The Data Incubator

If you are "good" enough for them to offer you the Fellowship (i.e. don't pay tuition but make a deal with them to not looking ofr a job on your own for a while), definitely DO NOT accept it. You can find much better opportiunities on your own and with... Read More a little self-training based on free online materials. If you are the so called "scholar" who needs to pay $10000 for their 8-week program, definitelt DO NOT take it. They won't help you to land a job at all. All of their courses are well-known important topics that every data scientist need to learn at some point. You don't even need their list of toics to start with. Save your money and do it yourself or go to other camps if there is a really good one out there (I can't tell since I only have this one experience).

They have been advertising that they are connected to 200+ companies who most likely hire from their graduates. But out of those listed 200, 185 are not "active" and a few have been out of business totally, but this bootcamp still list them as their hiring... Read More partners. Very shallow support if you are a paid student and already paid the full tuition. They will care and support more if you didn't pay and owe them money after you find job. The course materials were good but they are all available online elsewhere. The only thing that might make the $10000 cost worth is if they can help you find a job. But most of us after finishing the bootcamp and trying their shallow list of partners , are exclusively hunting jobs in linkedin, indeed, etc.

Full disclosure, I was a fellow, so I didn't have to pay. I wasn't able to find a job through TDI, but I am convinced that it helped me marginally to find a job on my own. It's been more than a year, so here is what I still remember about TDI. I will... Read More start with what is good about TDI: 1) It forces you to learn Python. Some PhD students come to data science with advanced knowledge of other programing languages, which makes them reluctant to put time into learning python. If I can do everything is R or Scala, why do I need to learn Python? The truth is that Python is necessary for data science. It's the market language. TDI does a good job forcing you to learn python. I won't argue that they teach you python, but you'll have to learn python on your own or through their lectures, or both, to pass the program. 2) The daily challenges and the weekly projects help you think outside the box as a programmer. I benefited a lot from these. 3) The capstone project helps you market your self. I got hired partly because of my capstone project. Employers loved looking at a minimal working example of my work. They felt like they can gauge my abilities somewhat reliably based on the project. Unfortunately, for many students, TDI will not help you with your project in terms of modeling, but they do help you a lot in terms of marketing your project. 4) The name recognition helps. My experience is that most of the employers that I interviewed with knew little to nothing about TDI, but they also perceived it to be a positive thing. I was able to capitalize on that and promote my time at TDI as some sort of work experience equivalent. 5) The teachers are nice people. They were honest with me. When my presentation of my capstone project sucked, they told me so, and they helped me market it better. Now, I will move to bad things about TDI: 1) They won't teach you about machine learning. Forget about it. Their lectures and their weekly projects and their daily challenges fall way closer to data wrangling than they do to modeling. Quite frankly, the instructors a more like debuggers and far less competent at the foundations of ML. (Don't forget though, they are very nice and honest. If you ask them something they don't know, they'll tell you they don't know. Which I admired.) So, take time to teach your self machine learning. It's critical for your success at the job interview and at work. 3) The job market board is a sham. There will be a handful of employers, most of whom are TDI graduates placed at some unknown, newly founded companies. I sent many emails to their employers with very few responses and even fewer interview opportunities. Other students I know reported the same. Yes, people find jobs through TDI. But, the choices are limited, if there are choices at all. I got way better success with LinkedIn. 4) The moment you graduate, they will stop responding to you. Yes, you will have access to the jobs board, but at that point it will be a dead jobs board. Employers wont respond to you, and TDI staff, who are supposed to coordinate with employers, wont respond to you either. I couldn't even get them to talk to my hiring company so they can come and recoup their commission! Overall, it's a step in the right direction. If you have a fellowship, it's a no brainer, go for it. If you have master's degree or a PhD in something closely related to data science (such as statistics, economics, computer science), find a data science internship. You will get more out of it. If your graduate degree is in a field that is not closely related to data science, they you have a big decision to make. TDI will not teach you machine learning, but it will give you name recognition, teach you python, and set you up with a capstone project. Are these things worth the fees they charge? Only you can judge that.

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.