Johns Hopkins University
Master of Science in Applied and Computational Mathematics
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Coursework:
- Calculus I.
- Calculus II.
- Multivariable Calculus and Complex Analysis.
- Linear Algebra and Its Applications.
- Introduction to Programming using Python.
- Data Structures.
- Algorithms for Data Science.
- Statistical Methods and Data Analysis.
- Statistical Models and Regression.
- Matrix Theory.
- Monte Carlo Methods.
- Principles and Methods in Machine Learning.
- Introductory Stochastic Differential Equations with Applications (Summer ’26).
- Financial Engineering and Structured Products (Summer ’26).
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Cumulative GPA:
- 3.950 / 4.000.