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.