Data are becoming the new raw material of business
The Economist


Engineering a New Career in Data Science: Alumni Spotlight on Abhishek Mishra

At The Data Incubator we run a free eight-week Data Science Fellowship Program 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. Abhishek was a Fellow in our Winter 2015 cohort in Washington, DC who landed a job as a Data Scientist at Samsung SDS.

 

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

I have a PhD in electrical and computer engineering from Lehigh University. I already had a good background in probability, statistics, and mathematical optimization that helped me in understanding the essence of data science. I was part of the Electrical and Computer Engineering Department and I was working on making the Internet more secure, by developing theoretical frameworks for timing attacks on anonymous networks such as TOR.

One of the key contributions of my PhD work was that I was able to find the closed form characterization of maximum achievable anonymity in a simple Chaum Mix (the basic building blocks of an anonymous network).

One important thing that I learnt from my PhD work was how to do research. This process consists of following four parts:

  1. Find out an interesting problem to work on.
  2. Formulate the problem in a concrete mathematical framework.
  3. Find out the mathematical tools required to solve the problem.
  4. Convince your adviser and the world that you solved an important problem worth publishing in a reputed conference or journal.

This whole process allowed me to work on an unstructured problem. I had to keep my eyes open to find the problem in the domain I was working on. This whole process helped me to become a better data scientist by just changing the role a little bit. Now I keep an open eye for finding any interesting patterns in the data. Anything that is unexpected is interesting.

 

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

There are tons of things that I got from The Data Incubator. First, The Data Incubator introduced me to a nice and comprehensive overview of the current techniques in data science. That includes everything from linear regression to Spark. Second, by hearing from many different companies, I got a feeling for the different types of problems that they tackle and how data science offers real solutions in a variety of fields. The Data Incubator exposed me to how data science is actually used by companies. Finally, The Data Incubator helped expand my network both by introducing me to companies looking for data scientists, and by introducing me to the other Fellows.

Continue reading


Asking the Right Questions: Alumni Spotlight on Suchandan Pal

At The Data Incubator we run a free eight-week Data Science Fellowship Program 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. Suchandan was a Fellow in our Fall 2016 cohort in San Francisco who landed a job at our hiring partner, Argyle Data – now Mavenir

 

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

I did my PhD in a part of mathematics known as number theory/arithmetic algebraic geometry. I’ve always been drawn to difficult and impactful problems, and my training has provided me with invaluable skills that I use use in problem solving everyday.

Knowing techniques and tools is important, but asking the right questions (and knowing which to avoid) is often what makes the difference between a problem you can solve, and one that remains intractable. For example, there have been many times where choosing the right strategy or perspective has made extremely difficult conjectures appear “natural” in number theory/arithmetic algebraic geometry. I have always found my experiences in mathematics to give me skills that guide me in problem solving outside of mathematics, and for that I am very appreciative.

 

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

I enjoyed learning from Robert, the instructor of the San Francisco cohort. I also liked that Fellowship program gave me exposure to different sectors of industry.

 
Continue reading


Predicting Ride Times with Machine Learning: Alumni Spotlight on Yutong Pang

At The Data Incubator we run a free eight-week Data Science Fellowship Program 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. Yutong was a Fellow in our Spring 2016 cohort in New York City who landed a job as a Machine Learning engineer at Apple.

 

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

My background is in computational physics. Throughout my education and during my doctoral research, I found that I have a great interest in data data analysis and the beauty of using models to predict things. So I decided that I wanted to be a Machine Learning Engineer, and a Data Scientist.
 

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

I think the most important thing is the Capstone project that I did during the Fellowship. I learned a lot of things from doing the Capstone protect, both from self-learning and also the Fellowship lectures and projects. So I think the Capstone project is the most important thing.

Continue reading


A Psychologist Takes on Data Science: Alumni Spotlight on Aleksandr Sinayev

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. Aleks was a Fellow in our Fall 2016 cohort in New York City who landed a job with our hiring partner, Via.

 

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

I was trained as a quantitative psychologist with a specialization in machine learning. In graduate school, I spent a lot of time thinking about experimental design and statistical analysis. My statistical toolbox emphasized frequentist and Bayesian approaches to hierarchical modeling. But I got exposure to a variety of methods like supervised and unsupervised machine learning, robust modeling, generalized linear and non-linear models, etc. I think the most useful things for data science were the most basic things: undnerstanding the linear and logistic models deeply, having a skeptical approach and maybe most importantly, being able to read and write math. The latter enabled me to quickly pick up new methods and read and understand relevat articles. Data science is a field of constant learning.
 

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

The program puts you in touch with excellent employers and in many cases allows you to skip the resume-scanning stage of being considered for a job. It also introduces you to very talented people in a similar stage in their careers – a network of brilliant PhDs aspiring to be data scientists. Everybody has unique pieces of the illusive data science credentials and is eager to teach everybody else what they know. The weekly miniprojects were a great way to facilitate interaction and learning. Because the student pool is so diverse in terms of the origin disciplines, everybody has different strengths that you can learn from.

Continue reading


Turbulence Theory to Data Science: Alumni Spotlight on Liang Shi

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. Liang Shi was a Fellow in our Fall 2016 cohort in Washington, D.C. who landed a job with our hiring partner, Afiniti.

 

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

I obtained my PhD in turbulence theory at Max Planck Institute for Dynamics and Self-Organization in Germany. Afterwards I did a postdoc on turbulence modeling of atmospheric boundary layer flows at the National Institute of Standards and Technology. Both problems require extensive numerical simulations and data analysis using parallel algorithms and computing. The largest simulation that I have performed, ran on 5000 cpu cores for 3 months, generating around 10 Terabytes of data. These experiences gave me my first contact with ‘big data’ and equiped me with a toolset of data analysis. Most importantly, as a scientist, I am extensively trained on asking the right scientific questions, designing the experiments or simulations, using the good visualiztion tools to explore the data, and then giving nice presentations to deliver the findings. These are actually the essential savoir-faire to be a data scientist.
 

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

Since I had always been in academia before, TDI is like a window to the industry, a bridge walking me smoothly from the academic world to the industry world. Through a series of activities like panel discussions and the alumni party, TDI offered me a great platform to know what kind of problems companies are trying to solve, what skills they are looking for, what the daily life looks like, etc. Moreover, TDI provides valuable guidance in the whole process of job searching, and last but not least, the chance to work with a bunch of very smart people.

Continue reading


From Mathematics to Freddie Mac: Alumni Spotlight on Daniel File

At The Data Incubator we run a free eight-week Data Science Fellowship Program 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. Dan was a Fellow in our Summer 2015 cohort in Washinton, DC, who landed a job with one of our hiring partners, Freddie Mac.

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

I have a Ph.D. in pure mathematics, and my dissertation was in number theory. After graduating I was a visiting assistant professor at a large university and later at a liberal arts college. Doing mathematics taught me to think deeply about hard problems, but I was lacking some of the skills I would need to be a data scientist. To prepare for The Data Incubator and a career as a data scientist, I looked for opportunities to improve my statistics and coding skills. For example I helped my department develop a statistics curriculum using R, and I supervised undergraduate research in number theory using C and Python.

 

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

The Data Incubator is incredibly good at recruiting highly motivated people who are very talented to be Fellows, and then introducing those Fellows to employers who want to hire a data scientist. This happens through Partner Panels, Happy Hours, Pitch Nights, and the growing network of alumni. I learned a great deal during the program, and much of that happened by interacting with the other Fellows. I also got a lot of great feedback from the other Fellows about my capstone project. The Data Incubator gave me support for job placement by helping me write a great resume, giving me advice about contacting employers, and doing interview preparation.

Continue reading