Pragmatic Institute

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.

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. 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!