Isac Artzi is an associate professor and computer science program lead at Grand Canyon University. His research interests are in data science and environmental sustainability. Additionally, he has completed research in virtual and augmented reality learning environments and machine learning algorithms. Part of his work includes mentoring research teams and capstone projects.
Isac Artzi’s Introduction to Data Science
How did you begin your career in data science?
Around 2007 I started to become interested in learning styles of students. The individual learning styles and preferences are not accommodated by current educational models. Consequently I started to research adaptive algorithms that could learn the students’ preferences and provide content that matches their styles. I was not thinking about data science at the time, but in retrospective, my interest in machine learning became my official entry point into data science.
What inspired you to learn more about data science, specifically data science and environmental sustainability?
Over the past few years we are becoming surrounded by devices that produce data (phones, smart lightbulbs, security cameras, backyard sensor-driven sprinklers, etc.). Our natural and human-made environments “talk” to us continuously. Yet, we mostly complain about them: climate change, noise pollution, traffic congestion, and many others. If all this communication is treated as simply data, it can be analyzed and converted into actionable information. The actions can be translated into (adaptable) regulatory systems and devices. Thus we could stop complaining and start mitigating environmental challenges.
What do feel are some common misconceptions about data science or your work in general?
Often data scientists are viewed primarily as statisticians. While this is indeed the case with many data scientists, a large group of us are computer scientists, with an interest in developing computational tools that go beyond the statistical analysis of data. Data science is directly connected with Internet of Things (IoT) devices, which need to be programmed to collect data and respond to changing conditions.
Data Science and Environmental Sustainability
What value do you hope your research into data science will eventually provide?
All research is meant to find answers about a lesser known domain. The uniqueness of data science is that it focuses on quantifying phenomena and translating them into streams of numbers. Then we can analyze these phenomena in a very abstract, mathematical way, without the bias imposed by the research context. I hope to contribute to the simplification of the understanding of how our surrounding world works.
What are some tools that you use to conduct your research?
As a computer scientist I am immersed into the world of Python and R. Both are endowed with a plethora of packages like Scikit-Learn, Pandas, Matplotlib, Weka, NetworkX. For Python I do all my work in PyCharm, while for R I use RStudio and Shiny.
What do you look for when hiring Teaching Assistants and Research Assistants?
There are three essential characteristics: passion for helping students, ability to explain complex concepts in simple terms, and mastery of all software tools required by a class or research project. In some cases, when I engage young assistants in the early stage of a project, I can afford to bring in someone less experienced but with potential, whom we can mentor over time.
What advice would you give to students who aspire to be data scientists?
First you need to test your relationship with math: do you tolerate it or do you absolutely love it? After you established that you love math, you must ask yourself whether you are OK at it or really good? After you established that you love math and are really good at it, assess your ability to write computer programs. Once you established all that, you can be confident that you meet the minimal mechanical criteria to potentially become a data scientist. Now you can pick a science field and develop the skill of translating scientific challenges into quantifiable data. If you are still excited and passionate, start reading the literature on machine learning, computational science, bio-informatics, big data, sensors, and smart cities, to name a few.