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 state-of-the-art 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:

  1. Coursework only option
  2. Completing an industry-based project  
  3. 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 (internet-based/paper-based 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

  1. All students will follow the requirements for core courses as given below.
  2. All students will choose one of the following three options:

    1. 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.

    2. 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 real-life phenomenon, possibly from outside mathematics, including industry sponsored group projects. This option also requires MATH 522 and the completion of a formal specialization.

    3. 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.

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

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

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

  6. 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 400-Level Credit 9
Minimum MATH Credit 25
Core Courses (9) 1
MATH 577Computational Mathematics I3
Select a minimum of six credit hours from the following:6
Applied Analysis I3
Real Analysis
Probability 23
Probability
Discrete Applied Mathematics I3
Graph Theory and Applications
Mathematical Statistics 33
Statistics
Additional Requirements (0)
MATH 593Seminar in Applied Mathematics0
Elective Courses (23)
Select 23 credit hours 423
Total Credit Hours32

Master of Science in Applied Mathematics (Master's Project Option)

Minimum Degree Credits 32
Maximum 400-Level Credit 9
Minimum MATH Credit 25
Core Courses (9)
MATH 522Mathematical Modeling3
MATH 577Computational Mathematics I3
Select a minimum of three credit hours from the following:3
Applied Analysis I3
Real Analysis
Probability 13
Probability
Discrete Applied Mathematics I3
Graph Theory and Applications
Mathematical Statistics 23
Statistics
Additional Requirements (0)
MATH 593Seminar in Applied Mathematics0
Specialization Courses (9-15)
Select 9-15 credit hours from an approved specialization 39-15
Master's Project (3-5)
MATH 594Professional Master's Project3-5
Elective Courses (3-11)
Select 3-11 credit hours 43-11

Master of Science in Applied Mathematics (Thesis Option)

Minimum Degree Credits 32
Maximum 400-Level Credit 9
Minimum MATH Credit 25
Core Courses (9)
MATH 577Computational Mathematics I3
Select six credit hours from the following:6
Applied Analysis I3
Real Analysis
Probability 13
Probability
Discrete Applied Mathematics I3
Graph Theory and Applications
Mathematical Statistics 23
Statistics
Additional Requirements (0)
MATH 593Seminar in Applied Mathematics0
Elective Courses (15-18)
Select 15-18 credit hours 315-18
Thesis Research (5-8)
MATH 591Research and Thesis M.S.5-8

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

Required Courses (9)
MATH 540Probability 13
or MATH 475 Probability
MATH 563Mathematical Statistics 13
MATH 564Applied Statistics3
Elective Courses (0) 2
Bioinformatics3
Online Social Network Analysis3
Probabilistic Graphical Models3
Machine Learning3
Natural Language Processing3
Machine and Deep Learning3
Design and Analysis of Exprmnt3
Optimization I3
Stochastic Processes3
Intro to Stochastic Processes
Introduction to Time Series3
Introduction to Time Series
Algebraic & Geometric Methods3
Monte Carlo Methods in Fin3
Adv Design of Experiments3
Design and Analysis of Exprmnt
Statistical Learning3
Bayesian Computational Stats3
Computational Mathematics II3
Meshfree Methods3
Computational Physics3

Discrete Computation and Optimization

Required Courses (9)
Select nine credit hours from the following:9
Applied/Computational Algebra3
Optimization I3
Discrete Applied Mathematics I3
Discrete Applied Math II3
Statistical Learning3
Elective Courses (0) 2
Dsgn and Anlys of Algorithms3
Game Theory: Algorithms & Apps3
Online Social Network Analysis3
Probabilistic Graphical Models3
Machine Learning3
Coding Reliable Communications3
Compt Vision Image Processing3
Applied Algebra3
Graph Theory and Applications 13
Stochastic Processes3
Intro to Stochastic Processes
Introduction to Time Series3
Introduction to Time Series
Algebraic & Geometric Methods3
Mathematical Statistics3
Applied Statistics
Monte Carlo Methods in Fin3
Adv Design of Experiments3
Design and Analysis of Exprmnt
Bayesian Computational Stats3

Industrial Mathematics

Note: The master's project track is required to pursue this specialization.

Required Courses (15)
MATH 540Probability 13
or MATH 475 Probability
MATH 522Mathematical Modeling 13
SCI 511Project Management3
or SCI 522 Public Engagement Scientists
MATH 523Case Studies & Project Design6
or MATH 592 Internship in Applied Math
Elective Courses (0) 2
Dsgn and Anlys of Algorithms3
Game Theory: Algorithms & Apps3
Online Social Network Analysis3
Probabilistic Graphical Models3
Machine Learning3
Applied Algebra3
Graph Theory and Applications 23
Stochastic Processes3
Intro to Stochastic Processes
Introduction to Time Series3
Introduction to Time Series
Algebraic & Geometric Methods3
Mathematical Statistics3
Applied Statistics
Monte Carlo Methods in Fin3
Adv Design of Experiments3
Design and Analysis of Exprmnt
Bayesian Computational Stats3

Quantitative Risk Management

Required Courses (12)
MATH 540Probability 13
or MATH 475 Probability
MATH 542Stochastic Processes3
or MATH 543 Stochastic Analysis
MATH 588Advanced Quant Risk Mgmt3
MATH 565Monte Carlo Methods in Fin3
or MATH 582 Mathematical Finance II
or MATH 587 Thry/Prac Modlng&Crdt Deritvs
Elective Courses (0) 2
Stochastic Analysis3
Stochastic Dynamics3
Stochastic Partial Diff Equatn
Introduction to Time Series3
Multivariate Analysis
Mathematical Statistics3
Applied Statistics
Statistical Learning3
Bayesian Computational Stats3
Computational Mathematics II3
Finite Element Method3
Num Meth for Partial Diff Equa
Meshfree Methods
Ther&Prac Fixed Income Modelng3

Stochastic Computation 

Required Courses (12)
MATH 540Probability 13
or MATH 475 Probability
Select nine credit hours from the following:9
Stochastic Processes3
Stochastic Analysis
Stochastic Dynamics3
Stochastic Partial Diff Equatn3
Monte Carlo Methods in Fin3
Bayesian Computational Stats3
Elective Courses (0) 2
Topics in Computer Science (Advanced Scientific Computing)3
Mathematical Modeling3
Applied/Computational Algebra3
Introduction to Time Series3
Statistical Learning3
Reliable Mathematical Software0
Computational Mathematics II3
Num Meth for Partial Diff Equa3