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FAQs

General Questions

What is Metis?

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

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.

Where does Metis run its classes?
Singapore

Kaplan City Campus @ PoMo
1 Selegie Road, Level 6, PoMo Singapore 188306

Chicago

1033 West Van Buren, 3rd Floor, Chicago, IL, 60607
Metis is located in Chicago’s West Loop district

New York City

27 East 28th Street, 3rd Floor, New York, NY, 10016
(WeWork Nomad) in New York’s Flatiron district

San Francisco

149 New Montgomery St 2nd floor, San Francisco, CA 94105
in San Francisco's SoMa district

Seattle

51 University Street, Suite 300, Seattle, WA 98104
in Seattle’s Pike Place Market Area

Washington D.C.

2025 M St. NW, Washington, D.C., 20036
(Kaplan Test Prep)

Why focus on Data Science?

Our research shows a still-growing demand for qualified individuals for positions in the field of data science. This is largely 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 data-related job postings has surged and median salaries have risen as well, leading to “data scientist” becoming the best job in America in 2016 and 2017 according to Glassdoor.

    External data continues to validate the demand for the types of programmes Metis offers. For example:
  • By 2018, demand for deep analytical talent in the United States could be 50-60% than its projected supply (McKinsey)
  • IBM predicts demand for data scientists will grow by 28% by 2020 (IBM)
  • The median base salary for a data scientist is $110,000 (Glassdoor)
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.

Bootcamp Questions

Is this a full-time commitment?

Yes, the bootcamp requires a full-time commitment Monday through Friday from 9am-5pm.

Does this programme guarantee a job at the end?

Recruiters from our dedicated Placement Providers will actively reach out to current students and alumni with potential job openings, and accord priority to our students.

While Kaplan in Singapore will assist students with job placement, the onus remains on students to seek and secure employment.

However, we do guarantee to:

  • Work hard to provide you with a great data science learning experience.
  • Introduce you to companies who are looking to hire data science professionals.
  • Provide support and professional development.

Note: This programme is designed to prepare graduates to pursue entry-level employment in the field, or jobs in related fields, the specific job titles of which may not be represented in the programme title or described above.

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:

Statistics
  • [+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 = _____

Programming
  • [+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 = _____

Personality
  • [+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.

How is the bootcamp different from a computer science programme?

A computer science programme tends to be more broad and theoretical. Our 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 communicating results, working with deadlines, combating perfectionism, and setting 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 overall success this bootcamp. Students jump into solving problems directly, which requires the use of Python skills and statistics knowledge together. If it applies to you, you will certainly learn more in the area you’re currently less versed in, meaning you will graduate from the bootcamp feeling strong and qualified in both.

What tools are 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 comfortable 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?

Completing the pre-work is essential to obtaining the foundational knowledge necessary to successfully start the Metis Data Science Bootcamp and setup your software. Each student should expect to spend approximately 60 hours on tutorials as they become familiar with Python, take a Command Line Crash course, go through a number of package installation tutorials (i.e., Numpy, Scipy, pandas, Scikit.learn), and do some preliminary linear algebra and 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 bootcamp and hit the ground running.

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 after the bootcamp. What will I build throughout the programme?

You will complete five data science projects throughout the bootcamp. At the beginning, you follow along with the Senior Data Scientists 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, shareable results like blog posts, graphs, and/or reports, and you will conclude with a story of what the problem was, how you approached it and 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 data science experience and confidence, these projects provide you with stories and outcomes as a great way to demonstrate your abilities to potential employers.