Alex Baransky is a data science instructor and bootcamp manager at NYC Data Science Academy. He has received his degree in Environmental Biology from Columbia University. He is an expert in multiple computer languages including Python, R, and SQL. As an engineer at heart and biologist through training, Alex is passionate about animal behavior and finding innovative ways to use data science in the field of biology.
We met with Alex last week to get some useful insights about the data science industry and some advise to those new to the field of Data Science.
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Tell us a bit about your background and interests
I have a degree in Environmental Biology, and I have a passion for learning about all types of animals and their behavior. I am particularly interested in different methods of animal communication. I picked up coding (Java) as a hobby when I was in high school. Later in my studies, I moved on to other languages such as Perl and R. I moved on to Python after I finished my degree. I enjoy reading fantasy, playing guitar, and solving problems with code.
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What is a recent trend in Data Science?
With the ever-increasing rate of data production, the regulations governing how personal data is collected and utilized have been changing. Large companies like Facebook, Amazon, and Google collect an enormous amount of data on a daily basis and it is the responsibility of not only the user but also the data scientist to contribute to discussions surrounding what limits should be put on data collection and how we define ethical data utilization. In response to the changing regulations, cyber security will play an even larger role in the industry.
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What appears to be the main issue a total beginner neglects when approaching Data Science?
A responsible data scientist must not only explain the ‘what’, but also the ‘why and how’. Results from analysis are only actionable if they are reproducible. This is where the “science” in data science comes in. Data scientists must adhere to the same professional and ethical standards as any other scientist. This is a key point to remember to be successful in the industry.
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What can someone do to build their data science skills?
Keep learning! Data science is an ever-evolving field which means there are constantly new tools and techniques that are being developed. Visit popular data science discussion sites and stay up to date on different programming tools to make sure you always have a competitive edge.
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For someone looking to transfer from a different industry, what should they be aware of in order to be successful at their transition?
Coding can be intimidating for someone who hasn’t spent much time working on problems that involve mathematics and logic. Make sure to take your time when learning the basics so that get a good grasp of the fundamentals before moving on to more advanced topics. For those who really struggle with coding, make sure to know your strengths and how they can be applied to show your value and potential. You don’t always need to be a math whiz or collaborate on the scikit-learn library to contribute to data science. However, you do need to find your fit and understand what skills you can bring to the industry.
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What’s one piece of advice to aspiring data scientists?
A complete understanding of the basics is more impressive than a weak understanding of more advanced topics. It’s easy to get distracted by the shiny new data science tools and the sexy buzz words, but make sure that your foundation is rock solid before moving on, otherwise your tower of knowledge can easily collapse when someone inevitably tests it.