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오클라호마 대학원 지원 SOP에 대해 기술한 참고자료입니다.

저작시기 : 2021년 1월

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During my time as a procuremen
t associate in eTEC E&C, a South Korean EPC company that constructs civil buildings and industrial facilities, Ianalyzed data to attain information that greatly enhanced the efficiency of my work. For example, by scrutinizing information gathered from completed construction projects and their purchased equipment and materials, I found significant recurring patterns in terms of technical requirements and cost ratios. These patterns remained quite consistent when I applied them to ongoing and subsequent projects, making them invaluable references for planning budgets and estimating costs in the early stage of new projects.
However, I was facing analyti
cal challenges. To list a few, I was almost completely dependent on the company's 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 organize. 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 learned about the application of data collection and analytics to the turbine. The sensors were installed to collect real-time data, which enabled the data scientist to remotely monitor the equipment's performance from a centralized data center and improve its efficiency by applying analytical solution to optimize the turbine's system. Based on this encounter with the company's smart features, I began exploring the potency of analytics and data science. 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. In order to make more effective quantitative data-based decisions, I need more advanced skills. I realized that analytics is the key to overcoming my current analytical limitations and 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, and optimization. It is for these academic goals that I now aspire to pursue a graduate degree in analytics.
What intrigues me most about
this field is the concept of data discovery, namely, discovering insights and applying them to products, services, or features to enhance their quality by using the information extracted from data. In particular, I find the concept of data discovery crucial in my future career because it became an essential element in the field of industrial machinery and plant construction. Through data discovery, the industry is finding ways to reduce operational costs. For example, with advanced analytical methods, unscheduled maintenance that entails costs for dispatching engineers can be avoided. My career goal is to become a data scientist with domain expertise in industrial construction and machinery, focusing on effective procurement, cost minimization, and efficiency optimization, and I believe the study of data science and analytics is a must in order to thrive in the field.
Data discovery encapsulates t
hethree main themes I focused on during my undergraduate studies in economics: efficiency optimization, changing economic policies, and the pursuit of technological progress to spur economic growth. I first learned how to optimize economic efficiency by solving for the equilibrium between supply and demand of a market, then finding the highest output from using resources, such as labor and materials, for manufacturing. Next, I examined the effectiveness of financial and other governmental policies and investigated the best options through which to stimulate economic growth under the given circumstances. Third, I studied the Solow model, which stresses the need for technological progress in order to sustain economic growth.
In studying these phenomena,
I realized that the main themes can be applied to businesses as well. Optimizing efficiency of a business's operations enables significant gains for the business in the long run. Making the most informed organizational decisions regarding policies and strategies of a business can enhance its economic prosperity, and the technological advancement (such as application of new analytical methods) that enables corporations to enhance productivity and helps them run businesses more efficiently is the essential factor to sustain the prosperity. Each of these themes heightened my interests in improving business operations by optimizing their processes and use of resources, and performing data-driven analyses to make better decisions. In the process, I hope to accelerate technological development by creating products with the data discovery aspect of data science to generate tactics with which to perform individual and organizational tasks more efficiently. Ultimately, my undergraduate study that focused on the optimization of efficiency and making decisions to modify policies under analytical observations will serve as a springboard for further exploration of my chosen field. I believe the knowledge and experience I gained will be highly beneficial when using analytics to enhance the efficiency of energy use or to optimize industrial equipment's performance, and help in seeking more efficient ways to operate a business that would require changes in company policies.
The Data Science and Analytic
s program in the Gallogly College of Engineering offers the optimal roadmap for me because its curriculum comprises of coursework that is essential to fulfill my educational goal. The "Data Mining," "Database Management Systems," and "Computing Structures" courses will be crucial in overcoming my analytical limitations, such as the trouble I had with gathering and structuring continuously flowing data. In addition, I must study "Optimization Analytics," "Multi-Criteria Optimization," and "Time Series Analysis" to understand how to apply analytical solutions to solve optimization problems. Moreover, the "Engineering Practicum" will allow me to better understand the various approaches the leading companies take toward analytics and data science, what they hope to gain from them and how they expect them to be applied. In the end, I hope to be able to connect with companies that find my career goal in line with their vision for analytics. I believe the program's strong curriculum that incorporates computational disciplines with technical skills and foundational knowledge in analytics will enable me to prepare for my career transition.
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