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【这些被世界顶尖名校录取者,有着更强的背景,更强甄别好坏的能力,他们在做了充分的对比之后,选择了斑马博士 -- 一家不盲目追求签约数量、拒绝流水线|模板化,强调提供精细化的精英留学理念与服务的“有情怀的、小众”机构!这些最优秀的学生的选择,值得你们参考!】
【本案例脉络】
又一个通过经历包装背景提升跟文书定制而拿到usc 金融工程offer的案例!三维跟他一样的朋友们一手拒信!真的不要相信五百块一份的ps能给你增加什么竞争力,那是翻译!有些人就是要等劣质文书拿到拒信之后,再花钱找人去argue!几十万学费一点不心疼,能帮助你去更好学校的文书钱反倒舍不得了 斑马博士懂得如何设计能最大化申请优势的经历组合从而增加综合竞争力!这是模仿不来也不是那些低价文书店,或者中介模板文书能比的!这甚至跟写作态度没太大关系,因为很多态度超级端正的顾问压根不懂怎么创作文书!
【OFFER展示】 University of Southern California(USC)Master of Science in Financial Engineering 南加州大学金融工程硕士
【客户评价】客户的认可就是对小斑马最大的鼓励!
【学校介绍】USC,全称:University of Southern California,南加州大学,US NEWS排名#21
【专业解读】The objective of this program is the training of graduate students with engineering, applied mathematics or physics backgrounds in the application of mathematical and engineering tools to finance. Financial engineering is a multidisciplinary education program that involves the Viterbi School of Engineering, the USC Marshall School of Business and the USC Dornsife College of Letters, Arts and Sciences (Department of Economics). Financial engineering uses tools from finance and economics, engineering, applied mathematics and statistics to address problems such as derivative securities valuation, strategic planning and dynamic investment strategies, and risk management, which are of interest to investment and commercial banks, trading companies, hedge funds, insurance companies, corporate risk managers and regulatory agencies.(摘录自官网)
【核心课程】
Corporate Finance
Management of Financial Risk
Financial Engineering
Probability for Electrical and Computer Engineers
Stochastic Processes
Mathematics and Tools for Financial Engineering
Directed Research
【选修课程】
Microeconomic Analysis and Policy
Macroeconomic Analysis and Policy
Economic and Financial Time Series I
Financial Analysis and Valuation
Applied Finance in Fixed Income Securities
Hedge Funds
Forecasting and Risk Analysis
Trading and Exchanges
Investment Analysis and Portfolio Management
Mortgages and Mortgage-Backed Securities and Markets
Financial Accounting Analysis for Engineering
Introduction to Programming Systems Design
Analysis of Algorithms
Neural Learning and Computational Intelligence
Statistics and Data Analysis for Engineers
Design of Experiments
Stochastic Systems and Reinforcement Learning
Optimization for the Information and Data Sciences
Machine Learning from Signals: Foundations and Methods
Linear Programming and Extensions
Stochastic Elements of Simulation
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