TDI is an intensive fellowship program in data science. Very similar to what you would experience in corporate dynamic culture. However, don't expect to learn to coding or basic machine learning from scratch. You are expected to have basic understanding of ML and data science tool. I would strongly recommend you to do enough homework before you apply to avoid later disappointment. I took around 4-5 DS and ML classes on coursera before I applied. 

The application process can be daunting and intimidating, however, each step has its reasons. This makes each cohort learn and progress in a homogeneous pace, which is key to a successful completion of the TDI. 

I was part of the D.C 2017 winter cohort and the 8 weeks were key to position myself as a Data Scientist in the industry. You share and work collaboratively with the rest of the cohort, making it invaluable because you are not only learning from the diverse curriculum but also from your peers. At the end of the day, Data Science is both a Science and an Art, so different perspectives and approaches to problem-solving definitely enhance your skillset. 

Additionally, there is a focus on soft skills, from getting your resume up to speed to effective communication. Each week there are dedicated sessions on how to tackle interview questions, how to sell yourself, and how to navigate opportunely the recruiting process, complementing TDI's rigorous technical curriculum. 

Going to the TDI was not only enriching but also enjoyable. You come out of the program with a powerful network of top-notch data scientist, a second to none skillset and the right toolkit to navigate the corporate world.

After the interview, I was not admitted, but I now know what my weakness was - lack of a prepared project. The exam for which  4days are given consists of 3 parts. Two parts are questions with ~8 subquestions with increasing difficulty. Anyone with a basic data science and programming skills will answer those questions in 2-3 full days. What I'd recommend doing is to complete the 3rd questions - on your project. In their email, prior to the exam, they give a hint that they want the project to be in the draft form. It should be done on a large data set, interesting in analysis and in insight, and be implemented desirably as Heroku app  (and look similar to the ones in the Youtube videos of the projects). IMHO, if you'll prepare such a project you won't have a problem having a good grade. At that point - you might be already a good data scientist - apply for jobs independently. Good luck!

I highly recommend this 8-weeks intensive training at The Data Incubator (TDI), because it really helped me to go deeper into data science field and get fully prepared for the essential skills to work in a big data industry.

As a PhD graduate in chemistry background, the transition from academia to industry is not easy. But fortunately, I attended TDI during Winter 2017, and I gained full stack from the program, including the cutting-edge analytics techniques, programming, machine learning, data visualization as well as business mindset. The networking with all other talented fellows is definitely a plus! Needless to say, my 1st data scientist job with a hiring partner in less than a month from graduation is the most valuable thing I got out of TDI!

As a recent graduate of the Winter 2018 cohort, going through the 8-week intensive data science training at The Data Incubator has taught me a great deal about various data science tools and has prepared me with the essential skills to thrive at my first data science job. I've gained a full data science stack, such as creating a web application, web-scraping, data cleaning, exploratory analysis and visualization, SQL, machine-learning, big data tools, and cloud computing, as well as a business mindset. More importantly, networking with and learning from other talented and brilliant fellows has taught me a lot about myself and how to become a great data scientist. More importantly, I made a lot of connections that I can see will be long term.

I attended The Data Incubator during Spring 2017. I earned a data science position with a hiring partner in the San Francisco financial district within three weeks of graduating. Below I enumerate the many aspects of The Data Incubator I found valuable.

Resume building:
Starting at the semi-finalist level, applicants are provided strategies for resume writing. With some careful thought, I was able to portray seemingly bland parts of my academic background as eye-catching resume bullet points. Time is also dedicated in the first days of the program for polishing resumes yet again before final submission to the employer-facing online resume book.

Professional headshot:
Prior to the program, Fellows and Scholars are advised to get a professional headshot.  I had never done this before (or really had been aware of such services), but I realized it was an important part of going all in. While this can be expensive, Fellows who successfully join a partner company are reimbursed for the headshot (I was).

Structured curriculum and weekly miniprojects:
The data science curriculum includes lectures, daily coding challenges, and miniprojects. Weekly lectures are accompanied by IPython notebooks mixing text exposition with runnable code. There is a lot of lecture material to master every week, and persevering here helps with interviews and the miniprojects. The notebooks encapsulate the advanced features of scikit-learn, SQL, and big data tools (Hadoop, Spark), and they make for indispensable reference material after the program. The miniprojects are essentially problem sets and provide hands-on experience with these tools.

The capstone project:
This is meant to be an application of data science to a publicly available (or scrapable) data set that is ultimately presented as a web application.  It is adisable to have a rough draft, or at least a strong start, on the project before beginning the program, so start thinking about this before applying. There are several upshots to doing well on the capstone: 1) You have a recent data project to talk about in interviews that is more substantive than any of the individual miniprojects, 2) Practice building a web app (e.g., with Flask) for deployment on cloud services (e.g., Heroku), 3) Practice pitching your project in weekly video updates; for these videos, I learned how to edit video/sound with Openshot and to splice in images and screen capture footage of my project. 

Soft skills lectures and interview practice:
Soft skills lectures provide coaching for resume writing, onsite interviews, and salary negotiations. Weekly interview practice covers computer science and statistics problems of varying difficulty, both on pen/paper and in front of a whiteboard. 

Summary:
The Data Incubator is an extremely worthwhile experience. The components of the program outlined above have a snowball-like cumulative effect at turning academics into viable industry job candidate, commensurate with the effort they put into preparation before and during the program.

TDI laid the foundation of knowledge for computer science and data science related concepts. The TDI curriculum provided me the breadth and depth of knowledge necessary to understand everything from software engineering and numerical computation to the use of programming tools (including Python and d3 for data visualization), Natural Language Processing, statistics and probability as well as the ability to apply parallel processing to data analysis. Importantly, TDI also prepared me for the technical questions asked during my data science interviews.

Learning:

The Data Incubator was an outstanding experience. I got to know and work with some of the brightest minds there are, the cohort included Ph.D's and PosDocs from Stanford, Duke, Johns Hopkins, Yale among others. We were all gathered in the same place to exchange ideas and tackle our very demanding weekly projects (a different relevant subject each week) while working on our final, capstone project.

I was exposed so many different technologies, from big data to deep learning, from statistical analysis tools to machine learning, some of them I already knew and some were new to me.

Interviewing:

They have a great list from hiring partners all over the US (and many abroad), including very big names from Silicon Valley and Seatle.

We had weekly interviewing practice, from hard to soft skills, from coding to communicating.

Your success is their success, it is a win-win scenario.

Completing miniprojects on diverse and up-to-date topics really helped me to be confident about how to apply my technical skills on solving problems in practical situations. The hands-on experience from end to end, especially the relevance of the techniques to that in industry, is going to be a long term benefit for me and certainly for any previous and current fellow. 

The opportunity to have conversation and build relationship with different companies. This is not only for landing a job but more for a healthy business relationship in a long term. Getting the benefit from the bridge built up by The Data Incubator between fellows and partners is one thing. Another important goal of networking is for future communication and collaborations. Here comes our Fellows. I kept in touch with some of the fellows after we finish the program and we keep each other posted. It is invaluable having fellows experience the transition from academia to industry together including sharing thoughts and helping each other.

I attended the Data Science Foundation Class in winter 2018. The class was held online using Go to Meeting and Slack. Average class attendance was around 20. The teacher was always on time and there were very few technical inconveniences during lectures. The Data Science Foundation provided each student with a virtual server with class projects already loaded which greatly improved on-boarding times at the beginning. We were able to jump right into the material and not spend a lot of time trying to make everyone’s machines work.

The class content was organized well. We started by reviewing python and learned pandas and other libraries required by the class. We then moved to statistics and probability. Then back to additional python libraries and finished up with a very brief introduction to machine learning. It being a foundation class it moves very quickly across the top of a wide variety of topics. Having come from a background in programming I felt very comfortable throughout the entire class. I did struggle with some of the math concepts. I felt that more time could have been spent in that area.

Overall the class gave me an excellent foundation on which to continue learning data science. I use the tools and concepts learned in the class everyday at work and it has helped to advance me in my field. You won’t regret taking this course.

Since I had always been in academic before taking TDI, 
TDI is like a window to the industry, a bridge walking 
me smoothly from the academic world to the industry one. 
Through a series of activities like panel discussion and alumni party, 
TDI offered me a great platform to know what kind of problems 
companies are trying to solve, what skills they are looking for, 
how the daily life looks like, etc. Moreover, TDI provides valuable 
guidance in the whole process of job search, and last but not 
the least, the chance to work with a bunch of very smart people.

TDI gave me a lot of experience handling data in a way that I didn't get in an academic environment. The data sets were big, messy, and realistic. In addition, I thought that the capstone was an excellent way to get into a more industrial environment. The Data Incubator required a lot of database management, web scraping, and the like, which I didn't get in the academic setting I came from.

I also felt that TDI gave me a number of excellent opportunities. It may seem frustrating at times, but the partners really do want to hire Fellows, and TDI's salary and compensation ranges are very accurate (in my experience). I'm not sure I would have gotten the same response rate and offers if I hadn't been applying through TDI.

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!

Completing miniprojects on diverse and up-to-date topics really helped me to be confident about how to apply my technical skills on solving problems in practical situations. The hands-on experience from end to end, especially the relevance of the techniques to that in industry, is going to be a long term benefit for me and certainly for any previous and current fellow. 

The opportunity to have conversation and build relationship with different companies. This is not only for landing a job but more for a healthy business relationship in a long term. Getting the benefit from the bridge built up by The Data Incubator between fellows and partners is one thing. Another important goal of networking is for future communication and collaborations. Here comes our Fellows. I kept in touch with some of the fellows after we finish the program and we keep each other posted. It is invaluable having fellows experience the transition from academia to industry together including sharing thoughts and helping each other.

Hi, 

I was a PhD student in physics who decided to switch to data science. I applied to both Inisght and the Data Incubator. The experience of the two bootcamps is very different. At Insight, they come across as very kind and helpful. Genuinly trying to get you some experience. While at Data Incubator, they are non-responsive and money oriented. You have to do a huge amount of work to get in, before even getting a single email from a coordinator. When you are selected as a semi-finalist, there is not much information about the next step, but they basically want you to have a full project ready to propose and talk about. After you have done all this work, more than onsite-interviews to data scientist at Google, you might end up with the "great news" that you have been selected as a scolar, where you have to pay $16,000 for the session. This is more than 2 semesters at a prestigious college. To me it seems like a scam, but that is just my oppinion. You will to better of taking online courses and traveling to Bahamas for this money!

Good luck on your transition!