The Data Incubator

Data are becoming the new raw material of business
The Economist

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FAQ

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
  • Form 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 McKinsey estimates a need for 140,000-190,000 more data scientists over the next few years [3]. New York City, with a burgeoning technology sector and home to more Fortune 500 companies than anywhere else in the world, is quickly becoming the center for data science. The competition for talent has led to compensation packages for talented first-year data scientists in the range from $100K to $200K.

[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] "Big data: The next frontier for innovation, competition, and productivity" (McKinsey Global Institute, 2011)

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 or PhD and only has one year left of their degree program.

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 12 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 four main components to the program:

  • Bootcamp modules. Short modules covering both the technical and non-technical skills necessary to succeed in industry.

  • Seminars with mentor data scientists. Unlike academic research seminars, we promise these will actually make sense. Hear from the top data scientists in the world about what data science is like for them.

  • Build a series of mini projects to showcase your programming and mathematical talents. Hone your skills on a 100 node cluster and get hands-on experience applying the tools employers value to real-world datasets.

  • Interview with amazing employers. Meet employers looking for top applicants.

The program builds on your scientific training and provides you the skills needed to quickly have large industry impact. While an advanced degree is excellent preparation, our experience has shown that academic researchers often lack a few key skills. The curriculum includes:

  • 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, Hive, Spark.

Succeeding in industry is as much about interpersonal skills as technical ones. We cover some of the basics:

  • Communication skills. Academics and people in industry communicate in very different ways. We'll work with you to avoid common pitfalls and distill your research and data science insights into messages that will be appreciated by non-experts.

  • Networking. Meeting people is really important for your career but there are half a dozen subtle mistakes that young professionals frequently make. We'll help you avoid them.

  • Practice interviews. Technical interviews can be notoriously tough. We help our Fellows prepare so that they know what to expect.

  • Leverage your degree as a data scientist.  Training that links your analytical skills to job opportunities..

  • Free tuition for Fellows.  Employer-paid Scholars keep the program free for admitted Fellows..

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

  • Jumpstart your data scientist job search.  Opportunities at top innovators in tech, healthcare, and finance with typical base pay ranging from $100K - $125K and as high as $150K..

  • Smart, passionate Fellows.  Make the transition from academia with a selective peer group excited to learn and collaborate..

  • Build a series of miniprojects.  Gain hands-on experience applying the tools employers value to real-world datasets. All powered by a 100-node cluster..

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 be a part of the program. For the in-person program, this means moving to New York, San Francisco Bay Area, Seattle, Boston, or Washington DC, for the duration of the entire program and being there every day during the workweek, interacting with the other Fellows, and working on your portfolio project. You should really think of this as a sort of internship. For the online program, this means setting aside enough time to participate remotely. It is difficult to do so while working full-time.

  • 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 immediately after the program. If there's another company that you would like to interview with, just notify us in advance so that we have a chance to work with them as a hiring company. Most employers would prefer you start within 2-3 months of an offer.

  • Decline to work with external recruiters while in the program. We provide training to Fellows for free and compete with external recruiters who charge for just making a placement without providing any training. Working with them prevents us from investing in curriculum and improving the program for future Fellows.

We welcome applications from anyone who has already obtained, or is within 1 year of obtaining, their master's or PhD degree in any field. If you are interested, we encourage you to apply.

Absolutely! People with industry experience are often some of our strongest candidates. Please sign up here.

Absolutely! There are no formal skill requirements for the program. That said, most applicants have some familiarity with programming. If you are interested, we encourage you to apply.

We are looking for people with strong scientific training who are able to work with data programmatically and can make valid real-world inferences based on data. Applicants usually have a strong background in probability, statistics, and experience with programming, scripting, or statistical packages. 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 (currently at 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 (currently at 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 (currently at 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 (currently at 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.
In addition to the Fellow program, we are also offering a paid Scholar program. Because we can only take a smaller number of Fellows than applicants who are truly qualified, we also allow strong applicants to participate as Scholars. The application for the program is the same as for the Fellowship program.