Data are becoming the new raw material of business
The Economist

Predicting Flight Delays with Random Forests: Alumni Spotlight on Stacy Karthas

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.  Stacy was a Fellow in our Winter 2017 cohort who landed a job with one of our hiring partners, AdTheorent

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

I received my Bachelor of Science degrees in mathematics and physics from the University of New Hampshire. I then went on to graduate school at Stony Brook University. I graduated with my master’s degree in Physics in December 2016. During my master’s degree, I did research in Nuclear Heavy Ion Physics with a focus on the analysis of gluons and their products as they traversed our detector. The data analysis, simulation, and clustering algorithms I worked on prepared me to become a data scientist because it was a physical application of many of the tools used by data scientists.

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

The Data Incubator gave me the chance to solidify my data science knowledge. It helped me pull together tools and concepts I had been using during all of my previous research experiences. I learned a lot of new machine learning concepts and how they could be applied to real world data.

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From Researcher to Algorithm Engineer: Alumni Spotlight on Anthony Finch

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.  Anthony was a Fellow in our Winter 2017 cohort who landed a job with one of our hiring partners, Afiniti

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

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I came into The Data Incubator with a Master’s degree in Computational Operations Research from The College of William and Mary. My Master’s program gave me a strong background in theory and in the practical application of machine learning, simulation, and optimization. I had a few internships as well, primarily in finance.

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

The Data Incubator 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. 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 The Data Incubator gave me a number of excellent opportunities. It may seem frustrating at times, but the partners really do want to hire Fellows, and The Data Incubator’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 the fellowship.  

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Analyzing Time Series Data for Parkinson’s Wearables: Alumni Spotlight on Jordan Webster

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. Jordan was a Fellow in our Spring 2017 cohort who landed a job with one of our hiring partners, IronNet Cybersecurity

 

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

My background is in particle physics. As a physicist, I analyzed large datasets of particle collision images, and I used machine learning tools to classify rare and interesting collisions.

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

At The Data Incubator I I learned a whole new toolset for approaching data analytics. I was exposed to new concepts like language processing and map-reduce, which never arose in physics. Furthermore, I was coached on how to best market myself to employers.

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Ride-sharing for Senior Citizens: Alumni Spotlight on Aurora LePort

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. Aurora was a Fellow in our Spring 2016 cohort who landed a job with Verizon Wireless

 

Tell us about your background. How did it set you up to be a great data scientist?Version 2

I obtained my Ph.D. in Neurobiology and Behavior from UC, Irvine in 2014. I collected data related to brain activity representing autobiographical memory using Magnetic Resonance Imaging (MRI) for my dissertation. The accurate analysis of MRI data demanded the ability to preprocess, and clean data as well as automate the processing steps using Matlab and R. Understanding how to properly use these tools was instrumental towards acquiring a new programming language (i.e. Python). Furthermore, the ability to apply statistical concepts to analyze various forms of data from diverse scenarios was highly conducive towards becoming a well-rounded data scientist who excels at analyzing novel datasets.

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Solving Interdisciplinary Problems with Data Science: Alumni Spotlight on Wendy Ni

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. Wendy was a Fellow in our Winter 2017 cohort who landed a job with one of our hiring partners, Facebook.

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

I have a PhD in Electrical Engineering from Stanford University, where I’m currently a postdoc.  My doctoral and postdoctoral research focus on the translation of novel magnetic resonance imaging (MRI) technologies to clinical neuroimaging applications, and the extraction of “hidden” imaging biomarkers from conventional clinical images.  In my research, I utilized my engineering, programming, study design, and communication skills to solve interdisciplinary problems with real-world impact.  I am now pivoting to data science, because I want to use my quantitative and analytical skills to discover hidden insights and guide decision-making for immediate applications in industry.

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Making the Switch from Management Consulting: Alumni Spotlight on Armand Quenum

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. Armand was a Fellow in our Fall 2016 cohort who landed a job with KPMG.

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

I received my Bachelor’s degree in Mechanical Engineering from NC State University. After college, I became a management consultant specializing in program and strategic management. As a consultant, I saw the value of data-driven decisions and extracting insights from data. As a result, I decided to go back to school to obtain my Master’s in Systems Engineering. There I was introduce to R Programming software, data mining techniques, and applications of optimization. My Masters not only exposed me to data science, but it also provided me a framework to approach complex problems.

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Standing Out as a STEM Graduate: Alumni Spotlight on Bernard Beckerman

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. Bernard was a Fellow in our Fall 2016 cohort who landed a job with Uptake.

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

I studied Materials Science and Engineering at Northwestern University for my PhD. Graduate school prepared me with an array of technical skills including programming, statistical analysis, and the ability to build, communicate, and defend a scientific argument. These are all important in producing data science products and presenting them to those at all levels of a corporate structure.

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

TDI helped me leverage my programming and critical thinking skills toward a career in data science by giving me essential skills and project experience that made me stand out from other advanced-degree STEM graduates. These include machine learning, parallel programming, and interactive data visualization. TDI also connected me to a cohort of accomplished students that has been a great support as I’ve started my career.  Continue reading

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The Science of Data Science: Alumni Spotlight on Paul George

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. Paul was a Fellow in our Fall 2016 cohort who landed a job with Cloudera.

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

Following the completion of my PhD in Electrical and Computer Engineering in 2009, I joined Palantir Technologies as a Forward Deployed Engineer (client-facing software engineer). There, I helped Palantir enter a new vertical, that of Fortune 500 companies, where I built data integration and analysis software for novel commercial workflows. I left Palantir in 2012 and in 2013 I co-founded SolveBio, a genomics company whose mission is to help improve the variant-curation process; the process by which clinicians and genetic counselors research genetic mutations and label them as pathogenic, benign, or unknown. At SolveBio, my work was primarily focused on building scalable data cleansing, transformation and ingestion infrastructure that could be used to power the SolveBio genomics API. I also worked closely with geneticists and other domain experts in a semi-client-facing role.

The theme of my six years as a software engineer has been to help domain experts, whether they be fraud investigators at a bank or clinicians at a hospital, analyze disparate data to make better decisions. I have built infrastructure in both Java and Python, have used large SQL and NoSQL databases, and have spent countless hours perfecting Bash hackery (or wizardry, depending on your perspective).

My experiences as a software engineer were very relevant to data science in that I learned many ways to access, manipulate, and understand a variety of datasets from a variety of sources in a variety of formats. As the adage goes, “Garbage in. Garbage out.” No more is this true than in data science. Performing good data science requires cleaning and organizing data, and I feel very comfortable with this process.

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From Astronomy to AI: Alumni Spotlight on Athena Stacy

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. Athena was a Fellow in our Fall 2016 cohort who landed a job with Brighterion.

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

My background is in astronomy.  My research consisted of developing and performing computer simulations of star formation in the early universe.  The goal of these simulations was to better understand what stellar clusters looked like in regions of the universe that telescopes cannot observe.  Thus I was already familiar with computer programming and visualizing data.  This was very helpful in the transition to data science.  Knowing how to present my research clearly to a range of audiences — both beginning students and other experts in the field — has helped as well!

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

Tons!  Just about everything I know about machine learning I learned at TDI.  I met lots of great, friendly, and supportive people through TDI as well.  This includes the instructors and mentors as well as the other fellows in my cohort, many of which I’m sure I will keep up with for many years to come.   Through TDI  I’ve also made contacts with other companies and data scientists in the San Francisco Bay area, which has been quite helpful in getting those job interviews!  Continue reading

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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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