While pursuing my PhD, I surveyed a community in India whose drinking water had been contaminated by arsenic. My goal was to incorporate socioeconomic, demographic, and other sociopsychological factors into models that could predict the likelihood that communities would adopt arsenic‐mitigation technologies. I extracted various topographic and hydrogeological parameters from remote‐sensing images and ultimately developed an arsenic contamination prediction model. Although this work could be useful at the regional level, a global prediction model was also needed, because arsenic contamination is an international public health issue, affecting more than 100 countries. To accomplish this, specialized skills in cloud computing, big data, and advanced analytical skills such as machine and deep learning were required. Later, when looking for relevant job opportunities, I initially focused on academia but came away empty‐handed. I therefore expanded my search beyond academia, hoping to take on whatever new challenges other types of positions might offer. And it was during this time that I began to seriously consider a career in data science – an exciting profession that offers job satisfaction, career growth, intellectual development, and financial stability.
Data scientists are specialists who extract insights from large, complex datasets and present them in easily interpretable ways to a variety of non‐technical audiences. Small‐ to large‐scale companies need data scientists who can help with decision making, expand their business, and launch their products and services. Experts who apply data science techniques to develop environmental prediction models are called environmental informatics or environmental data science (EI/EDS) professionals, a suitable career path for those who may have originally aspired to positions in academia.
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