Applied mathematics and statistics

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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  


    Introduction to Stochastic Processes  

    or EN.550.427

    Stochastic Processes and Applications to Finance


    Stochastic Processes and Applications to Finance II  


    Monte Carlo Methods  


    Probability Theory I  


    Probability Theory II  


    Introduction to Stochastic Calculus  


    Stochastic Search & Optimization  


    Modeling, Simulation, and Monte Carlo  
    Statistics and Statistical Learning  


    Applied Statistics and Data Analysis  


    Practical Scientific Analysis of Big Data  


    Nonparametric Statistics  


    Data Mining  


    Time Series Analysis  


    Computational Molecular Medicine  


    Statistical Theory  


    Statistical Theory II  


    Bayesian Statistics  


    Advanced Topics in Bayesian Statistics  
    Optimization and Operations Research  


    Optimization in Finance  


    Mathematical Game Theory  


    Network Models in Operations Research  


    Foundations of Optimization  


    Optimization Algorithms  


    Stochastic Search & Optimization  


    Convex Optimization  


    Combinatorial Optimization  
    Computational and Applied Mathematics  


    Financial Computing in C++  


    Mathematical Biology  


    Mathematical Image Analysis  


    Shape and Differential Geometry  


    Numerical Analysis  


    Matrix Analysis and Linear Algebra  


    Advanced Parameterization in Science and Engineering  
    Discrete Mathematics *  


    Combinatorial Analysis  


    Graph Theory  


    Combinatorial Optimization  


    Combinatorial Analysis  


    Graph Theory  


    Algorithms I  


    Randomized and Big Data Algorithms  


    Approximation Algorithms  


    Combinatorics & Graph Theory in Computer Science  


    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,
  • 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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