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However, I was facing analytical challenges. To list a few, I was almost completely dependent on the companys internal data, and it was very difficult to decide where and how to find relevant, accurate data from credible sources outside the company. Next, analyzing data and information that continuously changed or was affected by too many factors, such as the performance and output of machineries, was problematic because such information was hard to standardize. Moreover, I needed a way to gather the necessary data automatically because mining data manually is time consuming and inefficient. I was thus curious about the way my suppliers and subcontractors handled these problems and explored their efforts.
I found a potential solution while I was examining a gas turbine for my power plant project at one of my suppliers, General Electric (GE). After speaking to its data scientist, I was surprised to learn about the companys application of data collection and analytics to the turbine. The sensors were installed to collect real-time data, which enabled GEs data scientists to remotely monitor the equipments performance from a centralized data center and improve its efficiency by applying analytical solution to optimize the turbines system. Based on this encounter with the companys smart features, I began exploring the potency of analytics. The connection between data and machine and the utilization of valuable information derived using analytical data was an innovation that could help me revolutionize my analytical methods. Once I realized that analytics is the key to overcoming my current analytical limitations, I became determined to learn more about statistics, mathematics, and computer science to attain more sophisticated quantitative and analytical techniques such as statistical analysis, machine learning, optimization and algorithms. It is for these purposes that I now aspire to pursue a graduate degree in analytics.
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