The Data Incubator

Data are becoming the new raw material of business
The Economist

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Ninety percent of all the world's data was generated in the last two years. Every 2 days, we generate as much data as all of humanity did up to 2003. Data scientists have the analytical and programming skills needed to extract valuable knowledge out of the data. The unique combination of skills that data scientists have is used across many industries for projects such as:

  • Parsing unstructured electronic medical records to detect new risk factors for cancer
  • Poring through educational app data to glean insights on how students learn
  • Forming personalized recommendations for restaurants and bars for millions of users
  • Predicting crime based on social network data
  • Crawling through stock market data for hidden price signals

The demand for data scientists is growing exponentially [1, 2] and IBM estimates that by 2020, the number of jobs for US data professionals will increase by 364,000 openings to 2,720,000 [3]. New York City, home to more Fortune 500 companies than anywhere else in the world, is quickly becoming a center for data science. Washington DC, with a high concentration of both corporate and government employers, has emerged as a hotspot for data science, with large corporations drawn to its highly skilled talent pool. Growth in Silicon Valley has also driven the data science market in the San Francisco bay area, with more and more companies relying on data science talent to scale their businesses. The competition for talent has led to compensation packages for talented first-year data scientists in the range from $100K to $150K.

[1] "Big Data Needs Data Scientists or Quants" (Forbes Magazine, 2012)

[2] "What Are The Odds That Stats Would Be This Popular?" (New York Times, 2012)

[3] "The Quant Crunch: How the Demand for Data Science Skills is Disrupting the Job Market" (IBM, 2017)

We consider any of the following applicants:

  • Anyone who already has a master's degree or PhD. You do not need to currently be a student in order to apply as long as you already have a master's or PhD. Faculty and postdocs are also welcome.
  • Anyone who is in the process of earning a master's degree or PhD. We recommend master's students be in their last semester of coursework at the time they attend the program and PhD students be within six months of defending their dissertation.

The program is geared towards helping participants find a job in the private sector and we are looking for candidates who want to start within 1-2 months of completing the Fellowship. If you are interested, we encourage you to apply.

We accept international students for the program and encourage them to apply.

However, we are not lawyers and cannot provide legal advice. To the best of our understanding, you may participate in the program if you have a visa that grants work-authorization, e.g. H-1B, TN, L-1B visas or F-1 students on Optional Practical Training (OPT), so long as the program is incidental to your status, or if you have any visa that allows you to be in the country to attend meetings, e.g. a visa status which allows for participation in professional seminars and/or non-academic, short course of study such as the B-1 visitor visa or the Visa Waiver Program. Please consult an immigration attorney for additional guidance. If you need an immigration attorney, consider using Adam Moses at Wildes Weinberg.

There are three main components to the program:

  • Weekly Projects. You'll build a series of mini projects to showcase your programming and mathematical talents. These projects will help you build your data science skill set using real world data to solve business problems.

  • Capstone Project. Using a data set of your choice you will build a working web application to showcase your talents for employers.

  • Interviews with employers. TDI works with over 300 employers in a variety of industries. Those employers play an active role in our programming throughout the cohort, attending events with students, and hosting panels on their industries.

Our program aims to build on the preparation you received in your academic training, by developing key skills such as:

  • Software engineering and numerical computation. Numerical techniques for optimization and vectorized linear algebra. Programming tools including Python, numpy, scipy, scikit-learn, matplotlib.

  • Natural language processing. Handling unstructured data, stemming, bag of words, TF/IDF, topic modeling.

  • Statistics. Hypothesis testing, regression and classification, ensemble methods, cross-validation, variance-bias decomposition, data normalization.

  • Data visualization. Including geographical and temporal data. Packages like d3, ggplot, matplotlib.

  • Databases and parallelization. SQL, Hadoop, MapReduce, Spark, TensorFlow.

In addition to our technical curriculum, staff supports students in all aspects of the job search.

  • Resume building. We assist in building a resume that translates your academic experience for industry employers.

  • Interview Prep. Over the course of the program we cover every aspect of the data science interview process, from technical coding interviews to behavioral and onsite interviewing.

  • Placement Assistance. Throughout the program our team works with you to identify the kinds of employers that would be the best fit for you, and to facilitate your application to those employers.

  • Job placement assistance. Our staff works closely with Fellows to identify their unique interests and skills to facilitate placements with our industry partners.

  • Tuition free. The program is free for admitted Fellows.

  • Hands on experience. All of our projects are designed to give you experience with real data sets, solving real problems.

  • Onsite instructors. Every location has an onsite Data Scientist in Residence to lead discussion and assist students.

  • Mentorship from industry leaders. Learn from alumni and senior data scientists, and build your professional network.

  • Cohort style program. Make the transition from academia with a selective peer group excited to learn and collaborate. We aim to keep each cohort small, fewer than 20 students per location, to maximize your interaction with our Data Scientists in Residence.

The program is in partnership with the Fellows and while we provide our Fellows with a lot, a few things are expected in return:

  • Make a commitment to participate full time. Fellows are required to participate in the program in person. This means moving to New York, the San Francisco Bay Area, Seattle, Boston, or Washington DC, for the duration of the program. We expect Fellows to be in attendance for a standard 9 am - 5 pm work day, including occasional evening events. Scholars have the option to participate online, but we still recommend making a full-time commitment to the program for eight weeks.

  • Make a commitment to work as a data scientist in industry shortly after completing the program. We ask that you interview with our hiring companies during and immediately after the program. Ideally Fellowship candidates will be ready to start work within 1-2 months of completing the program.

  • Decline to work with external recruiters while in the program. In order to keep the program free for Fellows we do require that Fellows job search exclusively with our hiring partners during the program.

We welcome applications from anyone who has already obtained, or in the process of completing, their master's or PhD. If you are interested, we encourage you to apply. We recommend master's students be in their last semester of coursework at the time they attend the program and PhD students be within six months of defending their dissertation.

Absolutely! Candidates with industry experience are welcome to apply, as long as they have a completed Master's or PhD.

We require all applicants to be familiar with at least one programming language. While the program emphasizes Python, many of our applicants have experience with other languages, and varying degrees of coding experience. If you have no programming experience, we recommend starting with our Data Science Foundations Course which is designed to help students prepare for the Fellowship.

Applicants usually have a strong background in probability, statistics, and experience with programming, scripting, or statistical packages. We don't, however, have any strong preferences about academic discipline. We've had successful Fellows from backgrounds as diverse as Anthropology, Political Science, and Sociology, as well as Mathematics, Physics, Chemistry, and many others. If you are interested, we encourage you to apply.

Interested in the Fellowship but want to brush up your skills before applying? Read through this blog post to learn about some of the most important foundational skills for data science and to access resources to help you develop them.

Our alumni have gone on to take prestigious jobs in data science all over the country. Here's what they have to say about the program:

  • Dorian Goldman (hired by the NYTimes): "The Data Incubator team did an incredible job of emphasizing the most important and fundamental concepts that a data scientist needs to know in his career - I know, because all of these things were confirmed in my first week at my new job."
  • Justin Bush (hired by Palantir): "Already by the second and third week of the Data Incubator there were companies contacting me that may not have noticed my resume so readily otherwise. I also got a tremendous exposure to the variety of data science jobs out there, something that would not have happened had I taken a job directly out of grad school."
  • Brian Farris (hired by Capital One): "It was an extremely efficient way to do a lot of networking in a short amount of time, which greatly increases the chance of finding a job. It is much easier to initiate a dialogue with a hiring partner if you have already met someone from the company in person."
  • Sam Swift (hired by Betterment): "The intense incubator experience was also a great way to quickly transition my thinking and language from academic abstraction to business pragmatism. Like miscommunication between any two fields, I found that there was lots of common ground on ideas, but that it was obfuscated by specialized jargon on both sides."

For more on our alumni, check out our blog.

The tuition fee is paid for by hiring employers. The only cost for all Fellows is the cost of hosting a server in the cloud, which is required for running the course material. In-person Fellows are responsible for their own room and board during the Fellowship. We can assist Fellows in finding housing.

We accept only a very small number of applicants as Fellows, but will make additional offers to highly qualified candidates to participate as Scholars. There is a fee associated with Scholar participation, 50% of which is refunded to candidates placed with TDI hiring partners. If you would like to be considered for Scholar admission, the application process it the same and you should indicate interest on the application.

We also encourage anyone who is genuinely interested in data science as a career to reapply. Many of our most successful Fellows were not accepted on their first try, and used the time between application cycles to improve their application or technical skills.