Tag Archives: NYC

Open Data is Important For Cities

Ben Wellington gives an excellent Ted Talk on open data. He argues that cities need to make more of an effort to release data in a standardized and machine-readable format. This could help cities be safer and fiscally responsible. He is hoping New York City sets the standards for open data for cities. As a bonus, he is a wonderful story teller.


3 Questions with Metis: A Data Science Bootcamp

Do you need to build your skills to land that dream job as a data scientist? Well, Metis Data Science Bootcamp in New York City offers a 12 week program to do just that. The program sounds fantastic with a few of the highlights being: help landing a job, great guest speakers, no PhD required, and a strong curriculum. Tarlin Ray, Head of Admissions at Metis, was kind enough to answer 3 questions about the bootcamp.

If you are interested in applying, the next bootcamp starts in April and the deadline for early admission is next Monday, February 16, 2015.

metis bootcamp
Metis data science bootcamp in NYC

Can you describe a student who would be successful in the program?

If students do not have the technical skills, then they will struggle in the Metis Data Science bootcamp. Students must have some programming background and experience with statistics in order to get the most out of the bootcamp. We do not set a minimum score for what that means, rather we use our application, coding challenge and interview to asses someone’s skill level. The technical skills are definitely a determinant of success but really serve as one piece of the equation. Students with strong verbal and written communication skills will be very successful in the program. A student must be adept at listening to instructors, fellow students and even speakers in the program. This skill will enable the student, as a data scientist, to get to the heart of an issue and to create a question or set of questions to help uncover insights. Students should have an innate curiosity. The thirst for the answer (1+1=2) is often only half of what a data scientist is tasked to do. The other half is the search for the why something is the way it is. Students should not be afraid to tap into their creative sides. The output for many data science projects is a visual representation of numbers, statistics and business insight. Students need to feel comfortable with a blank canvas and muddy data with the goal of creating a new work of art, digestible by a wide audience. Lastly, a student should have a ton of grit. There should be no give up in the student. They should be comfortable with the unknown, googling for answers and just figuring things out. The life of a data scientist is one of a life long learner. Successful students will have this quality. (for a more in depth answer to this question please check out a blog post written by Laurie Skelly, co-creator of Metis Data Science Curriculum and Data Scientist at Datascope Analytics.)

What sets your program apart from other data science bootcamps?

Our bootcamp is focused on providing accessibility, practical training and creating pathways to a career as a data scientist.

Accessibility: There are bootcamps in the market that will only accepts students coming out of a PhD program. We fundamentally believe that there is a much bigger population of students that have the skills and the ability to become data scientist. Over our first 2 cohorts we had 44% bachelors, 36% Masters, 17% PhD and 3% with Professional degrees. If you have the skills, we can help you meet your data science goals.

Practical Training: The program is hyper-focused on exposing students to what it takes to design and deliver a data science project. This means teaching people how to think like a data scientist, which starts with how to ask the right questions that will drive business value. Once students know what question they’re trying to answer, only then can they use the many technologies, algorithms and tools that we teach in the bootcamp to find the data, clean the data, analyze the data, and communicate the data. In our bootcamp, students go through this design-thinking process five separate times, creating a portfolio of five distinct projects, all using real data, that they can share with prospective employers. In addition to the project based approach, we also know that Data Scientists do not work alone and that collaboration is key to transitioning into a role as a data scientist. We ensure this is embedded in the 12 week curriculum.

Pathways to a Career: see next question

How does your program prepare a student for life after the bootcamp?

Pathways to a Career: The whole Metis organization focuses on the ultimate outcome, helping students secure a job. In order to provide guidance, Metis has a full time Talent Placement team that works with the students even prior to the start of the program. In parallel with the Bootcamp curriculum the Talent placement team has a career curriculum to help students prepare for their job search. In addition to on going support Metis is very committed to bringing in speakers from the industry to expose students to various roles and companies. Last Metis holds a Career Day at the end of the program to allow for students to present their final projects and to engage with hiring partners. The Metis talent placement team maintains contact with the graduates beyond the program up until the students are placed.

Got a PhD? Want to spend 6 weeks in NYC to become a Data Scientist?

The Data Incubator in New York City is a new data science bootcamp.

  • The program is run by Michael Li, a Princeton PhD and former Foursquare data scientist
  • Located in New York City
  • Tuition is Free
  • Help with job placement
  • Session runs from January 5, 2015 to February 13, 2015

Don’t delay! It is highly recommended to apply to The Data Incubator before the early deadline of October 7, 2014.

Data Science Productivity Platform

Tristan Zajonc, cofounder of Sense Platform, gave a recent thought-provoking talk at Data Driven NYC. He spoke about the future of data science productivity. According to Tristan:

In the next 2 or 3 years, everybody doing data science should be using a data science productivity platform…a cloud-based data science platform.

In addition to the productivity platforms, the power methods will see some improvements. Here are 2 that Tristan mentions:

  1. Probabilistic Programming – matching of computer science and Bayesian statistics
  2. Deep Reinforcement Learning – making optimal decisions via deep learning

It is an exciting time for data science. I think the next few years will see much better productivity tools, workflows, and platforms. More on that in an upcoming blog post.

The other videos from Data Driven NYC are also available on Youtube.

Another Data Science Program in NYC (also online)

Recently, both NYU and Columbia launched academic programs in data science. Well, another school in New York City is entering the mix. The City University of New York (CUNY) is now offering an online masters degree in data analytics. If you would like more information, there will be an online information session on May 22.