Like most undergraduates I had little idea about I wanted to
do with my career. I enjoyed science, so
I studied the pre-med curriculum. After graduation I decided to work with a patient
population to evaluate if it was something I would really enjoy.
I was too impatient, and wanted to spend my time solving new problems. I landed in business school, and during my
first year I had the good fortune of working with a professor that was
building a data science driven web application.
Through the work I was exposed to other graduate students that were using machine learning
to develop projects like a hedge fund strategy using stock tweets data.
The
analytic way of problem solving I was introduced to as a research assistant
complimented what I was learning in business school. When learning strategy, I
understood how effective data analytics could help a company develop a
competitive strategy. When learning
finance, it was easy for me to understand how a technological shift and corresponding high growth market could drive
a company like IBM to make a large number of text-analytics acquisitions. Learning about economics helped me understand
how a labor shortage in data scientists could influence wages.
When it
came to thinking about what I would like to do after graduation, I decided that the best thing I could do for my career was work as a data scientist. Working as a graduate assistant had taught me that the most effective people had both a deep understanding of business problems, as well as the technologies that could be used to solve the problem. After graduation I wanted to ramp up my
programming skills and network in the San Francisco area, so I enrolled in the
Hack Reactor program.
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