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General Questions

What is Metis?

Metis accelerates the careers of data scientists by providing full-time immersive bootcamps, evening professional development courses, online training, and corporate programs. Courses are taught by expert data science practitioners, integrating project based learning with real data sets.

Where does Metis run its classes?

1033 West Van Buren, 3rd Floor, Chicago, IL, 60607


27 East 28th Street, 3rd Floor (WeWork Nomad) in New York’s Flatiron district.


633 Folsom Street, 6th Floor in San Francisco's SoMa district.


83 South King Street, Suite 250, Seattle, WA, 98104


Kaplan Test Prep 2025 M St NW, Washington, D.C.,

Is Metis accredited?

Yes. Kaplan, Inc. launched Metis in 2014 and has been awarded accreditation by the Accrediting Council for Continuing Education & Training (ACCET) for all Metis data science programming. ACCET has been recognized by the U.S. Department of Education since 1978, and Kaplan Test Prep, a division of Kaplan, Inc. has received back-to-back 5-year grants of recognition — the longest period provided to an accredited member school. The rigorous standards prescribed by ACCET include demanding review and approval of the Metis curriculum, of instructional personnel, of instructional delivery, and of admissions and student services.

Bootcamp Questions

What makes the Metis Bootcamp superior to other programs?

We believe we are different in several key ways:

Curriculum: We take an iterative, project-centered approach to our data science bootcamp curriculum. On each of the five projects, we teach students how to identify the problem, extract and clean data, analyze and interpret data, and communicate the results, both visually and orally in a presentation. Metis focuses on the full lifecycle of being a data scientist, rather than just teaching theory or just teaching tools and technologies.

Career Support: The Metis Careers Team offers valuable tools and coaching before, during, and after the bootcamp to help students achieve their individual goals. There are one-on-one meetings with your Career Advisor, exposure to industry experts via our in-class Speaker Series, workshops focused on resume writing, LinkedIn, salary negotiations, mock technical interviews with professional data scientists, and more. Our team provides personalized, ongoing support to each student until s/he secures a job.

Accreditation: Metis is an accredited data science bootcamp. In order to keep our courses running, we must adhere to certain regulations determined by the Accrediting Council for Continuing Education and Training (ACCET). Our future is directly tied to our ability to help students get jobs related to data science.

Is this a full-time commitment?

Yes. The program requires full-time commitment Monday through Friday from 9:30am-6pm.

Does this program guarantee a job at the end?

No, a job is not guaranteed. However, we do guarantee to:

  • Work insanely hard to provide you with a great data science learning experience.
  • Introduce you to many hiring partners who are looking to hire data science professionals.
  • Provide post-session support and professional development through our Careers Team.
What if I want to pursue a job that is not in the city where Metis runs classes?

While our hiring network is largest within our home cities of Chicago, New York City, Seattle and San Francisco—and therefore many local companies attend our Career Day events—we're also dedicated to helping those who wish to connect with companies based outside these areas. Our graduates currently work in 15 states and 7 countries.

Because Metis is backed by Kaplan, which has global reach, we have the ability to help introduce you to companies around the world. Our Career Advisors are 100% concerned with helping you find a job, not just in CHI, NYC, SEA and SF.

Does Metis provide living accommodations?

No, we do not provide living accommodations. We do partner with Common, which provides short-term collaborative living arrangements. If you want more information, please email us at [email protected]. There are also a number of other affordable short-term living options that you can research.

Does Metis provide visas?

International Students may attend the Metis Data Science Bootcamp in New York City or San Francisco on an M-1 Visa. Details here.

What are the payment options for the bootcamp?

Tuition: Tuition for the Metis Data Science Bootcamp is $16,000. A deposit of $1,500 is required within 7 days of acceptance to secure a spot. After making the deposit, students have the option of paying in three monthly installments.

Third Party Financing: Students accepted into Metis also have the option to explore financing through Skills Fund.

  • Borrow up to $24,000 for tuition and cost of living financing (less $1,500 deposit).
  • Metis students are offered one of the lowest interest rates among Skills Fund partners:
    • 36 Month Loan - 8.49% fixed interest rate for all approved students
    • 60 Month Loan - 10.49% fixed interest rate for all approved students
    • 4% Origination Fee
  • Select an interest-only or fully deferred type loan. Two-month repayment (interest + principal) grace period following program completion.
  • No income, employment, nor educational criteria used in underwriting.
  • Less than 10 minutes to complete your online application. Credit approval within minutes.
  • Scholarships can be applied towards financing at any time.

Scholarship: Women, members of underrepresented demographic groups*, members of the LGBTQ community, and/or veterans or members of the U.S. military are eligible to receive a $3,000 scholarship toward their Metis Data Science Bootcamp tuition.

* Underrepresented demographic groups include African Americans, Mexican-Americans, Native Americans (American Indians, Alaska Natives, and Native Hawaiians), Hispanic and Latino Americans, Pacific Islanders, and mainland Puerto Ricans.

Can I use 529 funds to pay the Metis tuition?

No. Although Metis is accredited by ACCET, we are not accredited as a Title IV organization, therefore we do not qualify for the use of these funds.

What is the application process like?

Admission into Metis is selective and is comprised of three phases. Prospective students must first submit a written application, which are reviewed by an Admissions Committee. To determine whether to advance a prospective student’s application into the next round of review, the Admissions Committee evaluates the applicant’s education and experience including (1) programming experience, (2) statistics experience, (3) effective communication skills, and (4) personality traits of curiosity, grit, and passion.

Prospective students who are advanced to the second round of screening are sent two challenges that include a technical assessment and a data science project challenge, which they have 48 hours to complete. Then, during a subsequent interview, conducted either in-person or online (e.g., Skype) by a Metis data scientist, the applicant presents their responses to the challenges (and the extra credit problems). While conducting each interview, the interviewer(s) evaluate, for a second time, an applicant’s demonstration of (1) programming experience,(2) statistics experience, (3) effective communication skills, (4) personality traits of curiosity, grit, and passion, as well as (5) motivation and (6) overall fit within Metis. If the Admissions Committee agrees that the person has the potential to succeed at Metis, the applicant is informed in writing of their acceptance into the program within two weeks of the interview.

Am I qualified for the Data Science bootcamp?

Not sure if your skills and experience are strong enough for the Data Science Bootcamp? Try scoring yourself using this brief self-assessment:

  • [+5] Have you ever performed quantitative or qualitative research?
  • [+5] Have you ever built a computational model of something?
  • [+2] Do you know what the Monty Hall problem is?
  • [+3] Can you explain why Monty Hall works?
  • [+2] Have you ever been bothered by a news story, report, figure, or infographic that had a flaw in its quantitative analysis?
  • [+2] Have you ever caught (or gone back to search for) an error in your work because the result you had seemed fishy?
  • [+2] Have you ever taken a stats class...and liked it?

Statistics total = _____

  • [+2] Have you ever written code to achieve a goal?
  • [+4] Have you written code to do something that no one assigned or paid you to do?
  • [+4] Have you ever written code to do something for you that you were asked to do by hand?
  • [+2] Have you ever experienced a "debugger's high" (bliss, excitement, or elation resulting from figuring out why your code isn't working)?
  • [+4] Could you name more than three data structures off the top of your head?

Programming total = _____

  • [+2] Have you ever been accused of being "obsessive" for the amount of effort you've put into finding a satisfactory answer to a seemingly unimportant or trivial question?
  • [+2] Have you ever said, "You know, I bet we could test that..." and then started designing a study or experiment? (Having done so in your head counts.)
  • [+2] Have you ever found yourself incidentally "interviewing" someone about what they do and how they do it?
  • [+2] Have you ever done the above in an unexpected environment, like at a party or a bar?
  • [+2] Have you ever taught someone about something just because it makes you feel good?

Personality total = _____

If you scored a six or greater in each of the above categories, you may be the kind of person we’re looking for. Of course, the bootcamp itself will be much more challenging, involved, and technical, but this assessment highlights the combination of skills, interests, and personality we think are necessary for a seriously considered application.

Is Data Science an in-demand occupation?

Metis’ research shows a growing demand of employers seeking qualified individuals for positions in the field of Data Science. This is driven by the exponential growth of available data (2.5 quintillion bytes of data are created daily and 90% of the world’s data was created in the past two years) and the narrow set of specific skills required to extract value from that data. As a result, the number of job postings has surged and median salaries have risen as well, leading to “data scientist” becoming the best job in America in 2016, according to Glassdoor. The median salary for a junior data scientist is $95,000 - $104,000, depending on geography.

External data continues to validate the demand for the types of programs Metis offers:

  • By 2018, the number of data science jobs in the United States alone will exceed 490,000, but there will be fewer than 200,000 available data scientists to fill these positions. Globally, demand for data scientists is projected to exceed supply by more than 50 percent by 2018. In addition, the US faces a shortage of 1.5 million managers and analysts to analyze big data and make decisions based on their findings. (McKinsey, 2011)
  • 75% of companies are investing in, or planning to invest in, big data in the next two years. (Gartner, 2015)
  • 99% of C-suite executives said analysis of data was important to their strategy next year. (KPMG, 2015)
  • According to a BurtchWorks flash survey conducted in the first quarter of 2013, 89 percent of responding data scientists said they were contacted via LinkedIn at least once a month with new job opportunities. Twenty-five percent said they were contacted weekly.
What is the difference between a Data Scientist, Data Analyst, and Data Miner?

We’ve certainly seen variation in regards to what employers have in mind when they use these terms, so please consider the answers below as general guidelines.

A Data Analyst is someone who creates and communicates insights from data to measure outcomes, make predictions, and guide business decisions. Often, there is a lighter coding burden placed upon someone with the title Data Analyst, though they may be expected to know certain languages or packages in R or python.

A Data Engineer is the designer, builder, and manager of the information or "big data" infrastructure. Each develops the architecture that helps analyze and process data in the way the organization needs it – and they make sure those systems are performing smoothly.

The term Data Scientist is used the most broadly. A job posting for a Data Scientist might describe a role identical to others calling for “data analyst,” though there is usually more diverse coding skills needed for a data scientist job. For the most part, data scientists are asked to participate in the entire cycle of problems and solutions. They help identify opportunities for companies to use data, while also finding, collecting, and integrating relevant data sources, performing analyses of varying degrees of complexity, writing code and creating tools that teams and businesses can use over time, and telling the story of what they’ve done to company stakeholders.

How is this different from a computer science program?

A computer science program tends to be more broad and theoretical. The Metis Data Science Bootcamp focuses on applications, so the computer science material covered within the bootcamp will be narrowly focused on topics in data structures, algorithms, input/output, and Python language that are pertinent to the data science workflow.

In addition, we cover topics in statistics and machine learning that are not computer science, per se. We also focus on the soft skills that make data scientists so valuable such as client interviews, communicating results, working with deadlines, perfectionism, and expectations.

Is it more important to have a background in Python or statistics?

Some experience in both programming and statistics are necessary. Further background in either one is helpful but not critical for this bootcamp. Students jump into solving problems directly, which requires the use of Python skills and statistics knowledge together. You will learn more in the area you’re currently weaker in (if that applies to you), meaning that you will graduate from the bootcamp feeling strong and qualified in both.

What tools will be used in the bootcamp?

Students build a strong foundation in the Python language, the unix/linux command line, machine learning packages such as scikit.learn, other statistics modules including scipy and pandas, web scraping packages like BeautifulSoup and Selenium WebDriver, PostgreSQL and mongodb databases, collaborative coding under version control with git, working on remote cloud servers such as rackspace, custom visualization tools, especially matplotlib and d3.js with support in HTML, CSS, JavaScript, and web hosting.

They will also learn about, and become familiar with, distributed algorithm frameworks such as Hadoop/MapReduce, system architectures with multiple servers of different roles, and web app frameworks such as Django.

What can I do to get ready before the bootcamp starts?

Each student will spend approximately 25 hours of minimum academic work with additional hours to get set-up, download software, and review introductory materials depending on the student’s level of programming experience, statistics background, and available time.

As part of the pre-work, we provide a Command Line Crash course; tutorials to become familiar with Python; a number of package installation tutorials (i.e., Numpy, Scipy, pandas, Scikit.learn); and some preliminary linear algebra statistics work.

The pre-work is intended to provide students with the essential background and foundational knowledge they’ll need in order to start the Metis Data Science bootcamp.

What do I need to bring to the bootcamp?

You will need your computer, your brain, and a readiness to learn. Your computer needs to run OS X and have at least 4GB RAM, 2GHz, and a 100 GB HD. Alternatively, if you are a Windows user and your computer is fairly powerful, you could run a Linux Virtual Machine inside your normal Windows install. This requires some configuration.

I want to have something concrete to show potential employers. What will I build during the course?

You will complete five data science projects throughout the bootcamp. At the beginning, you follow along with the instructors as they guide you, but as you learn more, your control over each project increases. You soon make your own choices when tackling data science problems, and with each project, you get concrete, sharable results like blog posts, graphs, and/or reports. For each of these projects, you will conclude with a story of what the problem was, how you approached it, how you solved it, and what the results look like. The final project is your passion project, during which you have full control from start to finish. Aside from giving you hands-on experience and confidence, these projects provide you with stories and outcomes as a great way to demonstrate your abilities to potential employers.

Have more questions?
Contact us.