I have a background in applied mechanics and engineering. My Ph.D. research simulated the response of randomly structured material, from which I learned a lot about statistical analysis, numerical computing and model development. Moreover, my academic experience fostered in me a “curiosity in data”, which I think is the most important quality for a data scientist.

During the program, I got the chance to learn what “data science” really is as an insider. In addition to those data analytics skills, I learned about how data science is applied in different industries, what qualities employers are looking for in a data scientist, what are the “front end” and “back end” of a data science project are and what are the associated skills with each stage. Only after those closer views, I can know what my strength and interest are and how I should prepare for my future career path.

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My background is in Geosciences. I was a climate modeler so I had a substantial amount of experience with scientific computing (numerical linear algebra, differential equations, data assimilation, etc).

Two things. First, I got my first exposure to data science in a non-academic setting: what sort of problems data scientists might be tasked with solving within a company, and the tools they use to do so.

Second, and more importantly, I got to know a fantastic group of people and make valuable connections. I got the interview for my current position based on a recommendation from a friend from my cohort who had been hired prior to me (we now work together, which is great!). Recently, I got another email from a friend indicating that he would be happy to refer me to his company if I wished. I don’t know if I just happened to have been part of a particularly great cohort, but I really did have a blast going through the incubator with them, and I look forward to keeping in touch with all of them for years to come.

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My formal education is in physics, but I’ve also done a lot of my research at UC Berkeley’s Computer Science department. I cherish both of these backgrounds equally since I learned how to “do science” from physics and build really cool things from computer science. I think this is a winning combination for a data scientist since a lot of companies are looking for scientist who can write code.

Two things, and both are equally important. First, TDI gave me the exposure to their hiring partners that I just couldn’t get on my own. Before starting the fellowship I had spent over 10 months applying to jobs on my own with a call back rate of 3%. At TDI, my call back rate shot to 90% and I even began fielding unsolicited interviews. I think having the TDI mark of approval certainly moved me up in the stack of resumes. Secondly, I expanded my professional network by becoming close friends with twelve other fellows who are all going to be doing fantastic things in the future.

 
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I received my M.S. in Reliability Engineering from the University of Maryland. In Reliability Engineering, a practitioner will assess/prevent the failure of a physical system (car, computer, etc.). Many of these approaches tend to be statistics and data driven and much of the modern research in the field (including my own) uses Machine Learning to improve relevant analyses. However, when I was done with my Master’s, I realized I was more passionate about the Data Science/Machine Learning than the engineering side. So when I heard about The Data Incubator, it seemed like a great fit.

As a recent MS graduate who had never worked before, the most important thing I learned at The Data Incubator was how to think like a Data Scientist. Since Data Science is still a new field, many positions require a unique and not necessarily homogenous set of skills. The Data Incubator not only teaches its students all the necessary technology, but it teaches them how to think about Data Science problems in a systematic and effective way. TDI also provided a network of possible employers and former alumni that proved valuable for my job search.

 
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I was a research mathematician and professor before deciding on a career change. Having a deep knowledge of math really helps me understand how things work, whether it is the theoretical ideas behind fancy algorithms or reading a piece of code and deciphering what it does.

Confidence in my code-writing ability. A polished resume and an important talking point with employers in my capstone project.

 
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My education includes a B.S. in Physics from Drake University and a Ph.D. in Astronomy from the University of Texas at Austin. After grad school, I went overseas for a Research Fellowship at the University of Nottingham. Astronomers do a lot of coding relative to other fields, and having been coding in Python since 2006 for work, I was very familiar with the Python SciPy stack. Since 2014, I have also been volunteering time to data science and software engineering projects for a people analytics startup. This was extremely useful because it provided references in industry who could vouch for my data science skills.

I got several useful things out of The Data Incubator: Strategies for resume writing, experience building and deploying a live web application, and a comprehensive set of IPython notebooks that encapsulate the advanced features of scikit-learn, SQL, and big data tools (Hadoop, Spark).

 
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My background is in neuroscience; specifically I studied how images are processed in the visual system of biological brains.

My goal was to determine if one can build an app that guides poorly performing taxi drivers so they can increase their hourly wage. One important decision that a taxi driver makes is the choice of where to go to look for customers. A naive strategy is to simply not move, and wait in the same general area until a customer arrives. Our algorithm determines where a taxi driver should search for customers by looking at the behavior of good drivers. It also factors in driving time with traffic and the cost of gas.

 
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I received my PhD degree from The Ohio State University majoring computational chemistry. For my PhD research, I developed multiple predictive models and published web servers to solve various biophysics problems using machine learning and statistical methods in Python, R and Matlab. The data science skills and experiences I gained in my 5 years of PhD not only allow me to solve the fundamental scientific problems effectively and efficiently, but also enable my transition from academia to industry to solve the real-world challenges.

The 8-weeks intensive training at The Data Incubator 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 with the cutting-edge analytics techniques, including programming, machine learning, data visualization as well as business mindset. Last but not least, I believe the networking with other very talented fellows are the most valuable thing I got out of TDI!

 
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My PhD and postdoctoral work was in the field of statistical mechanics with a strong emphasis on the design of new colloidal materials. Such research has required me to develop a hybrid set of strong analytical math and computational skills—of which have been extremely useful for bridging into Data Science. From the deeper level understanding afforded by this mixed skill set, I feel well posed to leverage existing technologies as well as develop novel alternatives. As an example of the latter, my forays into the fundamentals of Machine Learning helped me to develop a super-computing application capable of inferring the inter-particle forces an experimentalist must engineer to elicit a desired material property. This required the development of both an analytical framework and an underlying large scale molecular simulation element. Combining these general technical skills with what I learned at The Data Incubator, I feel well poised to be successful in a Data Science position

Firstly, I cannot emphasize how impressive the breadth and depth of the material is that one learns in only eight weeks. I have gained practical hands on experience with topics ranging across fundamental algorithms, machine learning, web scraping, time series, and wrangling large data sets using Spark and MapReduce. As a corollary to this though, the program requires a mindset shift away from traditional academics where the goal is to achieve the best, publication quality result or model and time constraints are less stringent. Specifically, a much more pragmatic perspective is required for success in the program, namely, judging when the product is good enough for the goal at hand. So in conclusion, while the technical course material is absolutely phenomenal, I must give a special mention to this soft skill teaching (as well as the many others).

 
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I am a PhD in computational astrophysics from Princeton University with a background in electrical engineering. Astrophysics gave me both a reasonably strong problem solving background as well as the ability to deal with quite terrible data.

The Data Incubator was really useful in a few ways. Firstly, I got a broad brush overview of the tools and technologies most commonly used in Industry. Obviously in 8 weeks, you’re not going to learn all of the tools and concepts in depth, but the Incubator was good at giving a frame of reference for asking deeper questions. More importantly, it was really good at setting up a network and providing a framework for reaching out to employers. It’s a huge advantage to meet employers face to face before reaching out to them, and to have something to show to them and talk about.

 
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