Intensive Bootcamps Can Get You Job-Ready and Lay the Foundation for a Degree in Data Science

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Strap on your combat boots and get ready to get smoked – whether you’re just getting started or looking to advance, a bootcamp is the fastest way to prime your neural pathways for data-intensive work and get up to speed on critical data science skills and disciplines.

It might not involve getting rousted out of bed for a pre-dawn, lung-burner of a forced run, but the use of the military nomenclature is no accident – a lot of information is being drilled into a lot of people in a short period of time, and the process is intensive and highly focused. No time is wasted and every detail is crucial. There simply is no faster way to get a practical introduction to the tools and methods data scientists use daily.

Rise of the Data Science Bootcamps

Coding bootcamps first appeared in the depths of the Great Recession, as laid-off workers looked to quickly revamp their skillsets for one of the few industries that was still hiring. But they really took off in 2014, when Kaplan, an established test-prep service and education provider, acquired Dev Bootcamp, a two-year old programming bootcamp startup. Kaplan’s confidence in the product ignited the industry. By December that year, there were more than 50 coding bootcamps in the United States.

At the same time bootcamps were proliferating, technology jobs were becoming more specialized. Inevitably, bootcamps started to open up to cater to the demands in these specialty fields. Fast forward to 2020, and there is at least 113 dedicated data science bootcamps worldwide… up from only 14 just five years earlier.

As their numbers expanded, so did the format. Bootcamps were originally short-term, all-day, and on-site. Now, you have all kinds of options, from all-online, to evenings and weekends, with terms as short as two days and as long as five months. There are data science bootcamps aimed at almost every skill level and focused on almost every tool and technique of the trade.

Bootcamps can be a great way to get the exact set of skills you need for employment in the field. One of the big selling points for many bootcamps is an integral job placement element. For many, bootcamps also serve as preparation for a graduate degree in data science, brining students with a background in fields like statistics and math current on all the coding language, modeling and analytic tools involved in tackling big data projects.

More and more, bootcamps are moving online. This opens up your options without having to think about commuting or relocating, and also helps keep the costs low. The general move to online coursework in the education industry received a big shove forward in the wake of the COVID-19 outbreak, so now even traditional on-site providers are offering online options that deliver the same curriculum and benefits.

Bootcamps for Every Specialist at Every Stage of Career Prep and Advancement

The proliferation of bootcamps has resulted in programs that have been tailored to fit almost any kind of objective, whether kickstarting a new career with very little previous knowledge and training, or building on your stats background with programming and data visualization chops that will make you a stronger candidate for an analytics position, or for a graduate program in data science.

High-School Graduates Looking for a Career in Data Science

Bootcamps originated as a way to quickly teach skills that were more current and more in-demand than colleges could keep up with, and some of them continue to fill that role today. Many bootcamps don’t have any formalized entry criteria, which means a clean interview and some self-taught coding skills could be all it takes to get you in the door. With a direct-to-employer career pipeline available from many, it is still a path you can take without having to earn a college degree that can get you into a six-figure position in months, not years.

Current Data Science Pros Looking to Skill-Up for a Promotion 

Data science is a broad field, and there’s a big gap between a SQL-monkey running queries on aged receivables for the accounting department and a high-end data scientist forecasting global economic impacts on the company by sifting through satellite data and mass aggregated cellphone location data.

If you are the SQL guy but want the skillset to turn yourself into the science guy, then a bootcamp offers a no-nonsense path to build on your current capabilities while giving you the extra analytical edge you need for the top jobs in the profession.

Programmers Looking to Shift into Data Science as a Career

Many technology professionals already have a perfectly lucrative career in IT, but can’t resist the attraction to the hottest new field in the industry (and the paychecks that come with it). A background in coding is a must to make that jump, but it’s not enough: the hard statistical analysis skills and data manipulation techniques used at-scale require specialized training. The right bootcamp can play off your current coding skills and give you exactly the tools you need in statistics and data analysis to begin earning your stripes as a data science professional.

Individuals Outside the Tech Industry Who Want to Learn Data Science

There are also some bootcamps that can help you take your first steps in data science even if you have almost no experience in technical or mathematics fields at all. These are a good fit for anyone looking to make a career transition, or even just to beef up their data science chops for an existing job in one of the many fields that’s exploring new ways to glean useful information from data.

But don’t kid yourself; building the kind of statistical and programming skills that are ultimately required in data science isn’t easy, and you should prepare for a stair-step approach if you are starting from scratch. You might have to go through several bootcamps, interspersed with periods of heavy self-study, college courses, or entry-level experience in between to ultimately get where you are heading.

Data Science Bootcamp Basics: Availability, Entry Requirements, Cost and Features

Naturally, you have to find the kind of bootcamp that caters to your personal goals. The laser-focus of the curriculum found in these programs means you’ll need to start by pinpointing the ones that provide the specific skills you’re looking to develop, then go on to dig into the ancillary details to find the right fit. The things everyone needs to consider typically include:

Availability and Scheduling

Bootcamps can be virtual or in a fixed physical location, but almost all of them take place during a fixed timespan. That’s a sharp contrast to traditional online courses, but the urgency is part of the point. Scheduling can be of paramount concern to participants who already have a full-time job. More and more, bootcamps are catering to exactly these kinds of students, though, by scheduling class time on evenings or weekends. Nonetheless, each bootcamp provider will usually only run a handful of camps each year, with a limited number of slots open.

Entry Requirements

Bootcamps designed to provide entry-level skills in data analytics have some basic entry requirements. In fact, anybody reading this page has likely already met them by simply getting through high school and turning 18. Others require candidates to have some rudimentary experience with a programming language.

On the other hand, some are designed as post-degree options for seasoned professionals and are extremely selective. These more advanced bootcamps will actually require a graduate degree in a related field and a certain number of years of experience.

In general, because data science is a hybrid field, bootcamp applicants will need to have some grounding in either math or computer science, or both, to be accepted into a program. This doesn’t mean formal training is required for entry-level and intermediate programs; a basic level of self-taught proficiency developed through personal interest will suffice in a lot of cases.

Cost

Bootcamps virtually always cost money, and often quite a lot: up to $20,000 for some of the more popular ones. But consider that the cost of a quality master’s degree can be three times that and 20k all of a sudden starts sounding pretty reasonable.

Some bootcamp “fellowships” may be offered free of charge to qualified candidates. The competition for getting into a fellowship programs is usually intense, but the pay off is big. Other bootcamps may offer financing options for students who cannot afford the entire amount in one chunk.

Portfolio Development

A particular class or group going through the bootcamp is often called a “cohort.” Most cohorts are expected to work together closely, in small groups, to complete practical projects throughout the term.

A capstone project is often undertaken as a sort of final exam and serves to provide graduates with a portfolio they can use to build their resumes and showcase their proficiency to either university admissions departments or hiring committees.

Job Placement

Most bootcamps have some sort of job placement assistance built right into the program, or available as a standard university resource in the case of programs offered through an established institute of higher learning.

Depending on the provider, this can take the form of a hiring day at the end of the course, regular meetings with a full-time career counselor, or engagements with corporate partners that come to the bootcamp to recruit talent and that expect to hire much of the graduating class. Some bootcamps even hire back their own graduates as instructors.

A Bootcamp for Every Data-Intensive Skill

Every data science bootcamp is unique, but as the field has matured, you’ll find different strains that offer different levels and types of preparation for highly specialized careers in data science.

Data Mining

These bootcamps often assume significant prior knowledge and experience in basic data manipulation and analysis, including common databases and toolsets, and are oriented toward current analytics professionals who want to upgrade their skills working with very large and diverse datasets. They concentrate on the unique tools like Hadoop and other NoSQL data stores and cloud-based storage and analytical solutions required for gleaning insights out of the largest datasets. They also teach the kind of thought process required for breaking down and performing statistically valid comparisons with that information.

Data Modeling and Analytics

You can think of these as sort of more like entry-level data science bootcamps, although in actuality they exist along a continuum of skill levels. They concentrate on the statistical science and tools used in performing basic analysis and creating visualizations to accurately present findings. You get the skills required to assess data from disparate sources and create valid conceptual models that can accurately analyze and quantify meaning from them.

 Machine Learning and Artificial Intelligence

These bootcamps are conceptually adjacent to data mining bootcamps, and often overlap in some places. They concentrate on building and training algorithms to handle data processing, often verging into other complex AI problems such as natural language processing or computer vision.

Programming

Coding is an art of its own, and some data science bootcamps concentrate on the distinctive languages and libraries that have evolved to handle data manipulation and analysis. Bootcamps teaching R, the de facto language of data science, and Python, the common tongue where data analysts and visualization professionals come together, offer a blend of programming and data analysis training.

The Curriculum You’ll Find in a Data Science Bootcamp

Your curriculum will vary based on the length and focus of the program. As the field of data science has grown, so has specialization in bootcamp programs. The most broad-based will cover each of these subjects. More narrowly focused camps may examine only a few, in greater depth. In other cases, you’ll find specific elements from different subjects combined to explore a particular application of the latest techniques, such as exploring statistical prediction through the lens of natural language processing.

Subjects can change quickly due to real-world advances and demands, but you can expect the curriculum to cover a mix of these topics:

Coding

Most bootcamps have some programming instruction to prime you for all the programming work involved in the projects and practice exercises you’ll be working on. Generally, however, you will be learning statistics-specific libraries and how to implement particular data functions in languages you are already expected to be familiar with.

Machine Learning and Artificial Intelligence

ML is a technique that has become synonymous with processing large data sets, so almost every bootcamp will cover the subject to some degree. It’s one aspect of artificial intelligence that is used heavily in data processing and analysis, but some programs go into broader AI concepts and techniques as well. 

Statistics and Prediction  

A solid underpinning in the statistical sciences is vital for anyone who wants to make a career in data science without being laughed at for making basic interpretive errors or outlandish predictions. Understanding statistical science and the math behind it is absolutely vital, and every bootcamp will cover these subjects to greater or lesser degrees. 

Data Visualization

Just crunching information has little practical utility if you don’t also have the ability to present it to decision-makers in a format they can intuitively understand. Visualization skills are a particular subset of data science that most data scientists need to understand, even if they don’t specialize in the subject.

Data Engineering  

Big data has produced its own unique challenges for storage and retrieval. These are all aspects of data science that are important for data scientists to understand, both for designing new and better systems and dealing with the kinds of engineering limitations that may impact real-world data projects. 

Databases and Data Languages  

Most bootcamps cover the general types of databases that are in common use today, even if they focus on only one or a handful for practical projects. You’ll learn about the differences between relational, graph, and hierarchical database storage designs among others, as well as their strengths and weaknesses in storing and accessing various types of data. And you’ll definitely get plenty of exposure to the data languages used to interact with them.

Tools

Most bootcamps spend a lot of time diving into specific data manipulation and analysis tools, libraries like NumPy, TensorFlow, and Keras, which offer the hammers, screwdrivers, saws, and nails that data scientists use to build real-world data solutions.

Bootcamps vs MOOCs: Battle of the Education Alternatives

MOOC stands for Massive Open Online Course, and many are offered that cover common data science subjects. But a MOOC is a vastly different approach to learning those skills than you’d find in a bootcamp.

MOOCs evolved primarily from college courses, offering the same instructor, materials, and class outline over the internet as regular matriculating students experience in person. Over time, MOOCs have taken on a less collegiate cast, but the principle remains: a MOOC is designed to teach a relatively narrow subject as a theoretical framework in preparation for further studies, rather than as a job-ready practical application.

MOOCs are a good solution for candidates who may be generally familiar with the principals and precepts behind data science programs, but who would benefit from a narrow, focused course of instruction in one or a few areas where their knowledge is lacking. MOOC participants must also be self-disciplined: none of the common social pressures are in place to encourage students to finish.

Bootcamps are a good fit for candidates who need a little external pressure to help them complete assignments, or who benefit from peer support and intensive one-on-one time with course instructors. A bootcamp tends to deliver up-to-date practical knowledge related to the tools and techniques actively in use in the industry, where a MOOC may offer more abstract knowledge.

A bootcamp is a significant time and cost commitment. MOOCs don’t hit your bank account quite as hard and are often self-paced. A bootcamp class generally cannot be rescheduled or set aside for a time and resumed later. The on-site nature of some bootcamps might also mean having to relocate, which carries its own costs, though for the time being, you’ll find all schools accommodating online learning as a result of the COVID-19 outbreak.

The limited number of slots available in a given cohort can also make scheduling difficult; students can’t count on being accepted to a particular class. MOOCs, on the other hand, have unlimited enrollment and are usually more flexible about start dates. But a bootcamp typically packs a lot more into the limited timespan. It could take years to go through an equivalent array of MOOCs to cover the same subjects as a 12-week bootcamp.

Bootcamps have a fixed curriculum; there is no chance of being able to string together only a particular set of subjects, which is something that can be done with some MOOCs.

Selecting a Bootcamp: Our Top Recommendations for University-Based and Independent Providers

With new bootcamps popping up all the time, Google could actually be more up-to-date than a lot of bootcamp aggregator sites you see out there, but all you’ll get from a web search is the generic results of your query staring blankly back at you: a long, unfiltered and undifferentiated list of options to pick through.

Data science is our business, and since we’ve already taken the time to distill those options down to the ones we think are actually worth considering, we can save you a lot of time and hassle.

When looking at bootcamps, you’ll find two basic provider categories: the OG-style options run by independent training providers carrying on the legacy of the originators that came on the scene nearly a decade ago … and the new generation of intensive bootcamp-style programs offered by some of the biggest names in higher education.

Universities Offering Data Science Bootcamps

Bootcamps originally came on the scene to offer a faster, more focused alternative to traditional master’s degrees or post-degree certificates offered at colleges and universities… but as it turns out, those colleges and universities are fully-equipped to deliver the same advantages you get from a bootcamp, plus a little something extra.

Bootcamps have never attempted to be anything other than what they are – highly focused, skill training that aligns precisely with industry demands. Still, they have had to work pretty hard to shake off the reputation that was slapped on them early on as somehow being a cut-corner version of a college education. But take the bootcamp model, run it through a major university, backed with all the resources, faculty experience and industry ties that come with it, and your reputation problem is suddenly solved.

After a little kick-start from the competition, universities have packaged content into bootcamp form to offer an alternative approach to standard degree programs. These programs offer the same focus and intensity of the original types of bootcamps, but with a deeper pool of academic talent to draw from, better funding and resources, and extensive industry connections. And with the funding to lead cutting edge research, bootcamps offered at America’s top universities are able to see around the corner and give students a better idea of what’s coming next in data science, not just what’s happening now.

Sizing Up Your Data Science Bootcamp Options

Even with a broad array of data science bootcamps already online, more are opening up every day as the demand for skilled-up data science professionals grows at a pace to match the growing troves of data being collected and processed every second of every day. What all these programs have in common is a general focus on data analysis and statistics… but beyond that, the differences can be significant. As you dig into your options, you’ll need to consider all the things that make a program suitable to what you’re looking for:

  • How long it takes to complete the program
  • Cost
  • Instructor background
  • Tools and languages taught
  • Location and scheduling
  • Job placement programs and rates

Because so many bootcamps are relatively recent startups, it can be difficult to learn enough about them to determine if they’re reputable and have a decent track record for student outcomes and satisfaction. Sticking with more established providers helps avoid this issue.

Look for a Small Cohort Size

With such enormous demand for bootcamp slots, some have begun to increase the size of the student cohorts accepted into each session. Although this increases the odds of acceptance, it also reduces the amount of individual attention that any particular student can expect from instructors.

In general, look for bootcamps with smaller cohorts, under 30 per class. If the class sizes are larger, at least make sure that the instructor to student ratio is still comparable to those of smaller classes.

Consider Online Bootcamps

The original bootcamps were all on-site, but online learning has just too many advantages to offer. More and more bootcamps are either going with entirely online courses or at least offering an online option. In most cases, unlike MOOCs, online bootcamps will require synchronous attendance, so you still have to attend during fixed hours.

Online bootcamps require a higher degree of motivation than the on-site version, but if you can bring your A game without someone looking over your shoulder all day, you can enjoy the flexibility and, often, the lower cost that online courses offer. You can get the same curriculum, access to instructors, and exposure to tools as you would with on-site courses, while avoiding the problem of having to temporarily relocate.

Check the Reviews 

Some independent reviews for different bootcamps are available on Course Report, a general coding bootcamp review website. Switchup is a similar service with a similar review feature.

Since many bootcamp providers run more than just data science courses, you can also check reviews of the school itself rather than their data science program specifically. You’ll get a good perspective on their administrative and support systems regardless of the subject.

General Google searches with the bootcamp name along with the search query, “review” may also be productive, as graduates sometimes blog about their experiences. Most bootcamps also publish blurbs from former students on their own websites, although these should of course be taken with a grain of salt.

Getting the Most Out of Your Bootcamp Experience

A lot happens during the average intensive bootcamp. Due to the nature of the hands-on, project-oriented pedagogy, it’s true that students won’t likely get a chance to apply everything they’re taught in real-world scenarios. And because many of the exercises are team-based, it’s entirely possible to spend less time on certain segments of the curriculum and more time on others.

You’ll need to be laser focused for the duration of the course. Cutting down on outside distractions can be critical to making sure you get the most out of the experience you’re paying for.

Get Prepared and You’ll Be Ready to Learn on Day One

It’s almost always a good idea to brush up on the programming languages that will be used as a basis for the program. Data science bootcamps, unlike coding bootcamps, are focused on advanced uses of coding skills, not the basics of the code itself, so instructors aren’t going to spend a lot of time helping you with your Python syntax. The more time you spend on remedial catch-up, the less you are learning of the data science lessons being taught.

Some bootcamps issue what they call pre-work, to help prepare candidates to hit the ground running. Don’t skip these assignments. When you start getting down into the weeds of your course content, you’ll be glad you had the primer.

Others, like Galvanize, have separate, for-pay preparation courses designed specifically to get you up to speed on the basics before you begin your immersive experience. If you’re not confident in your stats and coding skills, these can be a good investment.

Networking is Part of the Package

Bootcamps are often as much or more about making connections than about learning the source material. Most bootcamp programs offer job placement for graduates; some include so-called soft skill instruction during the course of the training, teaching basic interviewing skills and resume building.

It’s worth taking advantage of this aspect of the program. Be sure to participate in any career day or job fair events too, even if you’re not immediately seeking a position. A little elbow rubbing with people in the industry can go a long way, and could be the seed for everything from a job recommendation to a future promotion.

Location is Important, Even in an Online Program

Be aware that the location of the bootcamp may significantly impact networking options. If you are hoping to find a job in Silicon Valley, you’ll be meeting the wrong people (both in your cohort and at any career events) if you attend a bootcamp based out of Austin.

Earning Potential for Data Science Bootcamp Graduates

Data scientists fall into the Bureau of Labor Statistics category for Computer and Information Research Scientists. The 2019 median pay for the category was $122,840 per year.

That’s well above the already high-paying average for tech industry employees, but the news gets even better—that category includes a lot of academic and research jobs that don’t really fall into what the industry considers to be data science positions.

For a more nuanced perspective, consulting and staffing firm Robert Half offers some numbers in their 2020 Technology Salary Guide.  For the following job titles, they show much higher average salaries at the median, 75th and 95th percentiles:

  • Big Data Engineer – $163,250 / $193,750 / $172,750
  • AI Architect – $143,750 / $161,250 / $189,000
  • Data Architect – $141,250 / $163,500 / $193,500
  • Data Scientist – $125,250 / $152,000 / $180,250

The upper-tier salaries above the median are typically reserved for high-level contributors with a master’s or higher degree, but it let’s you know what you can shoot for if you’re thinking about going on to earn a degree. The median gives you a better sense of what to expect once you get yourself established post-bootcamp.

These numbers can vary both regionally and by industry. In Casper, Wyoming, for instance, a data scientist can make a respectable $102,830 per year at the median. Drop the same person in New York City and that number goes up to $175,976. And Burtchworks, an executive talent recruiting firm, found that data science and analytics professionals employed on the West Coast enjoyed the highest base salaries at all levels, with level 1 contributors, the ones likely to have completed a bootcamp in lieu of a degree, earning an average of $106,380, and $122,500 at the 75th percentile.

In such a mathematically intensive field, there can be big salary disparities based on education. Having a bootcamp under your belt without a degree puts you lower on the totem than high-level contributors with a master’s or higher degree, but it gets you in the door and a seat at a desk where you can start earning your keep and your reputation while working toward a master’s degree and the bigger salaries that come with it.

Employment Prospects After Completing a Data Science Bootcamp

Even now as the economy wakes back up after the long COVID-19 nap, the job market for data scientists is super strong. Data science today is at the same point of the arc that web design was in the late nineties… everyone needs what it can deliver, and there are very few people who can do it.

BLS data forecasts a 16% jump in employment by 2028, but that includes other research areas that probably depress the estimate. In fact, you almost need to be a data scientist to predict how big the demand is, and how much bigger it’s likely to get.

IBM, which counts some pretty big brains in the field among their employees, took a stab at the topic back in 2017 and estimated a 28% increase by 2020. Analysis of actual job listings by investment analysis firm Thinknum shows that they may have been a little short of the mark, but close enough for a projected estimate.

Moreover, Thinknum found that demand is tremendously diversified. Almost every sector has seen the number of openings rise, and almost every business is finding ways that big data can dramatically increase efficiency or profitability. Butchworks reported that the technology, telecom, and gaming sectors were the largest employers of data scientists, with financial services right behind.

The lag in supply far outweighs the pipeline, so graduates from data science bootcamps today at every level can expect a solid employment environment for the remainder of their careers.

Frequently Asked Questions About Data Science Bootcamps

Here’s some of the basic questions almost everybody has when mauling over the bootcamp option. We’ll keep this FAQ section updated as new questions come up.

Will I earn college credit in a data science bootcamp?

Generally, no. This is an entirely different type of education than college; although both can be effective and even complementary, a credit system simply wouldn’t apply to the kind of condensed short-course training you get from a data science bootcamp.

How long does it take to complete a data science bootcamp?

There are a wide range of timelines, but in general, you can expect camps to last roughly 12 weeks for full-time attendance and 24 weeks if part-time. But we feature camps on this page as short as one week and as long as nine months.

What are the admission requirements for data science bootcamps?

These requirements vary widely depending on the coursework and intent of the program. Some are for just getting the basics under your belt and require nothing more than a GED and that you be over 18 years of age… others are designed as post-degree programs that look primarily for candidates with a master’s, PhD, and several years of related industry experience.

Will my data science bootcamp help me land a job after graduation?

Most bootcamps either have a direct-to-employment pipeline with industry partners or offer some career counseling and placement assistance as part of their package. The ones offered through established colleges and universities give you access to all the resources any other student at the school would have, including career counseling and placement services.

Is an online data science bootcamp just as good as an on-site bootcamp?

This is a question that demands a very personal answer. Are you capable of maintaining your focus when no one is looking over your shoulder? Do you have a solid, quiet, multi-monitor workspace, fast internet connection, and powerful computer at home?

If the answer to these questions is yes, the course content and interaction available in online programs is every bit the equal, and even more efficient, than on-site bootcamps. But if you need that real-world kick in the pants to get motivated in the mornings, an on-site camp may still be your best option.

Is a bootcamp enough, or will I also need a college degree?

This can depend a lot on your industry and employer. For every honest-to-god data science job out there, there are dozens of former data analysts and data architects that had their titles changed to align with the inevitable buzzword flattening that’s happening around data science. These are data science jobs, to be sure; but they are not actually data scientist positions.

If you don’t expect the high-end salaries, you can easily get into these posts with only a bootcamp and possibly a high school diploma or bachelor’s degree. For the serious positions at the high end, some kind of formal statistical or math education is almost always necessary.

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