소개글

워싱턴 대학원 지원 SOP(Data Science)에 대해 기술한 참고자료입니다.

저작시기 : 2021년 1월

본문내용

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 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. Once I realized that data science 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 academic goals that I now aspire to pursue a graduate degree in data science.
What intrigues me most about
this field is the concept of data discovery, namely, discovering insights and developing innovative data based products, services, or features by using the information extracted from big data. These skills enable data scientists to not only support businesses by assisting with management-level decisions that help the business run efficiently, but also by offering new products or features that enhance the user's experience, and generate new channels for profits. The optimized energy use, production process improvements, and LinkedIn's People You May Know, which provides a user with a list of people it thinks the user is likely to know, are the examples of data products or services I aspire to develop. I find the concept of data discovery especially fascinating because I realized during my work that there is no position at eTEC that performs both a supporting role and is directly involved with developing products; instead, each of these tasks is strictly divided between engineers who designed structures and personnel like myself who supported their endeavors by implementing cost control measures and streamlining material procurement. As a data scientist, I could take on both roles, allowing me to make greater contributions to the business by not only reducing costs, but also creating new lines of revenue through product development, which is the type of work that I hope to focus on during my career. Therefore, while pursuing my degree in data science, I would like to concentrate on learning and researching methods to incorporate statistical, mathematical, and computer-based skills that would optimize the decision-making process and business operation efficiency, and lead to creative ideas to develop viable data based products.
The University of Washington'
s data science program emphasizes experiential learning through its practicum project per quarter and capstone project during the final quarter. Such hands-on experience will enable me to directly apply the skills and knowledge I gain from my studies in order to better understand the various approaches different organizations take toward data science, what they hope to gain from them, and how they expect them to be applied. Furthermore, the program's emphasis on team-based data analysis and engineering work will help me prepare for a career developing innovative products by utilizing data science and making improvements throughout various business units of a company such as production and supply chain management.
In addition, given that UW's
curriculum integrates computational disciplines with technical skills and foundational knowledge in data science, I will be able to achieve my academic goal of overcoming the analytical limitations. In particular, I look forward to being exposed to leading experts through research opportunities and seminars at the eScience Institute in order to learn about their latest findings and different perspectives on data science. With UW's data science program, I am confident that I will be able to fulfill my academic and career goals.
   (이하 생략)

참고문헌

없음