The Data Incubator

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.

Hi,
I am a PhD student in Mechanical Engineering. For my career, I’ve gave it a lot of thoughts and I chose to be a data scientist, so I got my minor in statistics. I’m going to graduate by the end of 2017. While ago, I received an email about Data incubator.... Read More I though it’s a good opportunity for me to learn more about data scientist and it’s going to be great for my resume. The first time that I applied for Data Incubator, it asked for recommendations and resume and the next step was link to a test, when it was the time for the test, the link wasn’t working. They emailed applicants after couple of days and apologized for the technical problem and gave us new timing for the test which didn’t work for me. After 6 months, I received another email from Data Incubator which said they kept my recommendation and encouraged me to apply again. I did and this time I got to the test. The test was super time consuming. In one question the data was related to 600,000 people who applied for a loan from a company and they were looking for real answers like the fraction of 36 month loans to 48 month or Pearson correlation between return rate and interest rate and more complicated stuff. Only this question had 12 parts and each part took me lots of time to code in R and I had to copy my code to the exam page! Another question was proposing a project that could be a good reason for a company to hire me, Also, I had to provide the code for the project and presenting it and post it on Youtube and create the github version of it. As you can guess see it was lots of work. I went through all and it took me 3 full days to get it done and submit all they asked! Then, I received an email that I pass the exam successfully and I’m the finalist and should get ready for the video conference interview. In the interview, there were 5 other poor PhD students that worked hard to prove themselves. I thought I did good job in the interview. Any ways, few days ago, I got the result from Data Incubator. They said congratulations on my selection but as a scholar! What does it mean?! When I opened the acceptance letter they asked for $1000 deposit, and it's not all, I have to pay $15000 more before the program gets started in September! I have to work for them for free for 8 weeks and pay them $16000, plus I’m responsible for my place and meals. Just imagine how much renting a place for 8 weeks in large cities like San Francisco or New York is going to cost?!
$16000 is approximately my 12 months income since I’m still a student and I believe it’s pretty much the same for all other PhD students around the country! So, I think Data Incubator is just a modern way of scientific fraud.
The fact is getting job in industry for a PhD student is really hard specially because PhD students are overqualified for most of the positions. Data incubator abuses this need of poor students and make them work hard for free and asks for $16000 for 8 weeks teaching material that you can access on Youtube for free!
Another thing that you have to know is that Data Incubator sponsor companies are not willing to hire people who participated in the program because they have to pay to Data Incubator! Funny, isn’t it!
I got really disappointing after all I went through to pass many hard steps of their competition. I hope my sad experience helps you save your time energy and effort. Do not get trapped. My last advice: all we need is a little self-confidence, we are PhDs for God sake, we deserve to have good jobs in great companies!

I was completely new to coding before this course and it was very hard, one of the hardest things I've done in my life, but I learned an enormous amount and I'm really glad I did it.
Some of the teaching styles really didn't work for me and I gave my feedback... Read More to the tutors and they were super quick to change things to help me complete the course.
The course is heavily marketed as being able to land you a job by the end of it, giving statistics that 80-85% of students find a job within 4 months of graduating. However, I find that a bit of an overstatement, given that only a small portion of students in cohorts ahead of me have found jobs. It just doesn't quite line up with the stats. But I think this could be massively remedied by providing graduates with more individualised support and following up with them later down the line.

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... Read More 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... Read More 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... Read More 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.