The Data Incubator

I write this review as someone who received a fellowship. The Data Incubator is, to put it simply (and bluntly), a scam. But given how they've blanketed themselves in a glossy website and high-quality video presentations, it's hard to view them for what... Read More they really are: a racket that preys on graduate students who need jobs. I was taken by their seemingly outstanding professional appearance, I mean, freal, their website is top notch, and the tutorials they posted made it seem like they really knew what they were doing. And they do know what they're doing, but it isn't to effectively teach you data science; rather, TDI simply acts as a middleman between talented graduate students and companies, looking for a fat cut ($16k a hire!). The quality of the program was piss-poor. If you accept, you are essentially agreeing to relocate to another city, find an apartment, and to sit in a damp office listening to lectures over a conference call device that had more noise than signal. For dessert, you get in-person lectures from unqualified career "professionals" who tell you how to frame yourself to employers. Ah, the employers ... the employers, during the recruitment process, were characterized by the TDI website as being top technology companies. When I was on the inside of the program, I got to see who these "top" technology companies really were: companies I had never heard of, or companies that were still in start-up mode (oftentimes both). I will give credit where credit is due: the weekly projects that they assign are very nice and helpful in learning the material. But is it worth spending a MINIIMUM of $3k on housing, $1k on food, and $16k if you're selected as a "scholar"? The answer is unequivocally no. It would be if they had complete ownership rights over the AI and ML branch of the tree of knowledge, but they don't. There is an awe-inspiring abundance of top-quality ML and coding resources online. Pick one at random and run. I assure you, it'll be just as effective as TDI was. As Elham mentioned in another review, you all are most likely graduate students in a technical field, and learning this stuff and doing a project should be precisely what you were doing in your particular subject matter specialities. You don't need TDI, and you certainly don't need them taking a fat slice of your pie (no matter how you cut it, some portion of the TDI fee will come out of your pocket). My heart really goes out to the people that paid for the program out-of-pocket.

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... Read More 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 also 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 rigorous technical curriculum.

Going to the TDI was I 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 toolkit to navigate the corporate world.

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... Read More 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.

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... Read More 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 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... Read More 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!

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... Read More 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.

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.... Read More

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 head shot:
Prior to the program, Fellows and Scholars are advised to get a professional head shot. 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 head shot (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 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.... Read More 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.

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... Read More 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 Post Docs from Stanford, Duke, Johns Hopkins, Yale among others. We were all gathered in the same... Read More 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 to 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 Seattle.
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.