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【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


University of Southern California,USC,Master of Science in Financial Engineering ,南加州大学,金融工程硕士
  


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斑马博士,斑马博士留学中心,University of Southern California,USC,Master of Science in Financial Engineering ,南加州大学,金融工程硕士

斑马博士,斑马博士留学中心,University of Southern California,USC,Master of Science in Financial Engineering ,南加州大学,金融工程硕士



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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