强背景+积极配合=Top录取(金融数学/统计学)-新东方前途出国

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    强背景+积极配合=Top录取(金融数学/统计学)

    背景介绍
    申请难点
    留学规划与提升
     首先我们来介绍下这位同学的录取项目:
     
     
    杜克大学  统计科学硕士
    约翰霍普金斯大学 金融数学硕士
    纽约大学  金融数学硕士
    南加州大学 金融工程硕士
    波士顿大学 数学金融硕士
    Duke University Master's in Statistical Science
    Johns Hopkins University M.S.E. in Financial Mathematics
    New York University M.S. in Mathematics in Finance
    University of Southern California M.S. in Financial Engineering
    Boston University M.S. in Mathematical Finance
     
    院校解读
    留学方案
    案例分析
     申请故事
     
    在第一次一对一沟通中,我们发现不同于一般的大学生,这位同学对自己未来的职业规划和学术计划都有非常清晰的认知。这样的认知让她不仅时刻关注提升自己的GPA,保持优异的学术成绩,也促使她不断参与多种多样的、有针对性的科研活动和实习。多方面的准备让她拥有了强大的软硬件条件,为她的申请成功提供了坚实的基础。
     
    鉴于这位同学优异的软硬件条件,我们后续的重点便放在帮助这位同学选取最有亮点的经历以及如何在文书中表现出来。因此我们与这位同学针对她的每段经历都进行了深入沟通以全面了解这位同学在这些经历中具体做的工作、完成的成果、软硬件技能收获,以及体现出的能够吸引招生官的亮点品质和能力。而在此期间,这位同学也能够根据需求及时补充相关的素材,并积极参与需要的额外活动或者科研项目。她的积极配合无疑也是取得申请成功的一大原因。
     
    除此之外,我们也针对各个申请学校的独特面经制定出更有说服力且更符合申请院校招生偏好的回答思路,这让这位同学能够很好地应对面试官的问题并在面试中展现自己。
     
    最终, 在我们的努力和精诚合作下,这位同学最终斩获了多枚美国优秀名校的录取,包括杜克大学、约翰霍普金斯大学、南加州大学等金工金数和统计项目。
     
    文书高光段落
     
     
    During my junior year, I have the opportunity to participate in the advanced business analytics and case study project headed by professor of Carnegie Mellon University. During this period, I, together with other resourceful research assistants, was cooperating to apply quantitative techniques to assess the market value of all available neighborhoods in the Pittsburgh area and select the most appropriate house for the designated middle-class family. To start our research, I initiated to collect the detailed information of the 83 neighborhoods in the Pittsburgh area, including their names and resident population from 2010 to 2019. Believing that safety must be the top priority of residents, I then conducted comprehensive research on the past ten years’ number of offense counts of the available neighborhoods. This index then helped us eliminated half of the available neighborhoods. Afterward, I utilized RapidMiner to run in-depth cluster analyses on all collected ratings of Good for Families and Health & Fitness of all remaining neighborhoods to select the top seven neighborhoods which had the most suitable environment for family life and well-established facilities that best improved residents’ health and family harmony. The distributions of necessary amenities and the distance to nearby shopping malls, schools, and business centers were also our main research areas. But the most important factor we considered at this stage was the house price. With a limited budget, a middle-class family needed to buy a house with high cost performance. To satisfy this need, we then applied a house valuation model to estimate the market value of all available seven neighborhoods based on all collected data above mentioned. Besides, I also suggested to visualize the house price fluctuation trend, which then help us screen out an undervalued house with moderate price and highest cost performance. 
     
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