Data are becoming the new raw material of business
The Economist

Making the Switch from Network Physics to Data Science: Alumni Spotlight on Hernan Rozenfeld

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. Hernan was a Fellow in our Fall 2015 cohort in New York City who landed a job with our hiring partner, 1010data, in New York City.

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

My background is in complex network and statistical physics. My PhD studies focused mostly on theoretical modeling of networks and their topological properties. Later on, during my postdoc, I worked primarily on using those networks and graph theory techniques to analyze real-world data.

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

I think the most important tool I learned is Machine Learning. Before coming to The Data Incubator I only knew conceptually what ML was. This fellowship gave me a much deeper understanding of the different ML techniques, and maybe more importantly hand-on experience using the different ML tools on real-world data.

I also learned a large number of tech tools, such as Hadoop and MapReduce which are essential for the analysis of very large amounts of data.

Last, but not least, the Incubator helped me to have a more business oriented thinking of problems. In a business environment conclusions must be concrete, translate into actionable items, and easily communicable. TDI helped me transition from an academic view of problems to a business/actionable approach.

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An Indirect Route to Automotive Technology: Alumni Spotlight on Alex Thompson

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. Alex was a Fellow in our Fall 2015 cohort in Washington, DC who landed a job with our hiring partner, NAUTO, in Palo Alto, California.

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

I went in a straight line for 28 years, and then zig-zagged all over the place. I pursued and received a PhD in Math from UCLA, which culminated two decades of focusing on math. However, during my grad studies I developed other interests, and following grad school I did a lot of political activism and founded a not-for-profit bicycle shop. After that I worked in K-12 Education for 3.5 years, first at Green Dot Public Schools, then at McGraw Hill Education. That gave me a lot of business experience that has proved to be useful connecting the technical side of data science with the business side.

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

It helped me get from the stage of unconscious incompetence – not knowing what you don’t know about data science – to conscious incompetence – knowing what you don’t know, and knowing how to fix that. After five hard weeks of homework, you have some pretty good skills, but more importantly, you have a good idea of where you need to spend time learning, and how to learn. If I was an employer, I would feel comfortable hiring people who have been through The Data Incubator, since they are (a) accomplished hard workers and (b) have shown a willingness and ability to learn a very new field.
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Finding a Curiosity in Data: Alumni Spotlight on Jun Zhang

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.  Jun was a Fellow in our Winter 2017 cohort who has moved to Germany for a job with our hiring partner, Boehringer Ingelheim

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

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.

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

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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The Value of Prioritizing Python: Alumni Spotlight on Aviv Bachan

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.  Aviv was a Fellow in our Fall 2016 cohort who landed a job with our hiring partner, Argyle Data

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

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).

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

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. Continue reading


Predicting Which Bills Will Become Laws, with Data Science: Alumni Spotlight on Michael Yen


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 2017 cohort in San Francisco, who landed a job with one of our hiring partners, Cerego

 

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

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.

 

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

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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Learning to Think Like a Data Scientist: Alumni Spotlight on Ceena Modarres

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.  Ceena was a Fellow in our Winter 2017 cohort who landed a job with our hiring partner, Capital One

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

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.

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

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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Taking on Data Science with Mathematics: Alumni Spotlight on Brian Munson

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.  Brian was a Fellow in our Winter 2017 cohort who landed a job with Quantworks

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

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.

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

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

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Bringing Astronomy Down to Earth: Alumni Spotlight on Tim Weinzirl

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.  Tim was a Fellow in our Spring 2017 cohort who landed a job with one of our hiring partners, First Republic Bank

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

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.

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

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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Using a convolutional neural network to identify anatomically distinct cervical types: Alumni Spotlight on Rachel Allen

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.  Rachel was a Fellow in our Spring 2017 cohort and an instructor for our Summer 2017 cohort. 

My background is in neuroscience; specifically I studied how images are processed in the visual system of biological brains. 14903037435-rachel_kay_allenFor my capstone project I knew I wanted to use an artificial neural network to create an image classifier. Intel and Mobile ODT released a large dataset for a medical image classifying competition around the same time I was brainstorming possible projects. Their dataset included thousands of medical images of cervixes that were labeled by medical professionals as one of three types based on anatomy. Healthcare providers often have difficulty determining the anatomical classification of a cervix during an examination. Some types of cervixes require additional screening to determine if pathology is present. Thus, an algorithm-aided decision of cervical type could improve the quality of cervical cancer screening for patients and efficiency for practitioners.

I began my project with some exploration of the images. I used t-SNE (t-distributed stochastic neighbor embedding) in scikit-learn, which is a tool to visualize high-dimensional data. Visualizing each image as a point in a 3-D plot showed that none of the three classes of cervixes clustered together. I also used a hierarchical cluster analysis in seaborn to confirm that the images did not easily group together by their three classes.

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How to Catch ‘Em All: Alumni Spotlight on Yina Gu

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.  Yina was a Fellow in our Winter 2017 cohort who landed a job with one of our hiring partners, Opera Solutions

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

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.

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

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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