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미국의 가족지원정책
한국과 미국의 장애인 가족지원정책 비교(한국,미국,장애인 가족지원정책, 한국장애인, 미국장애인, 외국장애인, 서비스, 장애인가정, 해외장애가정)
미국 유럽 국제화지원
[중소벤처기업창업] 중소벤처기업창업의 개념, 중소벤처기업창업의 절차, 중소벤처기업창업시 인력지원제도와 조세지원제도, 중소벤처기업창업시 규제완화지원제도와 기타지원제도, 미국의 중소벤처기업창업지원 사례 분석
소개글
미국 대학원 지원_SOP (Statement Of Purpose)_Grad Admissions_Cornell_MPS in Applied Statistics_Proofreading Service Verified에 대한 자료입니다.
본문내용
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 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 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 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 purposes that I now aspire to pursue Cornells MPS in Applied Statistics with the data science option.
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 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 users experience, and generate new channels for profits. LinkedInsPeople You May Know feature, which provides users with a list of people it thinks the user is likely to know, is an example of data product I aspire to develop. Data discovery encapsulates thethree 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.
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