Data are becoming the new raw material of business
The Economist

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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Medical Image Classification: Using a convolutional neural network to identify anatomically distinct cervical types

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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The Many Facets of Artificial Intelligence

artificial-intelligence-2228610_960_720When you think of artificial intelligence (AI), do you envision C-3PO or matrix multiplication? HAL 9000 or pruning decision trees? This is an example of ambiguous language, and for a field which has gained so much traction in recent years, it’s particularly important that we think about and define what we mean by artificial intelligence – especially when communicating between managers, salespeople, and the technical side of things. These days, AI is often used as a synonym for deep learning, perhaps because both ideas entered popular tech-consciousness at the same time. In this article I’ll go over the big picture definition of AI and how it differs from machine learning and deep learning. Continue reading

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Developing a Foundation for Data Science: Alumni Spotlight on Ryan Jadrich

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.  Ryan was a Fellow in our Fall 2016 cohort who landed a job at Austin based startup OK Roger

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

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

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Predicting Visa Wait Times: Alumni Spotlight on Sudhir Raskutti

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.  Sudhir was a Fellow in our Fall 2016 cohort who landed a job with one of our hiring partners, Red Owl

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

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.

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

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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4 Data Science Projects That We Can’t Get Enough Of

LI3Y5U376XAt The Data Incubator we run a free advanced 8-week fellowship for PhDs looking to enter the industry as data scientists.  

As part of the application process, we ask potential fellows to propose and begin working on a data science project to highlight their skills to employers.  Regardless of whether you’re selected to be a fellow, this project will be instrumental in attracting employer interest and highlighting your skills.  Here are some projects that we would love to see, and that we hope to see you take on as well.

 

Multi-Axial Political Analysis  

We often think of American politics in terms of a single axis: left versus right, democrat versus republican.  In reality, the parties are composed of varying factions with different identities and political priorities and American politics is actually broken along multiple axes: foreign policy, social issues, regulation, social spending, education, second amendment, just to name a few.  Continue reading

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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

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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.

 

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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