Departmental majors can earn either the B.A. or the B.S. degree by meeting the general university requirements and the general requirements of the School of Engineering (see Requirements for a Bachelor's Degree, including Writing Requirement, in this catalog), and the departmental requirements.
Honors
The Department of Applied Mathematics and Statistics awards departmental honors based on a number of factors, including performance in coursework and research experience. To be eligible for departmental honors a student must:
- achieve a 3.75 GPA in AMS Department courses (EN.553) used toward major requirements 1-12; and
- earn a C- or better in an additional one semester course in AMS (EN.553) at the 300-level or higher, or undertake significant research activity (equivalent to a 3-credit course) in a subject related to applied mathematics. Such research can be conducted as an official research course, or the student may request that the research supervisor provide an assessment to AMS academic staff toward the middle of the semester of intended degree conferral.
Program Requirements
All courses used to meet the following departmental requirements must be taken for a letter grade and passed with grade of C- or higher:
| Code | Title | Credits |
|---|---|---|
| 1. Calculus I, II, and III | ||
| AS.110.108 | Calculus I (Physical Sciences & Engineering) | 4 |
| AS.110.109 | Calculus II (For Physical Sciences and Engineering) | 4 |
| or AS.110.113 | Honors Single Variable Calculus | |
| AS.110.202 | Calculus III | 4 |
| or AS.110.211 | Honors Multivariable Calculus | |
| 2. Linear Algebra 1 | ||
| AS.110.201 | Linear Algebra | 4 |
| or AS.110.212 | Honors Linear Algebra | |
| or EN.553.291 | Linear Algebra and Differential Equations | |
| 3. Differential Equations 1 | ||
| AS.110.302 | Differential Equations and Applications | 3-4 |
| or EN.553.391 | Dynamical Systems | |
| or EN.553.473 | Introduction to Nonlinear Dynamics and Chaos | |
| 4. Computer Languages and Programming | ||
| Select one of the following: 2, 3 | ||
| Gateway Computing: JAVA | ||
| Gateway Computing: Python | ||
| Gateway Computing: Matlab | ||
| Introduction to Computing | ||
| Introduction to Mathematical Computing | ||
| Biological Models and Simulations and Nonlinear Dynamics of Biological Systems | ||
| Intermediate Programming | ||
| 5. Numerical Linear Algebra | ||
| Numerical Linear Algebra | ||
| 6. Discrete Mathematics | ||
| Select one of the following: | ||
| Discrete Mathematics | ||
| Honors Discrete Mathematics | ||
| Cryptology and Coding | ||
| Combinatorial Analysis | ||
| Graph Theory | ||
| 7. Probability and Statistics | ||
| EN.553.420 | Introduction to Probability | 4 |
| EN.553.430 | Introduction to Statistics | 4 |
| or EN.553.431 | Honors Introduction to Statistics | |
| 8. Optimization | ||
| EN.553.361 | Introduction to Optimization | 4 |
| 9. Area of Focus | ||
| Select two courses from one of the following areas of focus. They must be distinct from those courses used to satisfy requirements 1-8. | ||
| Probability and Stochastic Processes | ||
| Real Analysis I | ||
| Mathematical and Computational Foundations of Data Science | ||
| Introduction to Stochastic Processes | ||
| Stochastic Processes and Applications to Finance | ||
| Monte Carlo Methods | ||
| Mathematical Biology | ||
| Statistics and Statistical Learning | ||
| Mathematical and Computational Foundations of Data Science | ||
| Mathematical Modeling and Consulting | ||
| Applied Statistics and Data Analysis | ||
| Applied Statistics and Data Analysis II | ||
| Introduction to Statistical Learning, Data Analysis and Signal Processing | ||
| Mathematical Modeling: Statistical Learning | ||
| Bayesian Statistics | ||
| Monte Carlo Methods | ||
| Introduction to Data Science | ||
| Time Series Analysis | ||
| Computational Molecular Medicine | ||
| Optimization and Operations Research | ||
| Introduction to Optimization II | ||
| Mathematical Modeling and Consulting | ||
| Mathematical Game Theory | ||
| Network Models in Operations Research | ||
| Introduction to Convexity | ||
| Deep Learning in Discrete Optimization | ||
| Discrete Mathematics | ||
| Introduction to Abstract Algebra | ||
| Cryptology and Coding | ||
| Network Models in Operations Research | ||
| Combinatorial Analysis | ||
| Graph Theory | ||
| Financial Mathematics | ||
| Stochastic Processes and Applications to Finance | ||
| Stochastic Processes and Applications to Finance II | ||
| Equity Markets and Quantitative Trading | ||
| Investment Science | ||
| Introduction to Financial Derivatives | ||
| Interest Rate and Credit Derivatives | ||
| Quantitative Portfolio Theory and Performance Analysis | ||
| Financial Engineering and Structured Products | ||
| Advanced Equity Derivatives | ||
| Computing for Applied Mathematics | ||
| Computational Mathematics | ||
| Numerical Analysis | ||
| and, one of | ||
| Mathematical and Computational Foundations of Data Science | ||
| Monte Carlo Methods | ||
| Deep Learning in Discrete Optimization | ||
| Mathematical Image Analysis | ||
| 10. Scientific Computing | ||
| Select one of the following: | ||
| Mathematical and Computational Foundations of Data Science | ||
| Mathematical Modeling and Consulting | ||
| Applied Statistics and Data Analysis | ||
| Introduction to Statistical Learning, Data Analysis and Signal Processing | ||
| Mathematical Modeling: Statistical Learning | ||
| Bayesian Statistics | ||
| Monte Carlo Methods | ||
| Introduction to Data Science | ||
| Financial Computing in C++ | ||
| Computational Molecular Medicine | ||
| Network Models in Operations Research | ||
| Deep Learning in Discrete Optimization | ||
| Numerical Analysis | ||
| Computing for Applied Mathematics | ||
| Mathematical Image Analysis | ||
| Applied and Computational Linear Algebra | ||
| Intro Algorithms | ||
| Machine Learning | ||
| Machine Learning: Deep Learning | ||
| 11. Natural Sciences | ||
| Courses coded Natural Sciences. Laboratory courses that accompany Natural Science courses may be used in reaching this total. (Courses used to meet the requirements above may be counted toward this total.) | 12 | |
| 12. Quantitative Studies | ||
| Courses coded Quantitative Studies totaling 40 credits of which at least 18 credits must be in courses numbered 300 or higher. (Courses used to meet the requirements above may be counted toward this total.) | 40 | |
| 1 | A student who earns credit in EN.553.291 Linear Algebra and Differential Equations may not earn credit for AS.110.302 Differential Equations and Applications. |
| 2 | or JHU credit for AP Computer Science A. |
| 3 | Students are strongly encouraged to fulfill this element of the requirement by taking EN.500.113 Gateway Computing: Python, and to do this in their first semester at Johns Hopkins University. |
The requirements above together constitute a minimal core program, allowing maximum flexibility in planning degree programs. Students often are able to complete a second major during a four-year program or to proceed to the department’s combined bachelor’s/master’s degree program.
It is highly recommended that students develop a coherent program of study (see below) or at least take additional departmental courses, in order to establish a broad foundation for a career as an applied mathematician. Of particular importance are additional courses in optimization (EN.553.362 Introduction to Optimization II), stochastic processes (EN.553.426 Introduction to Stochastic Processes), statistics (EN.553.413 Applied Statistics and Data Analysis) , dynamical systems (EN.553.391 Dynamical Systems), mathematical modeling and consulting (EN.553.400 Mathematical Modeling and Consulting), scientific computing (EN.553.385 Scientific Computing: Linear Algebra, EN.553.386 Scientific Computing: Differential Equations), and investment science (EN.553.442 Investment Science).
Students planning to continue to graduate school in an applied mathematics program are encouraged to consider taking one or more graduate-level courses in probability (EN.553.720 Probability Theory I, EN.553.721 Probability Theory II), statistics (EN.553.730 Statistical Theory, EN.553.731 Statistical Theory II), optimization (EN.553.761 Nonlinear Optimization I, EN.553.762 Nonlinear Optimization II), combinatorics (EN.553.671 Combinatorial Analysis), graph theory (EN.553.672 Graph Theory), numerical analysis (EN.553.781 Numerical Analysis), or matrix analysis (EN.553.792 Matrix Analysis and Linear Algebra).