Master of Science in Applied Mathematics
The Master of Science in Applied Mathematics program at Illinois Tech is a modern graduate program tailored to serve students based on their academic background and future career goals. For students who wish to pursue a doctoral degree in the mathematical sciences, it provides a strong academic foundation that prepares the student for the challenge of Ph.D. coursework and research. For students who wish to pursue careers in industry, Illinois Tech trains students in stateoftheart advanced mathematical techniques and models that are appealing to future employers. These options are possible due to the remarkably flexible structure of the program that allows students to craft their own coursework to meet their career goals by choosing one of the three options of study:
 Coursework only option
 Completing an industrybased project
 Writing an M.S. thesis
In addition, students can choose a specialization from a wide range of contemporary areas of applied mathematics:
 Computational Statistics for Data Science
 Discrete Computation and Optimization
 Industrial Mathematics
 Quantitative Risk Management
 Stochastic Computation
Students satisfying the requirements of a specialization will have the specialization recognized on official transcripts.
Admission Requirements
The program normally requires a bachelor’s degree in mathematics or applied mathematics. Candidates whose degree is in another field (for example, computer science, physics, or engineering) and whose background in mathematics is strong are also eligible for admission and are encouraged to apply. Applicants should have a bachelor’s degree from an accredited university with a minimum cumulative GPA of 3.0/4.0. A combined verbal and quantitative GRE examination score of at least 304 and an analytic writing score of at least 2.5 are required. TOEFL scores (if required) should be a minimum of 80/550 (internetbased/paperbased test scores). A professional statement of goals/objectives (two pages) and a curriculum vitae must be submitted. Two letters of recommendation are required. Students must remove deficiencies in essential undergraduate courses that are prerequisites for the degree program, in addition to fulfilling all other degree requirements. Typically, admitted students score at least 156 on the quantitative portion of the GRE; however, meeting the minimum or typical GPA and test score requirements does not guarantee admission.
The Director of Graduate Studies serves as temporary academic adviser for newly admitted graduate students in the master of science programs until an appropriate faculty member is selected as the adviser. Students are responsible for following all departmental procedures, as well as the general requirements of the Graduate College.
Curriculum
Students may transfer up to two classes from a graduate program at another accredited university if the student has not used the classes to satisfy the requirements for a degree at the previous university.
General Program Requirements
 All students will follow the requirements for core courses as given below.

All students will choose one of the following three options:

Coursework Only Option. Students must pass the comprehensive exam, consisting of two exams corresponding to the courses MATH 500, MATH 540, MATH 553, MATH 563, and MATH 577, which must be passed at a master's level or above.

Master's Project Option. Perform an industrial project for three to five credit hours taken as MATH 594. A project may focus on the applications of existing methodologies or mathematical modeling of a reallife phenomenon, possibly from outside mathematics, including industry sponsored group projects. This option also requires MATH 522 and the completion of a formal specialization.

M.S. Thesis Option. M.S. Thesis for five to eight credit hours taken as MATH 591. A thesis should go into substantial depth on a topic or problem from a methodological or mathematical perspective and make a contribution towards the advancement of mathematical understanding of the problem under study.


All students will take the colloquium course MATH 593 (zero credit hours) at least one semester.

All students will take their remaining credit hours from the elective courses listed below or other courses with the approval of the academic adviser.

Students will maintain a GPA of at least 3.0 in their coursework.

Students in the coursework only option or thesis option may complete one of the listed specializations, but are not required to do so.
Master of Science in Applied Mathematics (Coursework Only Option)
Minimum Degree Credits  32 
Maximum 400Level Credit  9 
Minimum MATH Credit  25 
Code  Title  Credit Hours 

Core Courses  (9) ^{1}  
MATH 577  Computational Mathematics I  3 
Select a minimum of six credit hours from the following:  6  
Applied Analysis I  3  
or MATH 400  Real Analysis  
Probability ^{2}  3  
or MATH 475  Probability  
Discrete Applied Mathematics I  3  
or MATH 454  Graph Theory and Applications  
Mathematical Statistics ^{3}  3  
or MATH 476  Statistics  
Additional Requirements  (0)  
MATH 593  Seminar in Applied Mathematics  0 
Elective Courses  (23)  
Select 23 credit hours ^{4}  23  
Total Credit Hours  32 
^{1}  Students in the coursework only option must pass the comprehensive exam, consisting of two exams corresponding to the courses MATH 500, MATH 540, MATH 553, MATH 563, and MATH 577, which must be passed at a master's level or above. 
^{2}  MATH 540 or MATH 475 is required for students pursuing specializations in Stochastic Computation, Computational Statistics for Data Science, or Quantitative Risk Management. 
^{3}  MATH 563 is required for students pursuing a specialization in Computational Statistics for Data Science. 
^{4}  The remaining courses in each student’s program are selected in consultation with, and approval of, the Director of Graduate Studies. Students pursuing a specialization should choose approved courses specific to their specialization. See the Specializations tab on this page for more details. 
Master of Science in Applied Mathematics (Master's Project Option)
Minimum Degree Credits  32 
Maximum 400Level Credit  9 
Minimum MATH Credit  25 
Code  Title  Credit Hours 

Core Courses  (9)  
MATH 522  Mathematical Modeling  3 
MATH 577  Computational Mathematics I  3 
Select a minimum of three credit hours from the following:  3  
Applied Analysis I  3  
or MATH 400  Real Analysis  
Probability ^{1}  3  
or MATH 475  Probability  
Discrete Applied Mathematics I  3  
or MATH 454  Graph Theory and Applications  
Mathematical Statistics ^{2}  3  
or MATH 476  Statistics  
Additional Requirements  (0)  
MATH 593  Seminar in Applied Mathematics  0 
Specialization Courses  (915)  
Select 915 credit hours from an approved specialization ^{3}  915  
Master's Project  (35)  
MATH 594  Professional Master's Project  35 
Elective Courses  (311)  
Select 311 credit hours ^{4}  311 
^{1}  MATH 540 or MATH 475 is required for students pursuing specializations in Industrial Mathematics, Stochastic Computation, Computational Statistics for Data Science, or Quantitative Risk Management. 
^{2}  MATH 563 is required for students pursuing a specialization in Computational Statistics for Data Science. 
^{3}  Students should choose approved courses specific to their specialization. See the Specializations tab on this page for more details. 
^{4}  The remaining courses in each student’s program are selected in consultation with, and approval of, the Director of Graduate Studies. 
Master of Science in Applied Mathematics (Thesis Option)
Minimum Degree Credits  32 
Maximum 400Level Credit  9 
Minimum MATH Credit  25 
Code  Title  Credit Hours 

Core Courses  (9)  
MATH 577  Computational Mathematics I  3 
Select six credit hours from the following:  6  
Applied Analysis I  3  
or MATH 400  Real Analysis  
Probability ^{1}  3  
or MATH 475  Probability  
Discrete Applied Mathematics I  3  
or MATH 454  Graph Theory and Applications  
Mathematical Statistics ^{2}  3  
or MATH 476  Statistics  
Additional Requirements  (0)  
MATH 593  Seminar in Applied Mathematics  0 
Elective Courses  (1518)  
Select 1518 credit hours ^{3}  1518  
Thesis Research  (58)  
MATH 591  Research and Thesis M.S.  58 
^{1}  MATH 540 or MATH 475 is required for students pursuing specializations in Stochastic Computation, Computational Statistics for Data Science, or Quantitative Risk Management. 
^{2}  MATH 563 is required for students pursuing a specialization in Computational Statistics for Data Science. 
^{3}  The remaining courses in each student’s program are selected in consultation with, and approval of, the Director of Graduate Studies. Students pursuing a specialization should choose approved courses specific to their specialization. See the Specializations tab on this page for more details. 
Comprehensive Examination
The comprehensive examination requirement is fulfilled by either (a) passing written tests in two of the five core areas of study at the master of science level; or (b) performing an industrial project (three to five credit hours of MATH 594 ), satisfying the requirements for one specialization, and taking MATH 522; or (c) a master's thesis (five to eight credit hours of MATH 591) under the supervision of a faculty member.
Specializations
Computational Statistics for Data Science
Code  Title  Credit Hours 

Required Courses  (9)  
MATH 540  Probability ^{1}  3 
or MATH 475  Probability  
MATH 563  Mathematical Statistics ^{1}  3 
MATH 564  Applied Statistics  3 
Elective Courses  (0) ^{2}  
Bioinformatics  3  
Online Social Network Analysis  3  
Probabilistic Graphical Models  3  
Machine Learning  3  
Natural Language Processing  3  
Machine and Deep Learning  3  
Design and Analysis of Exprmnt  3  
Optimization I  3  
Stochastic Processes  3  
or MATH 481  Intro to Stochastic Processes  
Introduction to Time Series  3  
or MATH 446  Introduction to Time Series  
Algebraic & Geometric Methods  3  
Monte Carlo Methods in Fin  3  
Adv Design of Experiments  3  
or MATH 483  Design and Analysis of Exprmnt  
Statistical Learning  3  
Bayesian Computational Stats  3  
Computational Mathematics II  3  
Meshfree Methods  3  
Computational Physics  3 
^{1}  MATH 540, MATH 475, and MATH 563 may be used to satisfy both the core degree requirements and specialization requirements. 
^{2}  Students may also select core course options that were not used to satisfy the core course requirement. 
Discrete Computation and Optimization
Code  Title  Credit Hours 

Required Courses  (9)  
Select nine credit hours from the following:  9  
Applied/Computational Algebra  3  
Optimization I  3  
Discrete Applied Mathematics I  3  
Discrete Applied Math II  3  
Statistical Learning  3  
Elective Courses  (0) ^{2}  
Dsgn and Anlys of Algorithms  3  
Game Theory: Algorithms & Apps  3  
Online Social Network Analysis  3  
Probabilistic Graphical Models  3  
Machine Learning  3  
Coding Reliable Communications  3  
Compt Vision Image Processing  3  
Applied Algebra  3  
Graph Theory and Applications ^{1}  3  
Stochastic Processes  3  
or MATH 481  Intro to Stochastic Processes  
Introduction to Time Series  3  
or MATH 446  Introduction to Time Series  
Algebraic & Geometric Methods  3  
Mathematical Statistics  3  
or MATH 564  Applied Statistics  
Monte Carlo Methods in Fin  3  
Adv Design of Experiments  3  
or MATH 483  Design and Analysis of Exprmnt  
Bayesian Computational Stats  3 
^{1}  MATH 454 may not be taken if the student has already completed MATH 553. 
^{2}  Students may also select core course options that were not used to satisfy the core course requirement. 
Industrial Mathematics
Note: The master's project track is required to pursue this specialization.
Code  Title  Credit Hours 

Required Courses  (15)  
MATH 540  Probability ^{1}  3 
or MATH 475  Probability  
MATH 522  Mathematical Modeling ^{1}  3 
SCI 511  Project Management  3 
or SCI 522  Public Engagement Scientists  
MATH 523  Case Studies & Project Design  6 
or MATH 592  Internship in Applied Math  
Elective Courses  (0) ^{2}  
Dsgn and Anlys of Algorithms  3  
Game Theory: Algorithms & Apps  3  
Online Social Network Analysis  3  
Probabilistic Graphical Models  3  
Machine Learning  3  
Applied Algebra  3  
Graph Theory and Applications ^{2}  3  
Stochastic Processes  3  
or MATH 481  Intro to Stochastic Processes  
Introduction to Time Series  3  
or MATH 446  Introduction to Time Series  
Algebraic & Geometric Methods  3  
Mathematical Statistics  3  
or MATH 564  Applied Statistics  
Monte Carlo Methods in Fin  3  
Adv Design of Experiments  3  
or MATH 483  Design and Analysis of Exprmnt  
Bayesian Computational Stats  3 
^{1}  MATH 540, MATH 475, and MATH 522 may be used to satisfy both the core degree requirements and specialization requirements. 
^{2}  Students may also select core course options that were not used to satisfy the core course requirement. 
Quantitative Risk Management
Code  Title  Credit Hours 

Required Courses  (12)  
MATH 540  Probability ^{1}  3 
or MATH 475  Probability  
MATH 542  Stochastic Processes  3 
or MATH 543  Stochastic Analysis  
MATH 588  Advanced Quant Risk Mgmt  3 
MATH 565  Monte Carlo Methods in Fin  3 
or MATH 582  Mathematical Finance II  
or MATH 587  Thry/Prac Modlng&Crdt Deritvs  
Elective Courses  (0) ^{2}  
Stochastic Analysis  3  
Stochastic Dynamics  3  
or MATH 545  Stochastic Partial Diff Equatn  
Introduction to Time Series  3  
or MATH 566  Multivariate Analysis  
Mathematical Statistics  3  
or MATH 564  Applied Statistics  
Statistical Learning  3  
Bayesian Computational Stats  3  
Computational Mathematics II  3  
Finite Element Method  3  
or MATH 589  Num Meth for Partial Diff Equa  
or MATH 590  Meshfree Methods  
Ther&Prac Fixed Income Modelng  3 
^{1}  MATH 540 or MATH 475 may be used to satisfy both the core degree requirements and specialization requirements. 
^{2}  Students may also select core course options that were not used to satisfy the core course requirement. 
Stochastic Computation
Code  Title  Credit Hours 

Required Courses  (12)  
MATH 540  Probability ^{1}  3 
or MATH 475  Probability  
Select nine credit hours from the following:  9  
Stochastic Processes  3  
or MATH 543  Stochastic Analysis  
Stochastic Dynamics  3  
Stochastic Partial Diff Equatn  3  
Monte Carlo Methods in Fin  3  
Bayesian Computational Stats  3  
Elective Courses  (0) ^{2}  
Topics in Computer Science (Advanced Scientific Computing)  3  
Mathematical Modeling  3  
Applied/Computational Algebra  3  
Introduction to Time Series  3  
Statistical Learning  3  
Reliable Mathematical Software  0  
Computational Mathematics II  3  
Num Meth for Partial Diff Equa  3 
^{1}  MATH 540 or MATH 475 may be used to satisfy both the core degree requirements and specialization requirements. 
^{2}  Students may also select core course options that were not used to satisfy the core course requirement. 