The Department of Mathematics of Columbia University has a master's degree in mathematics and finance, which was jointly founded by the Department of Mathematics and the Department of Statistics of Columbia University. The new project combines several advantages such as mathematics, statistical analysis, stochastic process, numerical calculation method and financial application software. This project deeply attracts students with academic background of quantitative analysis such as mathematics, statistics, physics, social economics, computer science or water conservancy. Most people have financial work experience or internship experience.
There are many jobs suitable for students:
The career development of graduates majoring in mathematics in the financial industry of Columbia University is still very good. Graduates work in Goldman Sachs, Morgan Stanley, Citigroup, JPMorgan Chase, Bank of America Merrill Lynch, UBS, Credit Suisse, Barclays Capital, Deloitte Consulting, Ernst & Young, Societe Generale, Credit Agricole, various financial derivatives and investment management companies, and most other companies have recruited graduates from this project.
The course design is very flexible:
The financial mathematics and technology major of Columbia University strives to strike a balance between rigorous basic theory courses and advanced application courses, which are taught by financial professionals and experts in most cases. In addition to the courses in finance, mathematics and statistical analysis provided by this project, students can also choose courses from universities all over the world. As an elective course, the total number of financial academic year courses offered by the elective course plan has increased from 3 courses per year to 12 courses per year at the present stage. The new course is GR5430 Machine Learning Algorithm for Mathematics in Financial Industry, which was offered in the fall semester.
How about a master's degree in mathematical statistics from Georgetown University? I. Introduction of the Project
The graduate students of mathematics and statistics at Georgetown University belong to the Department of Mathematics and Statistics. The new project can lay a foundation for my career development in industries that use mathematics and statistical technology. Emphasize the close combination of mathematical analysis and statistical analysis or probability. Some key and typical fields are stochastic process, financial mathematics, measurement science, microbial statistics and big data mining. This program is suitable for students who have obtained a degree in mathematics or statistics, and also for applicants who have an undergraduate degree equivalent to a minor in mathematics.
The new project of Mathematics and Statistics in Georgetown University implements the academic year system, which is spring semester and autumn semester respectively, and the time is less than one and a half years. This course provides students with skills and professional knowledge related to career development in the application of mathematics, statistical analysis science and so on.
In addition, industries that combine mathematical analysis with statistical analysis or probability will be emphasized, such as stochastic process, financial mathematics, measurement science, microbial statistics and data analysis. The courses studied include 4 compulsory courses and 5 elective courses in mathematics and statistics, and it is expected to study 1 non-mathematics elective course.
Second, the application requirements
Curriculum environment: It is suggested to have a good environment, including calculus, discrete mathematics and so on. Language expression requirements: IELTS 7.5+/ TOEFL 100+GRE/GMAT: GRE/GMAT scores are required, and GMAT7 10+/GRE320+ is recommended.
Third, the curriculum.
In order for students to complete the 30-credit course system, they must study 4 compulsory courses and 5 elective courses in mathematics and statistics, and they must choose a non-mathematical elective course. Elective courses focus on popular machine learning, data analysis and many other aspects. And a non-mathematical elective course provides them with the probability of surpassing data information, such as biostatistics, social policy and so on.