NET OnLine by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA.