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Abstract:
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When I was an undergraduate, majoring in Mathematics and Computer Science, data analysis across platforms and industries was still in its infancy. For me it wasn't until I started my first job, ironically as a software developer learning SQL database tools, when I realized the need for widespread statistical analysis across industry verticals. Consequently, my transition into data science as a career was somewhat backwards, from the real world then into education, going back to school for my master's in Statistics.
Industry asks the business questions that dictate the tools and data required to answer these questions, and students today have an ever-increasing number of open-source and licensed tools available to answer such questions. Though learning tools and techniques is important, without the ability to tell a story with data, the data warehouses, applications and fancy algorithms are lost if the information is not useful. As a recent graduate working in data science, I argue this art of a science takes time, experience and real-world questions to master!
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