Applied mathematics and statistics

Applied mathematics and statistics is an integral part of emerging fields such as computational medicine/biology, language processing, information security, and computer science. In today’s data-intensive world, it is used to answer questions and solve problems in areas as diverse as finance, government and law, medicine, and national defense.

Beginning freshmen year at Johns Hopkins, students in applied mathematics and statistics work alongside faculty members who are world-class leaders in their fields, participating in research that typically takes place only in Ph.D. programs. Our undergraduate program is flexible enough to provide opportunities to integrate your mathematical reasoning and modeling skills with other academic and personal pursuits, including adding a second major.

Today’s employers need experts with a solid grasp of the sophisticated analytical tools to crunch big data and make solid decisions, and that’s what Johns Hopkins offers. Our AMS graduates are equipped with the analytical skills and expertise in machine learning and data-mining that prepare them to be leaders in the global economy.

Students may work toward either the master of arts (M.A.) degree or the master of science in engineering (M.S.E.) degree in applied mathematics and statistics. All master’s degrees in applied mathematics and statistics ordinarily require a minimum of two semesters of registration as a full-time resident graduate student.

To obtain departmental certification for the master’s degree in Applied Mathematics and Statistics, the student must:

1. Complete satisfactorily at least eight one-semester courses of graduate work in a coherent program approved by the faculty advisor. All 600-level and 700-level courses (with the exception of seminar and research courses), and some 400-level courses in the department are satisfactory for this requirement.  Certain courses in other departments are also acceptable.  At most 3 courses outside the department may be counted toward the Master's degree requirements.
2. Meet either of the following options:
   • (a) submit an acceptable research report based on an approved project; or
   • (b) complete satisfactorily two additional one-semester graduate courses, as approved by the faculty advisor.
3. Satisfy the computing requirement by receiving a grade of B- or better in one of the following courses:

   EN.550.400	Mathematical Modeling and Consulting	4.00
   EN.550.413	Applied Statistics and Data Analysis	4.00
   EN.550.415	Practical Scientific Analysis of Big Data	3.00
   EN.550.433	Monte Carlo Methods	3.00
   EN.550.436	Data Mining	4.00
   EN.550.443	Financial Computing in C++	4.00
   EN.550.450	Computational Molecular Medicine	4.00
   EN.550.487	Numerical Methods for Financial Mathematics	3.00
   EN.550.493	Mathematical Image Analysis	3.00
   EN.550.632	Bayesian Statistics	3.00
   EN.550.643	Graphical Models	4.00
   EN.550.653	Commodities and Commodity Markets	3.00
   EN.550.661	Foundations of Optimization	3.00
   EN.550.662	Optimization Algorithms	3.00
   EN.550.680	Shape and Differential Geometry	3.00
   EN.550.681	Numerical Analysis	4.00
   EN.600.475	Machine Learning	3.00

4. Complete an area of focus by taking three courses in one of the following areas. A list of courses that can be counted toward each area of focus will be maintained and updated every year.  Some courses from other departments can be eligible to count toward area of focus.  They can be used within the three-course limit specified in point 1, above. This list of courses is based on recent offerings. Not all classes are available every year and substitute classes may be accepted if approved by the advisor and the Academic Affairs Committee.  Higher level classes (700 or 800) can be accepted if given a letter grade.

   Probability Theory
   EN.550.426	Introduction to Stochastic Processes
   or EN.550.427	Stochastic Processes and Applications to Finance
   EN.550.428	Stochastic Processes and Applications to Finance II
   EN.550.433	Monte Carlo Methods
   EN.550.620	Probability Theory I
   EN.550.621	Probability Theory II
   EN.550.622	Introduction to Stochastic Calculus
   EN.550.663	Stochastic Search & Optimization
   EN.550.664	Modeling, Simulation, and Monte Carlo
   Statistics and Statistical Learning
   EN.550.413	Applied Statistics and Data Analysis
   EN.550.415	Practical Scientific Analysis of Big Data
   EN.550.434	Nonparametric Statistics
   EN.550.436	Data Mining
   EN.550.439	Time Series Analysis
   EN.550.450	Computational Molecular Medicine
   EN.550.630	Statistical Theory
   EN.550.631	Statistical Theory II
   EN.550.632	Bayesian Statistics
   EN.550.633	Advanced Topics in Bayesian Statistics
   Optimization and Operations Research
   EN.550.461	Optimization in Finance
   EN.550.453	Mathematical Game Theory
   EN.550.463	Network Models in Operations Research
   EN.550.661	Foundations of Optimization
   EN.550.662	Optimization Algorithms
   EN.550.663	Stochastic Search & Optimization
   EN.550.665	Convex Optimization
   EN.550.666	Combinatorial Optimization
   Computational and Applied Mathematics
   EN.550.443	Financial Computing in C++
   EN.550.492	Mathematical Biology
   EN.550.493	Mathematical Image Analysis
   EN.550.680	Shape and Differential Geometry
   EN.550.681	Numerical Analysis
   EN.550.692	Matrix Analysis and Linear Algebra
   EN.550.695	Advanced Parameterization in Science and Engineering
   Discrete Mathematics *
   EN.550.471	Combinatorial Analysis
   EN.550.472	Graph Theory
   EN.550.666	Combinatorial Optimization
   EN.550.671	Combinatorial Analysis
   EN.550.672	Graph Theory
   EN.600.463	Algorithms I
   EN.600.464	Randomized and Big Data Algorithms
   EN.600.469	Approximation Algorithms
   EN.600.470	Combinatorics & Graph Theory in Computer Science
   EN.600.471	Theory of Computation

   *

   The Discrete Mathematics area of focus requires a minimum of one listed course taken within the Applied Mathematics and Statistics department, but the other two courses may include the listed Computer Science offerings.  These courses can be used within the three-course limit specified in point 1, above.

5. Complete training on the responsible and ethical conduct of research, if applicable.  Please see WSE Policy on the Responsible Conduct of Research.
6. Complete training on academic ethics.

An overall GPA of 3.0 must be maintained in courses used to meet the program requirements.  At most two course grades of C or C+ are allowed to be used and the rest of the course grades must be B- or better.

Each candidate for the master’s degree must submit to the department for approval a written program stating how they plan to meet their degree requirements.  This should be done early in the first semester of residence.

Doctoral students in other departments may concurrently undertake  a master’s program in Applied Mathematics and Statistics with the permission of the AMS department and an application review. Application forms and information are available in the department office.

• Letters of Recommendation (3),
• GRE,
• TOEFL/IELTS,
• Statement of Purpose addressing your interest in graduate study in applied mathematics at JHU or another topic of your choice,
• Transcripts,
• GRE Subject (optional)

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• Flexible study schedule
• Experienced teachers
• Certificate upon completion

📘 Recommended for students with an IELTS level of 6.0 or below.

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