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Academic Programs

B.S. in Applied Mathematics

math student

Applied Mathematics

The B.S. in Mathematics degree program provides students with a strong foundation in the mathematics, data science, and physics.

The Applied Mathematics major helps students develop the computational and technology skills and ability to use quantitative reasoning and analysis in their careers. Students develop problem solving skills that can beyond the scope of mathematics.

Program Features

The program features many extracurricular opportunities to give students leadership/team experience and portfolio material. 

  • Students take part in a semester off-campus field experience that provides professional interaction and training in a student's chosen area of career focus.

What You'll Learn

From your first day, you’ll take courses in your major and advance towards graduation with a yearly plan. Not sure what classes to take? We’ll help you create the perfect plan. 

Learning Outcomes

  • Develop logical thinking and analytical skills needed for mathematical problem solving.

  • Develop quantitative reasoning and analysis skills needed for mathematical modeling.

  • Apply mathematical analysis and problem-solving skills in a range of applications in biological, physical, social sciences, and in public or private services.

  • Develop real world skills in computer technology, software, and algorithmic processes necessary in quantitative analysis and mathematical modeling.

  • Communicate effectively and collaborate well in multidisciplinary projects.

For a complete list of courses and learning outcomes, view the Academic Catalog >>

Accelerated Master's Program

Save time and money — earn your graduate degree in just 1 year with the Accelerated Master's program. Learn more and how to apply >>

Career Success with an Applied Mathematics Degree

Applied Mathematics majors are prepared for careers in almost every sector using mathematics for analysis, prediction, modeling, problem solving, and education. 

Our students have interned with:

  • Newton Public Schools
  • Boston Renaissance Charter School
  • America Counts

Student pursue careers in:

  • Operations research analyis
  • Education
  • Actuary Science
  • Mathematics



Request more information about the Applied Mathematics Degree:


DSCI102 - Introduction to Computer Science

This introduction to computer science, emphasizes problem solving and data analysis skills along with computer programming skills. Using Python, students learn design, implementation, testing, and analysis of algorithms and programs. And within the context of programming, they will learn to formulate problems, think creatively about solutions, and express those solutions clearly and accurately. Problems will be chosen from real-world examples such as graphics, image processing, cryptography, data analysis, astronomy, video games, and environmental simulation. Students get instruction from a world-class computer science professor, delivered remotely through video and interactive media and attend class for collaborative team projects to solve real-life problems. Prior programming experience is not a requirement for this course. Formerly: INTC102

DSCI306 - Advanced Python Programming

This course provides students with the opportunity to write useful Python applications in the ETL, web, and data analysis domains and knowledge of industry-standard tools and techniques for working within a development team. The course goes further into Python’s powerful advanced features, such as user-defined classes, object-oriented design, decorators, and generators. Students will learn to employ the most widely used algorithms and libraries to solve common problems in the field and gain a working familiarity with statistical analysis and visualization using Pandas, NumPy, and Matplotlib. Query and parse HTML, XML, and JSON are used. Students will learn to apply industry-standard tools and techniques for working within a development team, such as Git for versioning and code review. The course concludes with a discussion of common interview questions and pathways for gaining experience and eventually securing a position in the field. Prerequisites: DSCI102, DSCI202 and DSCI204.

DSCI309 - Biostatistics

This course introduces students to research method techniques and common statistical applications of importance to healthcare managers. Emphasis is placed on the study of statistical techniques for problem-solving and decision-making including the theoretical and applied statistical and quantitative skills required to understand, conduct and evaluate managerial research. Students will learn to distinguish between types of research (quantitative and qualitative) with an emphasis on the use of quantitative analysis in healthcare organizations. Basic research methods are described, including surveys, observational studies, experimental and quasi-experimental design; and the use of primary and secondary data sets. Statistical techniques for analyzing and interpreting data will include descriptive statistics, hypothesis testing, probability, sampling, t-tests, ANOVA, chi-square analysis, correlation, and linear regression.Prerequisite: MATH208 or MATH209

MATH205 - Calculus I

This course is an introduction to limits, continuity, and methods of differentiation. Application to problems in business management and physical science is emphasized. Prerequisite: MATH 203 with a grade of C or better or demonstrated competency through placement testing. Restrictions: not open to students who have completed MATH 206, or any 300 level mathematics courses.

MATH206 - Calculus II

This is a continuation of Calculus I. Includes graphical and analytic integration, partial differentiation, and solving differential equations. Applications include business, biological sciences, and physical sciences. Prerequisite: MATH 205 with a grade of C or better or demonstrated competency through placement testing. Restrictions: not open to students who have completed MATH 320, MATH 328, or MATH 330.

MATH208 - Statistics

This is an introductory course in descriptive and inferential statistics. Topics include: data analysis, and graphical methods of describing data, measures of central tendency and variability, probability, the normal distribution, sampling distributions, confidence intervals, hypothesis testing, correlation, and regression analysis. Prerequisites: MATH 106 with a grade of C or better or demonstrated competency through placement testing and ENG 102.

MATH212 - Finite Mathematics

The focus of this course is to develop mathematical models and to demonstrate the utility of various mathematical techniques that are most applicable to the creation of computer algorithms. Topics include functions and models, linear regression, solving systems of linear equations using matrices, matrix algebra and Leontief Input-Output models, linear programming (graphical and simplex methods), principle of duality, estimated and theoretical probability and Markov Chains. Prerequisite: MATH205 with a grade of C or better.

MATH215 - Discrete Math

Topics will include elementary logic and set theory, equivalence relations, functions, counting arguments, inductively defined sets, recursion, graphs and trees, Boolean algebra and combinatorial circuits, and countability arguments. Prerequisite: MATH203 with a C or better.

MATH305X - Advanced Statistics

Quantitative statistical tools for modern data analysis are used across a range of disciplines and industries to guide organizational, societal and scientific advances. Using data sets from across a variety of fields, the focus will be on applications and analysis. Topics include two sample confidence intervals, Chi Square tests, multiple regression analysis, ANOVA, non- parametric tests, sampling, and simulation. Prerequisite: Math 208 or Math 209

MATH307 - Calculus III

This course is an introduction to sequences and series, parametric and polar curves, vector functions, advanced techniques of differentiation and integration. Prerequisite: MATH 206 with a grade of C or better.

MATH325 - Linear Algebra

This is an introductory course in linear algebra blending the requirements of theory, problem solving, analytical thinking, computational techniques, and applications. Topics include in-depth treatment of matrix algebra, linear systems, vector spaces, linear transformations, determinants. Applications and modeling of real phenomena in transportation systems, economics, connectivity of networks, and graph theory. Prerequisite: MATH 206 with a grade of C or better.

MATH399 - Capstone Seminar

In this capstone course, Students investigate mathematics from a variety of fields and choose a topic for a mathematics project in their Field of Application. Mathematical methods for analysis, modeling, prediction, and/or problem solving are discussed. Students demonstrate knowledge of a substantial area of mathematics and present their work at a department seminar or the Connected Learning Symposium.

MATH499 - Internship

The internship seminar is a work or research experience where students combine theory and practice.

PHYS111 - General Physics I (KP)

This is the first semester of a one-year course that surveys the field of physics at a non-calcu­lus level. Topics include motion in one and two dimensions, force, uniform circular motion, work and energy, and statics of rigid bodies. The laws of thermodynamics are introduced. Laboratory experiments are conducted to complement the material covered in lecture. Prerequisite: MATH 203 or equivalent with a grade of C or better. Corequisite: PHYS111L, PHYS111R.

PHYS112 - General Physics II (KP)

This is a continuation of PHYS111. Topics include waves motion, electric potential, electric current, resistance, capacitance, and magnetism. Geometrical and wave optics are introduced. Atomic and quantum theory are also included. Laboratory experiments are conducted to com­plement the material covered in lecture. Prerequisite: PHYS 111 with a C or better. Corequisite: PHYS112L, PHYS112R.