Applied Mathematics, MS (Boston)

New applications of mathematics are constantly being discovered, and established techniques are being applied in new ways and in emerging fields. Northeastern University's Master of Science in Applied Mathematics caters to students who are looking to enter or who are currently working in a variety of applied math careers such as data science and high-tech firms, computer information and software firms, financial service and investment firms, academic institutions, and research institutes. Northeastern’s master's program offers students an opportunity to obtain solid knowledge of mathematical modeling, statistics, and data analysis, as well as excellent programming skills, through experiential learning and co-op experiences from industry.

Students may complete a data science concentration, a mathematical foundations of intelligence concentration, an operations research concentration, or an electives option. The value of the worldwide Big Data market is growing exponentially. Data science rooted in applied mathematics is essential in exploring Big Data from a wide range of science, businesses, and industries. The mathematical foundations of intelligence concentration provides students with rigorous training in the mathematical principles underlying machine learning and artificial intelligence, including optimization, probability, statistical learning theory, and algorithmic modeling. The operations research concentration offers students study of the analytical and computational skills necessary to enhance efficiency and support decision making across diverse industries such as logistics, finance, and engineering. The electives option allows students to personalize their education with more in-depth knowledge of data science, statistics, or other areas of interest in applied mathematics.

Data Science Concentration

Data science is an interdisciplinary field using techniques and theories drawn from mathematics, statistics, computer science, information science, and related fields to uncover insights hidden in data and to make predictions and decisions. The data science concentration offers students opportunities to learn mathematical modeling, probability, and statistics, which are the theoretical background for data science. Courses in data visualization; machine learning and deep learning; statistical inference; algorithmic, numerical, and computational thinking; experimental design; and coding are also offered. The program offers a multimodal approach including lecture courses, data-driven computer labs, and industry projects. Some courses listed for this program are in collaboration with the College of Engineering and the Khoury College of Computer Sciences.

Mathematical Foundations of Intelligence Concentration

The mathematical foundations of intelligence concentration emphasizes the central role of applied mathematics in the design and analysis of modern intelligent systems. It provides students with a rigorous foundation in linear algebra, probability theory, optimization, statistical learning theory, and numerical methods, which form the theoretical backbone of machine learning and artificial intelligence. The concentration integrates these mathematical principles with advanced topics such as deep learning, reinforcement learning, computer vision, and natural language processing, together with the computational and algorithmic techniques required to implement and analyze intelligent models. The learning experience is reinforced through lecture courses, computational laboratories, and industry-oriented projects, preparing students to develop, understand, and advance intelligent systems from a principled mathematical perspective.

Operations Research Concentration

The operations research concentration offers students an opportunity to obtain a deep understanding of mathematical modeling and optimization techniques to solve complex operational problems. This concentration emphasizes areas such as decision analysis, stochastic processes, and advanced optimization to prepare students to tackle real-world challenges in fields like supply chain management, finance, and systems engineering. By integrating applied mathematics with problem-solving methodologies, students may gain the analytical and computational skills needed to enhance efficiency and inform decision making in both industry and research settings. Some courses listed for this program are offered in the College of Engineering.

Electives Option

Applications of mathematical modeling and methods are widely involved in different fields such as computer science, engineering, finance, health science, social science, artificial intelligence, etc. The electives option allows students to design their advanced coursework around an area of specific interest, pursue personalized training in applied mathematics, or strengthen an application to a PhD program.

Beginning with students admitted in the fall of 2026, all students pursuing on-ground master's degree programs must complete a university-level experiential learning requirement. This requirement may be fulfilled through the curriculum, co-op, or other work- or industry-integrated learning experiences.

Complete all courses and requirements listed below unless otherwise indicated.

​Core Requirements

Course List

Code	Title	Hours
Modeling and Linear Algebra
Complete 4 semester hours from the following:		4
MATH 5110	Applied Linear Algebra and Matrix Analysis
MATH 5111	Algebra 1
MATH 5131	Introduction to Mathematical Methods and Modeling
Probability and Analysis
Complete 4 semester hours from the following:		4
MATH 5101	Analysis 1: Functions of One Variable
MATH 7241	Probability 1
Statistics
Complete 8 semester hours from the following:		8
MATH 7243	Machine Learning and Statistical Learning Theory 1
MATH 7342	Mathematical Statistics
MATH 7343	Applied Statistics

Concentrations or Electives Option

A concentration is not required. Students may complete the electives option in lieu of a concentration.

• Data Science 
• Mathematical Foundations of Intelligence
• Operations Research
• Electives Option 

Optional Co-Op Experience

Course List

Code	Title	Hours
Complete the following. Students must complete EESC 6500 to qualify for co-op experience:
EESC 6500	Pathways to Professional Success	1
Complete one of the following:		0
EESC 6964	Co-op Work Experience
EESC 6954	Co-op Work Experience - Half-Time
EESC 6965	Co-op Work Experience Abroad

Program Credit/GPA Requirements

32 total semester hours required (33 semester hours for students who opt to participate in co-op)
Minimum 3.000 GPA required

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​Data Science Concentration 

Course List

Code	Title	Hours
No more than 8 semester hours of coursework outside of the MATH subject code may be applied to the requirements of this concentration.
Core
Complete 8 semester hours from the following (students may take other Khoury College of Computer Sciences courses not on the list in consultation with their faculty advisor):		8
CS 5800	Algorithms
CS 6140	Machine Learning
CS 6220	Data Mining Techniques
DA 5020	Collecting, Storing, and Retrieving Data
DA 5030	Introduction to Data Mining/Machine Learning
DS 5220	Supervised Machine Learning and Learning Theory
DS 5230	Unsupervised Machine Learning and Data Mining
EECE 5644	Introduction to Machine Learning and Pattern Recognition
MATH 7243	Machine Learning and Statistical Learning Theory 1
Electives
Complete 8 semester hours of courses at the 5000 level or above in the following subject area. See suggested elective course list.		8
MATH

​Mathematical Foundations of Intelligence Concentration 

Course List

Code	Title	Hours
No more than 8 semester hours of coursework outside of the MATH subject code may be applied to the requirements of this concentration.
Core
Complete 8 semester hours from the following (students may take other CS or EECE courses not on the list in consultation with their faculty advisor):		8
CS 5100	Foundations of Artificial Intelligence
CS 5130	Applied Programming and Data Processing for AI
CS 5180	Reinforcement Learning and Sequential Decision Making
CS 5330	Pattern Recognition and Computer Vision
or EECE 5639	Computer Vision
CS 6120	Natural Language Processing
CS 6180	Foundations for Generative AI
CS 7150	Deep Learning
CS 7180	Special Topics in Artificial Intelligence
Electives
Complete 8 semester hours of courses at the 5000 level or above in the following subject area. See suggested elective course list.		8
MATH

Operations Research Concentration

Course List

Code	Title	Hours
No more than 8 semester hours of coursework outside of the MATH subject code may be applied to the requirements of this concentration.
Core
Complete 8 semester hours from the following (students may take other College of Engineering courses not on the list in consultation with their faculty advisor):		8
IE 7200	Supply Chain Engineering
MATH 7207	Algorithms for Optimization
MATH 7234	Optimization and Complexity
MATH 7341	Probability 2
OR 6205	Deterministic Operations Research
OR 7230	Probabilistic Operation Research
OR 7245	Network Analysis and Advanced Optimization
OR 7310	Logistics, Warehousing, and Scheduling
Electives
Complete 8 semester hours of courses at the 5000 level or above in the following subject area. See suggested elective course list.		8
MATH

Electives Option

Course List

Code	Title	Hours
Complete 16 semester hours in the following subject area. Students may take MATH courses at the 5000 level or above listed in other concentrations or the suggested elective course list. Courses outside of MATH may be chosen with faculty approval.		16
No more than 8 semester hours of coursework outside of the MATH subject code may be applied to requirements of this option.
MATH

Suggested Electives Course List

Course List

Code	Title	Hours
Students may complete other MATH courses not listed below and courses outside of MATH in consultation with their faculty advisor:
DS 5110	Essentials of Data Science
EECE 7205	Fundamentals of Computer Engineering
MATH 5010	Foundations of Statistical Theory and Probability
MATH 5352	Quantum Computation and Information
MATH 6241	Stochastic Processes
MATH 6243	Statistical Learning
MATH 6910	Master's Project
MATH 7203	Numerical Analysis 1
MATH 7205	Numerical Analysis 2
MATH 7207	Algorithms for Optimization
MATH 7223	Riemannian Optimization
MATH 7233	Graph Theory
MATH 7234	Optimization and Complexity
MATH 7339	Machine Learning and Statistical Learning Theory 2
MATH 7341	Probability 2
MATH 7342	Mathematical Statistics
MATH 7344	Regression, ANOVA, and Design
MATH 7980	Capstone: Applications of Statistics and Modeling
Additional Courses
The following are some theoretical MATH courses usually taken in the PhD program (these may not be offered every academic year):
MATH 5102	Analysis 2: Functions of Several Variables
MATH 5112	Algebra 2
MATH 7202	Partial Differential Equations 1
MATH 7221	Topology 2
MATH 7315	Algebraic Number Theory
MATH 7320	Modern Algebraic Geometry
MATH 7371	Morse Theory
MATH 7381	Topics in Combinatorics
MATH 7741	Readings in Probability and Statistics
MATH 8450	Research Seminar in Mathematics