Data are becoming the new raw material of business
The Economist

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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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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Ranked: 15 Python Packages for Data Science

Cover of Python Packages for Data Science

At The Data Incubator we pride ourselves on having the latest data science curriculum. Much of our course material is based on feedback from corporate and government partners about the technologies they are looking to learn. However, we wanted to develop a more data-driven approach to what we teach in our data science corporate training and our free fellowship for
Data science masters and PhDs looking to begin their careers in the industry.

This report is the second in a series analyzing data science related topics, to see more be sure to check out our R Packages for Machine Learning report. We thought it would be useful to the data science community to rank and analyze a variety of topics related to the profession in a simple, easy to digest cheat sheet, rankings or reports. Continue reading

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