Preparing for a Master’s in Data Science Through Intensive Bootcamps

Strap on your jump boots and get ready to get smoked: an intensive bootcamp is the fastest way to get your brain up to speed and ready for a career or further education in the field of data science.

Candidates aren’t rousted out of bed at dawn for a two-mile run and calisthenics at a data science bootcamp, but the use of the military nomenclature is deliberate: a lot of information is being drilled into a lot of people in a short period of time, and the process can be hectic and overwhelming. But there is no faster way to get a practical introduction to the tools and techniques data scientists use daily.

Along the way, participants meet people, both those already in the field and those hoping to enter it, and can make some good connections that might help with their own career aspirations.

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 preparation and educational materials firm, acquired Dev Bootcamp, a two-year old programming bootcamp startup. Kaplan’s confidence in the product ignited the industry. By December 2014, 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, and as of 2015, there were at least 14 bootcamps dedicated to data science, with more opening all the time.

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

Bootcamps can be a great way to get hands-on experience leading to immediate employment in the field—indeed, a selling point for many bootcamps is their integral job placement element. For many, bootcamps also serve as preparation for a graduate degree in data science.

The Basics: Availability, Entry Requirements, Cost and Features

Availability – Bootcamps can be virtual or in a fixed physical location, but almost all of them take place during a fixed timespan. Scheduling can be of paramount concern to participants who already have a full-time job. More and more bootcamps are catering to such students, though, scheduling times 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 – Most bootcamps have some basic entry requirements; some are extremely selective. Often, candidates will have to have some rudimentary experience with a programming language. More advanced bootcamps will actually require a degree in a related field or equivalent 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. More advanced bootcamps have more stringent educational prerequisites.

Cost – Bootcamps virtually always cost money, and often quite a lot of money: up to $16,000 for some of the more popular ones. But others, usually called “fellowships,” may be offered free of charge to qualified candidates. Competition for entry to fellowship programs is usually intense. 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 add to their resumes.

Job Placement – Most bootcamps have some sort of job placement assistance designed to help candidates find a position in the industry after graduation. This can range from holding a hiring day at the end of the course, to offering a full-time career counselor, to engaging corporate partners who expect to hire much of the graduating class. Some bootcamps hire back their own graduates as instructors.

Bootcamps vs MOOCs: Battle of the Education Alternatives

MOOC stands for Massive Open Online Course, and many are offered covering common data science subject matter. But a MOOC is a vastly different style of education from a bootcamp.

MOOCs evolved primarily from college courses, offering the same instructor, materials, and class outline over the Internet as regular matriculating students experienced 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 rather than as a 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, however. MOOCs are less costly and can often be completed at the student’s leisure. A bootcamp class generally cannot be rescheduled or set aside for a time and resumed later. The on-site nature of bootcamps also can require physical relocation, which carries its own costs. 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 usually more flexible starting dates. On the other hand, a bootcamp packs a lot of education into a 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 as can be done with MOOCs. Students who might already be experts in a particular aspect of the bootcamp curriculum will still have to sit through those lectures and complete those projects along with their cohort.

Where To Find Data Science Bootcamps: Top Programs and Aggregators

There is no comprehensive listing or search website for data science bootcamps. A good Google search, using the topics you are most interested in, the locations you are willing to attend at, along with the search term “data science bootcamp” may be your best bet. With new bootcamps popping up all the time, Google will be more up to date than any other online listing.

There are several established bootcamp providers across the country. They are generally located in cities or regions where the technology industry is strong: New York, the Bay Area, the Seattle region. And, of course, a number of schools now offer online bootcamps that can be taken anywhere, or hybrid programs that combine online learning with a relatively short in-person stint.


Galvanize is the current incarnation of the Zipfian Academy, one of the oldest data science bootcamp programs. With campuses in Austion, Denver, San Francisco, and Seattle, they are one of the more geographically diverse bootcamp operations.

  • Location: Denver, San Francisco, Seattle, Austin
  • Admission Requirements: Python, SQL, and basic statistics; tested by take-home assignment and two technical interviews
  • Cost: $16,000, financial aid available
  • Length: 12 weeks, full-time
  • Language: Python
  • Outcomes: Capstone portfolio project. Claims 94 percent job placement rate.

NYC Data Science Academy

Another of the original data science bootcamps, New York Data Science Academy is highly respected and has a very competitive entry process. The program is highly advanced, requiring a Master’s or PhD in a STEM (Science, Technology, Engineering, or Math) field for admission. However, the topics of study are equally advanced and graduates can expect substantial interest from high-paying employers.

  • Location: New York City
  • Admission Requirements: Masters, PhD or equivalent STEM experience
  • Cost: $16,000
  • Length: 12 weeks, full-time
  • Language: Python and R
  • Outcomes: Capstone portfolio project. No published job placement but extensive hiring partners and placement assistance


Metis is a data science-specific bootcamp company organized and owned by Kaplan. From the Kaplan connection, the school has obtained accreditation to offer continuing education credits, so is popular among established professionals looking to expand their career options.

  • Location: New York, Chicago, San Francisco
  • Admission Requirements: Prior programming experience, some statistics use
  • Cost: $14,000, scholarship and financing available
  • Length: 12 weeks, full-time, plus online preparation phase prior to start
  • Language: Python
  • Outcomes: Capstone portfolio project. Runs hiring bootcamp concurrently with data science course, teaching interviewing skills, resume writing, and offers career day.


Level is a program established by Northeastern University and has a more academic cast to it than many bootcamp programs. However, the university has also partnered with a number of private corporations, including Humana and Starbucks, and its capstone projects are genuine efforts that those companies will put into regular use– an added feature for the resume of graduates. Level also provides some flexibility by offering three different programs, each oriented toward students with various skill levels.

  • Location: Seattle, Boston, San Jose, Charlotte
  • Admission Requirements: Varies by course, interview required
  • Cost: $8000
  • Length: 8 weeks
  • Language: Varies by course
  • Outcomes: Real-world capstone project; access to Northeastern University’s Career Services

General Assembly

Founded in New York in 2011, General Assembly has since branched out to open campuses in 14 different cities around the globe, as well as establishing an online bootcamp presence. In addition to its broad reach, the school is unusual in that it offers a combination of full-time, on-site intensive programs with part-time and online courses.

  • Location: New York, Austin, San Francisco, Boston, Seattle, Atlanta, Chicago, Washington, Overseas and Online
  • Admission Requirements: Varies by course
  • Cost: $14,500 – $4000, depending on program
  • Length: 2-3 months, full or part time, depending on program
  • Language: Python
  • Outcomes: Offers job and career coaching, no placements

Sizing Up Your Options

There are a broad variety of data science bootcamps, with more opening up every day as the popularity of the field—and the demand for trained scientists—increases. They have in common a general focus on data analysis and statistics, but beyond that, the differences can be significant. You’ll want to look into the suitability of:

  • The length of the program
  • Cost
  • Instructor background
  • Tools and languages taught
  • Location and timing
  • Job placement programs and rates

Because so many bootcamps are relatively recent startups, it can be difficult to determine their track record for quality. Sticking with more established programs helps avoid this issue.

Check the Reviews

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

However, because data science bootcamps are relatively small, the sample sizes are correspondingly weak. You might only find reviews of the school itself rather than their particular data science program.

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.

Look for a Small Cohort Size

With such enormous demand for bootcamp slots, some bootcamps 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 to receive 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.

Getting the Most Out of Your Bootcamp

So much happens during the average intensive bootcamp that it is not uncommon for participants to come out on the other side of it feeling that they have missed something. 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 miss out on segments of the curriculum that other students end up learning.

Bootcamp attendance will demand your full attention for the length of the course. Cutting down on outside distractions can be critical to making sure you get the most out of the experience you are paying for.

Preparation Ensures You’re Ready to Learn on Day One

It’s almost always a good idea to brush up on knowledge of the 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 pre-work, to help prepare candidates to hit the ground running. Don’t skip these assignments.

Networking is Part of the Package

Bootcamps are often as much or more about making connections than about learning the source material. Many 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.

Make an effort to take advantage of this aspect of the program. Be sure to participate in any career day or job fair events, even if not immediately seeking a position. Getting to know people in the industry can generate everything from recommendations to future promotions.

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 located in Austin.

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