Now that we’ve clarified what exactly data science is, let’s dive into what you can expect from a data science bootcamp.
A data science bootcamp is generally a months-long, intensive course of study designed solely to prepare participants for a career as a data scientist. Accordingly, the focus is on practical, job-ready skills, with less emphasis placed on theoretical background or the broader education a bachelor’s degree would provide.
Oftentimes, data science coursework is supplemented with a capstone project and a careers-guidance component. The capstone project gives students the opportunity to apply what they have learned independently, with the final project serving as the first entry of the data science portfolio that many employers will want to see during the recruitment and hiring process.
Careers guidance can take many forms. Some bootcamps offer weekly one-on-one mentoring throughout the course of the program. Others have career services departments that can provide advice on everything from resumes and cover letters to interviewing skills. Some programs also curate jobs boards accessible only to bootcamp attendees.
Data science bootcamps will generally market themselves with appealing offers like job guarantees. While this may appear at first to be a risk-free way to break into data science, it’s important to always read the fine print: many times these guarantees require students to jump through hoops to demonstrate they’ve sufficiently completed a job search and will consider even a low-paying, part-time job placement enough to require a student pay in full. In the past, providers have also gotten around these guarantees by offering students job placements as instructors of the very bootcamp they’ve just graduated, though it appears this practice is beginning to subside.
Who are data science bootcamps for?
Data science bootcamp attendees vary widely in their backgrounds. Some might have existing backgrounds in STEM fields such as information technology (IT), data analytics, computer science, computer engineering or applied statistics, while others will come from the social sciences or humanities. While some might already have a bachelor’s degree, for many attendees a data science bootcamp is the first experience of post-secondary (post-high school) education.
Some programming experience or experience with statistics will sometimes be required as a prerequisite for attendance, but this is not essential. In fact, some programs even offer self-paced pre-course modules in these areas to help those with less experience hit the ground running.
Regardless of background, attendees of data science bootcamps are united in their goal: to make a quick but lasting change in their career without spending as much time and money as it would take to pursue a data science bachelor’s or master’s degree.
What core topics do data science bootcamps cover?
While there is certainly variation, the core curriculum of a data science bootcamp will be consistent across the board. Students can expect to cover the following:
Fundamental computer science skills
Either in self-paced prerequisite work or during the course itself, students learn how to write code using SQL, Python, and/or R programming languages. For more information, head to our data science programming language primer.
Statistical modeling
Students learn methods of statistical modeling and predictive analytics that allow them to develop and test hypotheses from data and use linear regression to make predictions.
Machine learning fundamentals
Instruction in machine learning fundamentals unsupervised learning, supervised learning, reinforcement learning, as well as more advanced topics like deep learning with neural networks, gives students the tools to design machine learning models to perform complicated analysis of big data sets much more quickly and efficiently than a human could.
Data management
Core data management skills taught in a bootcamp generally include web scraping, data cleaning, data mining, cloud computing with services like Amazon Web Services (AWS), and basic data engineering.
Data visualization
In a data science bootcamp, students generally practice their data visualization skills using Tableau, an industry-standard software that allows for the creation of elegant and informative dashboards, among other visualizations.
What are the different ways you can attend a data science bootcamp?
Most bootcamps last between 3 and 6 months, but students have a variety of options when it comes to how they study during this time.
Self-Paced Online
Self-paced online bootcamps offer pre-recorded learning content that can be accessed asynchronously, so attendees have the flexibility to study when it suits them and progress on their own schedule.
Live Online
Instead of pre-recorded videos, live online data science bootcamps feature real-time instruction from faculty through a video conferencing service. To encourage student-teacher and student-student interaction, these bootcamps are often cohort-based, meaning classes are capped at a relatively low number of students.
Hybrid Online & In-Person
Hybrid online & in-person data science bootcamps pair synchronous and/or asynchronous online instruction with in-person learning and events. Check out our in-depth guide to online data science bootcamps.
In-Person
The option most resembling traditional learning, an in-person bootcamp is just that: instruction that takes place in a classroom with a live instructor.
Is a data science bootcamp worth it for you?
In determining whether a data science bootcamp makes sense for you, take into consideration the associated costs as well as the potential outcomes and their likelihoods.
What are the associated costs with data science bootcamps?
Tuition
While bootcamp tuition won’t run you as much as tuition for a bachelor’s or master’s degree program, these programs still aren’t cheap. The average bootcamp cost $11,727 in 2020, and some data science bootcamps can cost as much as $18,000.
Temporary Relocation & Travel Costs
For hybrid or in-person bootcamps that don’t take place in your hometown, you’ll need to factor in the cost of commuting, or, if the bootcamp is far away, travel and lodging costs. For this reason, those in areas without available in-person options often go with an online bootcamp.
Lost potential income and/or other expenses
If you are considering a full-time bootcamp and currently working or a primary caregiver, keep in mind that learning full-time will mean that you won’t be pulling a salary and/or might need to pay for a babysitter or other caregiver.
What are the potential outcomes of a data science bootcamp?
For many, these costs are justified by the career prospects promised by bootcamp providers. Generally, those who successfully complete a reputable bootcamp are positioned to apply for entry-level data analyst and data scientist jobs.
Data analyst
Data analyst positions typically have a narrower scope of work and require more rudimentary skills, so they are often more realistic targets for bootcamp graduates. According to Salary.com, the median salary of a data analyst is $81,964. With time and experience, this median salary grows to $100,101 for a senior data analyst.
Data scientist
Data scientist positions usually require significant knowledge of machine learning and more advanced skills in coding and applied statistics, so they are tougher — but by no means impossible — targets for bootcamp grads. The median salary for a junior data scientist is significantly higher than that of a data analyst, $91,055. With time and experience, this gap widens: a senior data scientist can expect to earn between $124,021 and $153,409.
Head over to our article covering the differences in roles for data analysts vs data scientists.
How likely are the potential outcomes of a data science bootcamp?
Given just the associated costs and the potential outcomes, a data science bootcamp seems like a no brainer. For less than $20,000 (so the logic goes of bootcamp marketers) you can put yourself on track to earn at least $20,000 more than the average American every year going forward, and in some cases much more than this. But exactly how likely is it that you’ll land one of these jobs and set yourself on a data science career path after just 6 months of intensive study?
It’s difficult to arrive at an answer to this question for two reasons. First, it’s difficult to measure each bootcamp attendee’s pre-existing potential because factors like existing professional network, aptitude, and self-discipline resist easy quantification. (Though it would be a great problem for a data scientist…).
Second, outcome data from data science bootcamps is notoriously difficult to come by. While bootcamp providers list the companies graduates now work at (generally without specifying in what capacity), provide glowing testimonials from past students, and extol the many career resources they provide, they typically don’t list graduation percentages, job placement rates, or average salaries for graduates.
But this isn’t to say that there’s no data available. Some providers do perform internal audits and publish the data, as Flatiron School does with their 2021 Jobs Report, though this data isn’t specific to their data science programs. Other providers pay the Council on Integrity in Results Reporting to perform independent audits on their behalf.
We encourage you to peruse these reports for yourself to get an idea of how bootcamp students are faring on the job market, with the caveat that as the available data is so spotty, “an idea” is the most it can give. Giving the same caveat, here are some key insights we’ve taken away:
Completing a bootcamp does not guarantee immediate full-time, long-term employment in the field.
For one self-paced online data science bootcamp offered by Thinkful in 2018, less than 50% of a graduating class were employed in the field 3 months after the end of a program, and a quarter of graduates were still unemployed after 6 months.
Fewer than half (46.7%) of the 15 reported graduates of Galvanize’s 2019 Data Science Immersive were employed in the field as full-time employees after 180 days, with a further 26.7% employed as full-time contractors and 13.3% employed as short-term contractors.
Once employed, some graduates earn less than the 6-figure salaries prominently advertised in marketing materials, but for many, salaries are still substantial.
Graduates who find employment in the field are not always placed in data scientist or data analyst roles.
Two-thirds of the graduates of Galvanize’s 2019 Seattle Data Science Immersive were working as a data scientist or a senior data scientist. Other roles included data engineer and machine learning engineer.
None of the graduates of Codeup’s 2020 San Antonio Data Science bootcamp were working as data scientists and only 11.1% were working as data analysts within 180 days of graduating. Other job titles included business analyst and data management analyst.
What are some other factors to keep in mind?
In weighing the costs of a data science bootcamp against its potential benefits and their likelihood, there are several other factors to pay attention to:
Money-back guarantees and income-share agreements
Some bootcamps will give you your money back if you don’t land a job in the field within a certain time-frame, or will let you pay only once you’ve landed a job. For the latter, your payments will be calculated as a percentage of your salary and will cut off after you pay a certain multiple of the normal tuition, often 1.5x.
As we noted above, it’s important to make sure to read all of the fine print for either of these options. For money-back guarantees, bootcamp providers usually require that you complete the program exactly as prescribed and complete certain tasks during your job search. Failing to follow these requirements can leave you liable for the entire tuition. For income-share agreements, make sure you are aware and comfortable with the kind of job and compensation specified, and ensure that the payments cut off after a reasonable amount of time and money.
Financing
Many bootcamps also partner with private loan providers such as Ascent, Meritize, Upstart, or Climb Credit. Students can also seek out loans independently. As with money-back guarantees and income-share agreements, with these options make sure to pay attention to the fine print: in taking out loans, you aren’t just deferring your payment. And these aren’t student loans, they’re personal loans: interest rates can be quite high. Don’t be afraid to shop around, and make sure that you’ll be able to repay both interest and principal after graduating your bootcamp and landing a job before you commit to anything.
Quality of instruction
Even if you can’t get hard data on student outcomes for a particular program, you can still do your own audit of the quality of their instruction. Ask yourself the following questions:
Is the curriculum rigorous, and does it cover the basic skills required of an entry-level data analyst or data scientist?
Is the program sufficiently interactive? Will I practice the skills I learn through projects I can put in a portfolio? Will I have real-time interaction with my classmates and/or instructors?
Are the instructors sufficiently qualified? The mentors?
Are the alumni advertised on the website alumni of the program I am interested in? In what capacities are they currently working? What were their backgrounds before attending the program? Can I connect with them or other bootcamp alumni on Reddit, LinkedIn, or some other social network to learn more about their experiences with the program?
Some of these questions you’ll be able to answer by perusing a program’s website, but not all of them. In some cases, you’ll be required to enter your contact information to unlock things like student outcomes or data science course syllabi. If you do so, you’ll likely soon be inundated with calls, texts, and emails from a bootcamp’s marketing team.
Sometimes, these marketers can be quite pushy, but, if you can, try to see your contacts with them as opportunities: pose your questions, and if they can’t or won’t answer them, you can always terminate your exchange.
A data science bootcamp might be for you, but what if it isn’t?
If you come to the decision to pursue a data science bootcamp, congratulations! You can find our favorites over at our data science bootcamp guide.
If you decide that a data science bootcamp doesn’t make sense for you, but you’re still interested in data science, there are still options depending on your background.
Data Science Certificate Programs
If you are interested in gaining a better understanding of data science and starting to key skills in Python or R programming languages, but don’t want to commit time and money into a data science bootcamp just yet, a program like a data science graduate certificate or other short course might be right for you. Check out our guide for more information.
Data Science Bachelor’s Degree
While longer and more expensive than a bootcamp, a bachelor’s degree offers a far more substantial education and a more impressive credential.
Historically, the best majors for those wishing to eventually enter data science roles have been those that allow students to gain training in advanced mathematics, programming, business, or data analytics, such as computer science, mathematics, finance, or information technology, sometimes with relevant minors. Recently, however, more and more colleges and universities have been offering specific majors in data science.
Our list of best data science undergraduate colleges and universities has some ideas for programs that might work for you.
Data Science Master’s Degree
If you already have a bachelor’s degree, you should consider a data science master’s degree as an alternative to a bootcamp, especially if you are able to part with the money and time required to earn one.
While those with only a bootcamp certification or a bachelor’s degree may be able to gain promotion from an entry-level position to a mid-level data scientist role, frequently those interested in accelerating their progress down a data science career path will level-up their skills and gain exposure to new, more advanced areas of data science through graduate study. Accordingly, for those who are convinced data science is the field for them and already hold a bachelor’s degree, going straight into graduate school for a master’s data science program may help save money and time down the road.
Universities frequently offer full-time in-person master’s programs, but there are more and more part-time and online options for students looking for the flexibility to continue working or care for a loved one while they study.
If you’re interested in learning more about data science master’s degrees, dive deeper with our guide.
