Data are becoming the new raw material of business
The Economist

How Foundations Student Russell Martin got into The Data Incubator’s Fellowship

At The Data Incubator we run online data science courses for busy professionals, as well as a free, eight-week Data Science Fellowship Program for PhDs and Master’s graduates to bridge the gap between academia and industry.

Our Data Science Foundations online course is the perfect way to launch a new career in data science, or supercharge a current career with new data science skills. It’s also the best way to prepare for our highly selective Fellowship program. Russell Martin, after completing Data Science Foundations, also completed our Applied Machine Learning online course and then joined the Data Science Fellowship for Summer 2018 in Washington, DC. Russell’s next step will be joining The Data Incubator team as a Data Scientist in Residence at our Washington, DC location.

 

Can you tell us a little bit about your background?

I’ve always been interested in Mathematics, and found that I had a certain knack for it. So my educational and professional background is really based in Mathematics – I studied for my Master’s at Clemson University and my PhD at the Georgia Institute of Technology, both in Applied Mathematics. Then I returned to England for my Postdoctoral research, as a Fellow at the University of Warwick. A few years later I was able to secure a position at the University of Liverpool there, as a Lecturer – equivalent to an “Assistant Professor”, here in the US.
 

How did you find TDI’s Data Science Foundations, and what made you decide to try it out?

While I was always involved in mathematics in academia, I found a real interest in computer science as well and focused a lot of my work on both mathematics and theoretical computer science. Over time, I started to look for data science courses online, to supplement my research work with new skills. That’s how I found the Data Science Foundations course. Most of the programs I was finding, that seemed like they were any good, were upwards of ten to fifteen thousand dollars, or even more. When I learned about TDI’s Foundations course, I felt that the price was fair and I was happy with the structure of the course.

 

Did you take any other online courses from The Data Incubator?

After completing Foundations, I decided to continue with the Applied Machine Learning course. The course was challenging, especially being in the UK at the time. While the course runs in the evenings for US time zones, it ends up being about midnight in the UK. So it was really helpful that the live lectures were recorded, in case I had to go to bed early on a certain night or if I felt like I missed something during a lecture. I found that the Machine Learning course really built on the knowledge and skills I had gained from Foundations.

 

Did you know about TDI’s Data Science Fellowship program before taking our Foundations course?

Yes, I knew about the Fellowship but I wasn’t necessarily planning to apply when I joined Foundations. I was planning mostly to stay in academia and utilize my data science skills to benefit my academic research work. Becoming a Data Scientist was something I had considered but hadn’t really been pursuing. However, taking these online data science courses very much solidified my interest in pursuing data science as a career, outside of academia. So I decided to apply to the Data Science Fellowship Program.

 

Did TDI’s online courses help prepare you for success in the Fellowship?

Definitely. Prior to the Data Science Foundations course I had no experience with pandas dataframes. The Fellowship curriculum, as well as the online courses, is based in Python and pandas is one of the primary libraries we use for data analysis. Also, I became very familiar with workflows for machine learning through the online courses. This is another central concept that is taught in the Fellowship. Having prior experience with each of these things really set me up to be able to come into the Fellowship program without having to start fresh and learn all new concepts while working on implementing them into projects at the same time. In the later weeks of the Fellowship, we learned a lot more things I wasn’t so familiar with at the time – but having that background already really helped with learning those new concepts even more quickly.

 

Lastly – tell us a little about your new job!

I’ll be joining The Data Incubator as a resident Data Scientists and instructor, actually – in Washington, DC. When I was accepted into the Fellowship, TDI reached out and asked if I would be interested in working as an instructor after graduation. My previous experience as a professor, I think, lends itself to a career in teaching data science. And with TDI, I’ll be teaching the tools and techniques that are at the cutting edge of the data science industry.

 

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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.

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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.

 
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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.

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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.

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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.

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Analyzing the Language of Twitter: Alumni Spotlight on Marc Ettlinger

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. Marc was a Fellow in our Spring 2016 cohort in San Francisco who’s now working at Google as a Computational Linguist.

 

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

I started life as a programmer, then I went back to graduate school at UC Berkeley for linguistics where I actually didn’t use my programming skills for a while. From there I did a neuroscience postdoc, where I started to use my programming skills a bit more. The neuroscience endeavor is, in many ways, a big-data endeavor. You get a tremendous amount of data from doing neuroscience experiments, and figuring out how to interpret and make sense of that incredible amount of data requires techniques that are not typical common in the behavioral scientific world, but are quite typical of machine learning and related data science fields. The way I would use those techniques as a scientist were not particularly sophisticated and while there is a lot of data when you’re analyzing neuroscience, it’s still orders of magnitude less than what people typically think of with big data.

So, when I started looking for job opportunities outside of academia, I realized that the way I talked about data analysis and the techniques that I used were not up to date. I wasn’t using the latest methodologies, tools, and terminologies that data scientists used even though the basic concepts were much the same.
 

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

The key thing was being able to talk about data science intelligently in a way I hadn’t before, during interview. I was able to update my knowledge to where the field and industry currently is, which helped tremendously talking with prospective employers. I also learned about some ideas and concepts that helped make me be a better data scientist, reflecting the latest research within the field.

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

 

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 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. 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 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. 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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