The Data Incubator
The Data Incubator is an intensive 8-week fellowship that takes applicants who have already learned engineering and science skills, and equips them with the final skills to be self-sufficient, productive contributors to data science. The Harvard Business Review calls Data Science the "sexiest job of the 21st century" and The Data Incubator wants to help you leverage your PhD or Master's to become a Data Scientist or Quant. With various campuses in different locations, the program is free for admitted Fellows, and employers pay a Fellow's tuition fee if they are successfully hired. Students receive mentorship from hiring companies throughout the program, and will build a portfolio project that employers value. The Data Incubator only accepts PhDs and Master's graduates.
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Recent The Data Incubator Reviews: Rating 4.69
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The Data Incubator Reviews
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I got a job out of The Data Incubator! But in addition to that, I got at least three important things out of The Data Incubator. One, I received comprehensive training in key data science tools. Two, I was able to become a part of a supportive network of fellow PhD data scientists. And three, I came away with a good understanding of what is needed in industry and how I, as a PhD, can be of use.
I participated in The Data Incubator's Spring 2015 cohort concurrently with finishing my PhD in physics at Penn State. Through TDI I landed a job at Optoro, an e-commerce start-up in Washington, DC.
The course was amazingly comprehensive in covering the tools and techniques most in-demand in industry today. Many of the topics we covered came up in interviews and problem sets during my job applications, and I wouldn’t have done nearly as well as I did in my job search without the skills I gained in the course.
Another great thing about the program was the opportunity to hear directly from company representatives during the panel discussions. This helped me get a feel for the lay of the land and discover what types of jobs were out there. There were a lot of companies where I never would have considered applying, but once I heard what they were doing with data and about the scope of their technical ambitions, I became really interested.
If you're thinking of applying, be persistent - it's quite common to apply several times before being admitted (I applied twice). Overall the program is a lot of work but it is worth every hour you put into it.
At the end of my Computer Science PhD program, I attended the Data Incubator session in NYC in early-2015. I am very satisfied with my experience and recommend it to those with academic/research background looking for next steps.
The greatest benefit of The Data Incubator is in the ability to ramp-up your job search very quickly and take it to the next level. The partner companies come to the program ready to interact with Fellows and to find good hiring matches that satisfy their needs. The Fellows come ready to demonstrate their abilities, to show how they can add value, and to jump into new opportunities. This convergence of interests is very special and benefits everyone involved. I know that I benefited from the opportunity to interact so intensively with the wide variety of partner companies in my own search process.
Also, I especially enjoyed participating as part of the cohort in the NYC Data Incubator. Being located in NYC and interacting with the other fellows was very valuable. It helped me to understand my own strengths relative to others looking for similar opportunities. Coming together as a group all at once with similar goals to a new city helped to turn what is usually an individual effort into a more collaborative process. The connections formed within the group will help us far into our future careers.
I was a fellow during the summer of 2015 in DC. The Data Incubator helped me become a much more competitive applicant for jobs in the data science industry. By completing a collection of projects, I became much more proficient in the tools a data scientist needs to know and had multiple points to show off during job interviews. I also got to network with and befriend many other bright fellows who have since moved on to great jobs. I myself was hired at my current job through the Incubator, so it's hard to argue with results.
To those considering the incubator, you will definitely benefit from the experience, but be prepared to work ~12-14 hour days throughout most of the program. It's hard work, but it pays off.
I attended The Data Incubator with a very talented and highly motivated group of recent and soon to be PhDs in engineering, physics, math, neuroscience, psychology, and linguistics. We spent our days working on challenging mini projects and evenings polishing the pitches for our capstone projects. In between we met employers at partner panels, happy hours, and pitch nights. I ended up taking a job from one of the hiring partners and am really glad that I was a fellow at TDI.
The Data Incubator surrounded me with incredibly smart, motivated Fellows who all had similar goals and backgrounds. Being in that environment, and having the structure of the program and the demands of my project, were essential to my education.
A talented instructor is not supposed to implant information into your mind, but rather function as a tour guide in a difficult landscape of concepts of varying relevance. 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.
I can't imagine having tried to find a job without The Data Incubator faciliating all of the connections we made.
I was in the Spring 2015 cohort in NYC. I was very happy with the program. In two months, I was able to learn a ton, and ultimately land a job I am very happy with at Capital One Labs.
The most valuable thing I got out of The Data Incubator was the ability to meet people from industry through the partner panels and happy hours. For me this served two purposes. First, 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.
Second, I learned a lot about the landscape of the industry, which helped me figure out what I was looking for in a job. Over the course of the program, I was introduced to companies that weren’t on my radar and found myself very interested in them. Once you are in The Data Incubator program, there are too many partner companies on the list to be able to seriously pursue them all. You have to know what you are looking for in order to focus your efforts, and I definitely gained that focus over the course of the program.
I was a tenure-track scientist when I decided to move to a job as a data scientist in industry after I got frustrated with funding issues in academia. I was very happy with the Data Incubator course which met all my expectations an more. I was introduced to many concepts that I had just touched on in some online courses I had taken prior to the course. By having weekly mini projects, I was forced to understand the material more. Whilst there were lectures to accompany the mini-projects, in order to complete them I was forced to do some research on my own which was a valuable thing. We weren’t just fed the answers.
On top of this, as an online Fellow, I appreciated being split into small groups. In my case, this worked really well and I would encourage others in the same situation to use this to their advantage and hold regular meetings.
The course provides practical interview prep and works with a large number of companies which makes finding a job after the course that much easier. Often, as in the car of the company I ended up at, companies hire from the Data Incubator after hiring fellows from previous cohorts. This shows that the Data Incubator graduates are held in high esteem by the hiring companies which shows the strength of the course. I have no hesitation in recommending the Data Incubator to any aspiring data scientists.
I applied to several data science programs. This one had by-far the most rigorous admissions standards. While it was a lot of working, after joining, I realized that it was well worth it because everyone you meet in the program is a genius. And I'm not exaggerating. I was sitting next to a guy who did his PhD research with a Nobel Laureate!
Being surrounded by the most inspiring, passionate, intelligent, and dedicated students meant I learned so much from my peers. And that's on top of the industry-leading staff that taught the courses. Their network is also really strong, especially in the Bay Area and NYC so I now have a built in network of data scientists. I know that some employers don't even consider applicants who are not graduates from this program (I know we certainly don't)!
I highly recommend it to anyone who is looking for a job in industry as a data scientist or data analyst.
The data scientists at The Data Incubator taught us so much about data science -- from mathematics and statistics to machine-learning, natural language processing, mapreduce, and spark. I really learned many useful things that I would not have learned on my own.
We got to run real mapreduce and spark jobs on 100-node clusters on AWS. It was very impressive to employers. This is the kind of experience you never get to have if you are just learning on your own. I also met make great friends who are great professional network for me now.
You can learn more about them at their fellowship page. And of course, I got a great job working at a top bank. I love this program!
I love this program. I had to apply twice but was finally admitted. The program was very tough to get into and it was really a very intense course -- you have to work very hard even when you get in. But I learned a lot.
Most important, they had great relationships with employers. As a foreign student, it can be hard to get a job in the US. The team at The Data Incubator connected me to many opportunities and helped me get a job. I am very grateful!
I went through the complete application process which involved several coding challenges, project proposal, and interviews, and thankfully, got in the program. Although their whole admission process has plenty of room for improvements (e.g. solution submission is a bit cumbersome, no contact person nor phone # for assistance), it takes about six to eight weeks, and a fair amount of work from the applicant; yet in hindsight, I think the whole process was instructive and helped sharpen both my programming, data analysis, and presentation skills. If you're phd academic refugee looking for a switch into data science industry, the data incubator is certainly worth a try. Good luck!
Here's a warning: They don't seem interested in training people from scratch, despite what they claim. They appear only interested in training people who already have a pretty good knowledge base and could possibly already get a good job in the field, based on the application process. And their application process is amateurishly executed at best. The program itself might be just fine (it seems to be well regarded by those who do get in), but they seem like they're probably just trying to get a financial piece of the pie for demand for these positions and working off desperation for recent PhDs trying to get a job in a marketplace that is extremely hostile to on-the-job training.
Our latest on The Data Incubator
The Data Incubator is an intensive eight-week data science bootcamp which trains people with STEM backgrounds to become in-demand data scientists. With an acceptance rate of just 2%, we wanted to find out what the application process is like, and the types of applicants that The Data Incubator accepts. We asked Alyssa Thomas and The Data Incubator admissions team to answer all of our questions about how to successfully get through the application (hint: the project proposal is key!) and interview process.
How long does the Data Incubator application typically take? What are the steps applicants should expect?
The entire application process is spread out over the course of roughly six weeks. After the deadline, initial applications are reviewed and semi-finalists are selected. Semi-finalists must complete the technical challenge portion of the application. From this pool of candidates, finalists are selected and are asked to schedule admissions interviews. Admissions decisions are made after the interviews (which are typically spread out over about two weeks) and applicants are notified of final decisions within about a week of the conclusion of interviews.
What is the technical challenge portion of the application like? Can you give us a sample question?
The technical challenge consists of a programming challenge and a data challenge.
Here is a sample of one of our past challenge questions. We don’t re-use them, but this gives you an idea of what to expect:
A chess knight piece is sitting on the "0" key of a numeric keypad.
1 2 3 4 5 6 7 8 9 0
The knight makes T jumps to other keys according to its allowable moves (so that from each reachable key it has two or three allowable moves). The knight chooses amongst the allowable moves uniformly at random and keeps track of the running sum SS of keys on which it lands. We are interested in S under the modulo operator.
After T=10T10 moves, what is the expected value of the quantity S mod 10?
How long should the coding challenge take? Is there a time limit?
We give applicants five days to complete the challenge (including project proposal and video submission - more about this below) but the coding challenge itself takes at minimum four hours to complete.
In what programming language should the applicant complete the coding challenge? Does this have to be in Python?
The coding challenge supports several different languages. C/C++, Matlab, Stata, Fortran, Perl, SQL, IDL, Python, VBA, Java, and R will all work.
How should a student who doesn’t have a background in Python prepare for the coding challenge?
The programming challenge can be done in just about any language, the data challenge could be done in SQL, R or possibly several other languages with a bit more work. We encourage everyone to learn Python, but if it isn’t the language you’re most comfortable with at this point there are options!
What goes into the written application?
We require short answers for each of the following:
- Tell us about what makes you excited about data science generally.
- Tell us about what makes you excited about The Data Incubator specifically.
- What would you do if you were not accepted as a Fellow for The Data Incubator?
- Tell us about your industry job search experience so far.
- Anything else? You can write about courses you took, side projects related to data science, or anything that you are passionate about.
We also ask for information about degree status (current/expected), educational background, work experience, programming experience, mathematical/statistical experience, industries of interest, and geographies of interest.
Does Data Incubator require a video submission? What’s the purpose of the video submission?
Applicants submit their video along with the technical challenge. In the video we want you to pitch your project proposal to us, tell us what would make your project unique and worthwhile!
What is the Project Proposal?
Towards the end of the bootcamp, Data Incubator Fellows complete a Capstone Project separate from the weekly mini projects. This Capstone Project can cover any subject you like - and we’ve seen everything from Chicago crime data to using machine learning to categorize Google images. So when you are applying, the project proposal is your chance to tell us what you’d like to work on and why it matters. The best project proposals are the ones that show off your unique perspective and explain the business relevance of the work you’re doing.
What types of backgrounds have successful Data Incubator students had? Does everyone come from a technical background?
We’ve had successful Fellows from almost every academic background. Of course, Physics, Engineering, and Math are popular majors, but we’ve also had Anthropologists, Political Scientists, and Marketing PhDs who were very successful in our program. Most Fellows have been coding for at least a year, but certainly not all of our Fellows came from technical backgrounds.
What are the education requirements for a Data Incubator student?
We require Fellowship applicants to be within a year of completing their Master’s Degree or PhD.
Who conducts Data Incubator interviews? Should applicants expect to be talking with a founder, an instructor, or an alumni?
All of the above! Our founder participates in interviews, as do our instructors (some of whom are Data Incubator Alumni) and our partnerships team.
Is there a technical component to the Data Incubator interview or are you looking for culture fit?
We ask everyone to present their project proposal and answer a few questions about the proposal from other prospective Fellows. We’re looking for the technical ability (and potential) in the project proposals, but also paying attention to interaction with other prospective Fellows in the interview session. Our program is very hands-on and does require a lot of working together, so we’re paying attention to both cultural fit and the way applicants interact with others.
Can you give us a sample of a Data Incubator interview question?
“Why are you transitioning into Data Science?” It sounds simple but it tells us a lot about an applicant’s motivation for entering the program. The program isn’t easy and the most successful Fellows are the ones that are really committed to this field.
How do you evaluate an applicant’s future potential? What qualities are you looking for?
We want each class to bring a variety of different backgrounds and perspectives, so there isn’t any one specific thing we look for. We like to see applicants who have given serious thought to their project proposals though, and we highly value presentation skills and the ability to distil a complex subject into a format anyone can relate to. We also look for Fellows who are very motivated to enter the field of Data Science quickly and make the most of their time in the Data Incubator.
Can applicants do the interview in-person or are all interviews conducted online?
We conduct all of our interviews online.
Are students accepted on a rolling basis?
We don’t do rolling admissions. Fellows are accepted for one of the four sessions we hold throughout the year and will be notified of their acceptance about a month before the session is scheduled to begin.
What is the current acceptance rate at Data Incubator?
Right now it’s a little over 2%, but don’t let that scare you!
Does Data Incubator accept international students? Do international students get student visas/tourist visas to do the program?
We will accept international students, but we do not provide visa sponsorship.
Can rejected applicants reapply to The Data Incubator? If so, how many times?
It is a very competitive admissions process. But several of our most successful Fellows were those who applied more than once, and there isn’t a limit on how many times you are eligible to apply. Often applicants who are not admitted the first time they apply spend the time before the next admissions cycle working on their skills or improving their project proposals, which can make them even stronger candidates the next time they apply.
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The Data Incubator is a course for PhDs who want to exit academia and enter into the world of Data Science. We talk to Jon Ferris, a cofounder and Director of Partnerships at The Data Incubator, about the unique 6-week course, requirements for admittance, and how the "bootcamp" model is being applied to Data Science.
What is your role at Data Incubator? How did you get involved in the bootcamp space?
I’ve developed a passion for big data over the past couple years, and I was brought on a few of months ago to join Michael and help create the Data Incubator. Michael is an academic, a data scientist, and also one of our instructors. He’s focused on building the curriculum and speaking at conferences. My speciality is more around developing partnerships and growing businesses.
Where is Data Incubator teaching classes?
We’re working and teaching out of the AlleyNYC campus in Manhattan.
This will be your second course?
Yes, and it starts on September 9th
Are you all getting a lot of applications?
We had about 1,000 for our summer fellowship, probably because it’s free for fellows. Appliants need a PhD to apply, along with coding skills, strong aptitude for math and statistics, and the ability to communicate effectively.
What kind of applicants are you looking for? The website talks about applicants needing to have 90% of data science skills already. How do you know who qualifies?
They have to have a strong working knowledge of mathematics and statistics. They have to be able to code in at least one object-oriented language, and they have to be able to prove it; the admissions process includes a series of challenge questions, a code review, and an oral interview.
What’s the structure of the boot camp once students are there? What do you fit into those 6 weeks?
It’s an intensive so it’s onsite, in person, in New York. The first part of the day is spent in lecture, going over best practices for a particular data science tool or technique.
At the beginning of the 6 weeks, Fellows are asked to create a portfolio project. This is where they decide how they want to showcase their talents to employers. The project involves manipulating a large data set to come to some sort of conclusion. How do crop yields in Pakistan affect equity trading in Manhattan? So we have lecture in the morning, lunch, portfolio project development in the afternoon, and then at the end of the day, every day we have an employer come in.
Tell us about those employers.
During the program we have employers come visit the Fellows every day. For example, someone from Microsoft will explain what they’re doing, some challenges they work on, what data they’re working with. And they’ll answer all of the fellows’ questions. The fellows get great exposure to hiring companies and of course it gives the employers a sense of the Fellows, before they ever decide if they want to interview them. It kind of breaks the ice.
Why are you confident that the fellowship model can be applied to data science as a subject?
These folks are in really, really high demand right now. It’s very competitive to get them to come work for you. But you can’t create a data scientist overnight, and certainly not in a 6-week program. So everybody we’re working with has 5 to 6 years of advanced academic training. The chance of becoming an effective data scientist is much higher.
It doesn’t mean that there aren’t self-taught Data Scientists out there – there are. There are folks who probably don’t even have college degrees who work as data scientists because they were too smart or didn’t have the patience for an advanced degree. There are some terrific data science programs popping up around the country - just be wary of boot camps that say they can create a data scientist overnight.
We’ve started to see schools like Metis launch data science programs which are 12 weeks and you need to have some prior experience in Python, but you don’t have to have a PhD. What do you think about schools like that? I think the field is so broad that there’s potential at any level. You could take an entry level business person and put them through a 12-week analytics bootcamp and they can speak knowledgeably about that subject.
So I think it would totally help them in their careers and help them get a job. Any education is better than no education. There’s a real variety out there. Berkeley launched their Master’s program last year- it’s $53,000. Then you have some bootcamps for data science that are 12 weeks that are about $15,000. And then there are $6,000 programs on Coursera. So there’s all kinds of options. This is a great time to pursue education in this space.