Data are becoming the new raw material of business
The Economist

Tying Together Elegant Models: Alumni Spotlight on Brendan Keller

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Brendan was a Fellow in our Fall 2015 cohort who landed a job with one of our hiring partners, Jolata.

Tell us about your background. How did it set you up to be a great data scientist?144670722818-brendan_keller

I did my PhD research in theoretical condensed matter physics at the University of California, Santa Barbara. The focus of my research was on studying the phase diagram of chains of non-abelian anyons. Because such chains are gapless in most regions of the phase diagram we had to model them using very large matrices in C++. To make this computation more tractable we used hash tables and sparse matrices.  Besides my background in numerics I also took the time to learn Python, Pandas, SQL and MapReduce in Cloudera a few months before starting the fellowship.

What do you think you got out of The Data Incubator?

The Data Incubator gave me a solid foundation in data parsing, large scale data analysis and machine learning. I went into the fellowship already knowing about various concepts like SVM, bag-of-words and cross-validation. But I learned how tie these together into a elegant models that are both modular and easy to modify or upgrade. I also learned how to use Map Reduce on a cluster where the behavior of your program can be quite different then on a single node.  Continue reading

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The Details of Data Science: Alumni Spotlight on Ellen Ambrose

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Ellen was a Fellow in our Spring 2016 cohort who landed a job with one of our hiring partners, Protenus.

Tell us about your background. How did it set you up to be a great data scientist? 

The title data scientist implies both technical skills, as well as the ability to ask and answer questions scientifically. My academic decisions were driven by the desire to develop the second of these skills. I studied math in college because I was attracted to the logical way of thinking and proving concepts that I encountered in math class. I went on to do a PhD in neuroscience in part because I wanted to pursue a question to an extreme level of detail, leveraging the logic that I learned doing math. (I also wanted to understand how learning works at the level of neural ensembles, but that’s another story.)

As result of my academic trajectory I also learned to write analysis code. I enjoyed coding, and at first I considered it a perk of my particular field of neuroscience that a lot of coding (mostly in Matlab) was necessary for analyzing the large datasets I was collecting. However, I eventually came to appreciate coding in it’s own right and started taking steps to learn new languages and to improve my analyses by incorporating better tools. By this time I had realized that I would be happy doing coding full time, so The Data Incubator was a great segue way to new concepts and tools in the world of data science.
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Crunching Yelp Data to a Job at Crunchbase: Alumni Spotlight on Newton Le

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Newton was a Fellow in our Summer 2016 cohort who landed a job with one of our hiring partners, Crunchbase.

Tell us about your background. How did it set you up to be a great data scientist? 

I have an electrical engineering and computer science degree from UC Berkeley, which gave me a strong coding foundation. I am also almost done with a PhD in structural engineering at UC Davis, which gave me a lot of experience solving analytical problems computationally.

What do you think you got out of The Data Incubator?

Beyond the miniprojects and learning the data science tools available, The Data Incubator really made me see my potential. Being challenged to do something interesting with data, I came up with a fun idea that I had no idea how to execute, but I pushed myself to learn new tools and produce some useful within a few days. Seeing what others could do every week, I was inspired to add significant improvements to my capstone project and learned a lot each step of the way. Super smart instructors and talented peers really inspired me as well. Everyone had their strengths and I could learn something different from each person. The environment didn’t feel competitive at all and was actually very collaborative. I actually miss coming in every day to work with my pod. Go Team Lannister!  Continue reading

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Data-Driven Solutions for Agronomy: Alumni Spotlight on Lindsay Bellani

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Lindsay was a Fellow in our Summer 2015 cohort who landed a job with one of our hiring partners, DuPont Pioneer.

Tell us about your background. How did it set you up to be a great data scientist? 

I love biology — in particular, neuroscience — and I had every intention of pursuing a career in academia. I received my BS in biology from UNC Chapel Hill, and went on to study neurogenetics at The Rockefeller University in New York City. I decided to pursue a bit of a non-traditional PhD project — I wanted to understand why mosquitoes bite some people more often than others. Though I didn’t know it at the time, it was this choice that led me to a career in data science. I began by setting up a clinical study wherein we recruited hundreds of volunteers and tested them for attractiveness to mosquitoes. We then collected a bunch of different samples from each of them — everything from blood to questionnaire results. We wanted to understand which, if any, of these factors were predictive of mosquito attractiveness. At the end of the study, I was left with a whole lot of data and not a clue what to do with it. With the help of our University’s biostatistics department, in particular Joel Correa da Rosa, I learned how to use machine learning to do predictive modeling. It was a difficult, real-world dataset, and its analysis led to many interesting debates as to what was the best way to handle its various nuances. I began coding on my own to try new ideas, and eventually Joel and I became equal thought partners in the process. I actually ended up working out of the biostatistics office instead of my own lab for a few months before my thesis defense. Through this process, I began to love the art of data science, and I was encouraged to hear from others that I had a knack for it. It was all of the rigor and analytical-thinking and puzzle-solving that I loved about bench science, but even better. Seeing my enthusiasm and aptitude, my husband recommended that I apply for The Data Incubator. I kind of applied on a whim — I think I filled out the application the same day it was due.


I’m grateful for the path that led me to a career in data science. My background in biology has given me the ability to think scientifically about a problem — to understand the nuance of data collection, and how to design a good experiment, and which analyses might provide the biggest insights. Because I ran a clinical study and none of the members of my lab had a background in machine learning, I had to practice explaining this complex data science problem to non-technical audiences, which has been an asset when presenting results to the business side of the company I work for. It’s been a very natural transition, which I think speaks to what a good fit it is for my personality and talents.
From a research perspective, working in a vibrant academic setting also meant learning how to ask bold questions, even at the risk of sounding stupid in front of a large group of mentors and peers–something I’ve done more than I care to admit. For me, finding the right question to ask is just as important as having the technical expertise to find an answer, and that’s one of the things that makes Data Science so exciting.

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Turning Bold Questions into a Data Science Career at Amazon: Alumni Spotlight on David Wallace

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. David was a Fellow in our Winter 2016 cohort who landed a job with one of our hiring partners, Amazon.

Tell us about your background. How did it set you up to be a great data scientist? 

Before joining The Data Incubator, I completed my Ph.D. in chemistry at Johns Hopkins University, where I focused on the design and synthesis of new magnetic materials. My work gave me the opportunity to work alongside scientists in many different disciplines, and exposed me to a vast array of experimental techniques and theoretical constructs. From a data science perspective, this meant that I was constantly encountering new types of data and searching for scientifically rigorous models to explain those results. As the volume and complexity of these datasets increased, graphical data analysis tools like Excel and Origin weren’t making the cut for me, and I gradually made the transition to performing data transformation and analysis entirely in Python. That was a big technical leap that took a lot of time and frustration, but I think it ultimately made me a better researcher.

From a research perspective, working in a vibrant academic setting also meant learning how to ask bold questions, even at the risk of sounding stupid in front of a large group of mentors and peers–something I’ve done more than I care to admit. For me, finding the right question to ask is just as important as having the technical expertise to find an answer, and that’s one of the things that makes Data Science so exciting.

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Machine Learning and Modeling the Stock Market: Alumni Spotlight on Michael Skarlinski

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Michael was a Fellow in our Winter 2016 cohort who landed a job with one of our hiring partners, Schireson Associates.

 

Tell us about your background. How did it set you up to be a great data scientist? 

My PhD work was in computational materials science, where I worked with reactive molecular dynamics simulations. The field is totally simulation based, and typically requires high performance computing resources. Running these simulations helped build my chops for working with parallel systems and command line tools. The software required familiarity with some powerful languages and APIs like C and CUDA. Learning those definitely helped my understanding of Python once I converted to using it.

Toward the third year of my PhD I got really interested in machine learning. I started using scikit-learn to predict different aspects of simulations I worked on. These projects became a large part of my thesis and contributed to choosing The Data Incubator as a next step in my career.

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Calculating the Perfect Algorithm: Alumni Spotlight on Sumanth Swaminathan

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Sumanth was a Fellow in our Winter 2016 cohort who landed a job with one of our hiring partners, Revon.

 

Tell us about your background. How did it set you up to be a great data scientist?

I did my bachelors degree in Chemical Engineering at the University of Delaware and my PhD in Applied Mathematics at Northwestern University.  After some postdoctoral work between Northwestern and Oxford University, I went into industry as a quantitative consultant for W.L. Gore & Associates.  For the past 4 years, I have spent most of my time delivering technology solutions at W.L. Gore, teaching mathematics at the University of Delaware, and performing and teaching Indian Classical Music.  

On the question of what makes a strong data scientist, I think that the better practitioners in the field tend to be hypothesis driven, strong critical thinkers with hard skills in statistics, programming, mathematics, and hardware.  Hence, my background in engineering and mathematics, my consulting experience, and my years of teaching probably contributed the most to my success.  

 

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Making (LinkedIn) Connections: Alumni Spotlight on Xia Hong

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Xia was a Fellow in our Summer 2015 cohort who landed a job at LinkedIn.

 

Tell us about your background. How did it set you up to be a great data scientist? 

I am an experimental physicist in soft condensed matter by training in my PhD program at Emory University. There are three things that I think have helped me a lot to become a good data scientist:
1). The solid background in physics and math that I obtained back in my college. The knowledge itself isn’t necessarily reflected in my day to day work now. However, the training of logical thinking and critical thinking is really beneficial in a long run.
2). Persistence in finding root causes. The massive amount of data can easily leave you feeling swamped. I believe that always asking why until you get to the true cause of the problem is really essential. Sometimes, the insights are hidden behind and need our motivation to dig them out. No matter if it’s driven by natural stubbornness or original curiosity, I find the persistence usually a great help for walking the last mile to the final discovery.
3). Passion for solving problems using data. There is a joint program in our department where I took computer science courses for a masters degree. In the course projects, I started to find my passion in solving practical problems using data science approaches. Now I am working on product analytics and I cannot imagine how tough it could be without that passion and curiosity about what we can do to improve it.

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From Eco-Friendly Batteries to Random Forests: Alumni Spotlight on Matt Lawder

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Matt was a Fellow in our Winter 2016 cohort who landed a job with one of our hiring partners, 1010data.

 

Tell us about your background. How did it set you up to be a great data scientist? Matt Lawder

I defended my PhD dissertation at Washington University in St. Louis, a few weeks before coming to The Data Incubator. I was part of the MAPLE lab in Energy, Environmental, and Chemical Engineering (I know, it’s a mouthful). Our lab focused on physics-based electrochemical modeling, mostly geared toward Li-ion batteries.

For my main dissertation project, I studied how batteries age under different real-world cycling patterns. Most cycle life estimates for a battery are based on simple constant charge and constant discharge patterns, but lots of applications (such those experienced by batteries in electric vehicles or coupled to the electric grid) do not have simple cycling patterns. This variation effects the life of the battery.

Both through model simulation and long-term experiments, I had to analyze battery characteristics over thousands of cycles and pick out important features. This type of analysis along with programming computational models that were used to create these data sets helped give me a background to tackle data science problems.

Additionally, I think that working on my PhD projects allowed me to gain experience in solving unstructured problems, where the solution (and sometime even the problem/need) are not well defined. these type of problems are very common, especially once you get outside of academia. 

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Leveraging a Physics Background to Achieve Data Science Success: Alumni Spotlight on Andrew Yue

At The Data Incubator we run a free eight-week data science fellowship to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring data scientists. Andrew was a Fellow in our Fall 2015 cohort who landed a job with one of our hiring partners in Washington, DC.


Tell us about your background. How did it set you up to be a great data scientist?

Andrew YueI’m an experimental nuclear physicist by training. I had the great privilege to perform research at the National Institute of Standards and Technology (NIST) for nine years. NIST is a Department of Commerce laboratory that specializes in the science of measurement (metrology) and its application to industry. My research focused on precision measurement techniques with neutrons to advance our understanding of fundamental physics and to improve industry services offered by my group.

There are two things that I think have helped me get to where I am:

1) Like most physicists, I think I have a natural propensity to tinker with things well outside my expertise. Taken too far, this can be a bad thing. But, applied appropriately, it’s exactly the kind of attitude needed to learn and keep up with the ever-changing field of data science.

2) Having focused on precision measurements in my research, I’ve seen time and time again how much the environment in which I performed my experiments impacted the data and informed my analysis. The parallel to data science is that my training has taught me that a deep understanding of the problem and how the data was collected are what allow you to ask the right questions and produce meaningful results.  Continue reading

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