UMass Dartmouth's Mathematics department offers both BA and BS degrees in mathematics with concentrations in applied statistics andapplied & computational mathematics, as well as a degree in data science, taught jointly by the mathematics department and college of engineering.
With a core of courses covering randomness and reasoning under uncertainty, the applied statistics concentration emphasizes concepts, principles, and methodology needed for data analysis, decision making, and computing for applied problems that involve uncertainty.
Program curriculum and details
You’ll learn in-depth skills in mathematics and statistics to work as an applied statistician in a variety of industries, government, or nonprofit agencies, or for graduate studies. You’ll also gain effective communication skills for the presentation of analysis or results.
BS degrees in mathematics
degrees require completion of 120 credit hours of overall coursework
take an additional 6 credits in natural science courses to earn the BS degree in mathematics
humanities/social science requirements for the BS degree are a combined total of 18 credits
BA degree in mathematics, you'll take an additional 3 credits in natural science courses
humanities/social science requirements for the BA degree are a combined total of 21 credits
Applied Statistics curriculum
Our curriculum offers flexibility, allowing you to concentrate in your areas of interest. You'll have a wide selection of courses to choose from, including algebra, calculus, computational mathematics, geometry, probability, simulations, and statistics. You will learn to how to:
Understand core mathematical skills
Form logical arguments with correct reasoning
Recognize connection between different areas of mathematics and understand relationships between ideas
For the major, you'll complete 59 credit hours in courses related to mathematics or physics, and 6 credits in English, 6 credits in Literature, and an additional 27 credits in upper level courses.
For the appliedstatistics concentration, you'll complete core courses Math US-5A and 5B, and 9 credits of recommended Mathematics (MTH), Computer Information Science (CIS), or Data Science (DSC) electives.
Courses
CIS 360
Algorithms & Data Struct
Comprehensive coverage of all major groups of algorithms, including divide-and-conquer, dynamic programming, greedy, backtracking, branch-and-bound, and parallel algorithms. Discussion of the design and implementation of complex, dynamic data structures. The course also covers an introduction to the functional paradigm.
Course type:
Lecture
Course hours:
3 hours per week
MTH 432
Applied Statistical Consulting
Engagement of students with communities having statistical analysis needs. Academic faculty, graduate and undergraduate students, external businesses. Focus on problems of statistical analysis of data or design of experiments. Skills include multiple regression, analysis of variance, design of experiments, nonlinear estimation, spatial and time series analysis, contingency table analyses. Topics covered include: research process, questionnaire design, experimental design, sampling methods, data collection and preparation, data analysis, statistical report writing
MTH 431
Applied Statistical Investigation
Investigation in applied statistics. Topics for investigation chosen in
MTH 151
Calculus I
An intensive study of differential calculus and its applications, and an introduction to integrals, Topics include: limits, continuity, indeterminate forms, differentiation and integration of algebraic and transcendental functions, implicit and logarithmic differentiation, and applications to science and engineering. This is the first semester of the standard calculus sequence designed for students interested in Mathematics, Physics, Chemistry, Engineering and Mathematical/Computational Biology. This course fulfills the general education core requirements for Mathematics, Physics, Chemistry, Engineering and Mathematical/Computational Biology majors who matriculated prior to Fall 2012 and has been approved by University Studies Curriculum for students matriculating in Fall 2012 or later.
Course type:
Lecture
Course hours:
4 hours per week
MTH 152
Calculus II
An intensive study of the techniques and applications of integration and infinite series. Topics include: techniques of integration and its application, improper integrals, infinite series (including convergence tests, the interval of convergence for power series, and Taylor series), and parametric equations and polar coordinates. This is the second semester of the standard calculus sequence designed for students interested in Mathematics, Physics, Chemistry, Engineering and Mathematical/Computational Biology. This course fulfills the general education core requirements for Mathematics, Physics, Chemistry, Engineering and Mathematical/Computational Biology majors who matriculated prior to Fall 2012 and has been approved by University Studies Curriculum for students matriculating in Fall 2012 or later.
Course type:
Lecture
Course hours:
4 hours per week
MTH 421
Complex Analysis
Analytic functions, differentiation, integration, conformal mapping, calculus of residues and infinite series.
Course type:
Lecture
Course hours:
3 hours per week
CIS 115
Computer Program With C
Algorithm development, syntax and semantics of a high level programming language, debugging and verification of programs. Concepts of structured programming. Arrays, subroutines. Elementary system concepts (compilation, time-sharing).
Course type:
Lecture
Course hours:
4 hours per week
DSC 201
Data Analysis and Visualization
An introduction to data analysis with a focus on visualization. Topics include: visualization of scalar, vector and tensor data; software tools for image, volume and information visualization and analysis;descriptive statistics; time dependent data; data patterns; analyzing propositions, correlations, and spatial relationships. Application of these topics to natural sciences and engineering are discussed.
Course type:
Lecture
Course hours:
3 hours per week
MTH 181
Discrete Mathematics I
An introduction to mathematical reasoning, mathematical logic, and methods of proof. Topics include: properties of numbers, elementary counting methods, discrete structures, Boolean algebra, introduction to directed and undirected graphs, methods of proof, and applications in mathematics and computer science. This is the first semester of a discrete mathematics sequence designed for Mathematics, Computer and Information Sciences majors. This course fulfills the general education core requirements for Mathematics, Computer and Information Sciences majors who matriculated prior to Fall 2012 and has been approved by University Studies Curriculum for students matriculating in Fall 2012 or later.
Course type:
Lecture
Course hours:
3 hours per week
MTH 182
Discrete Mathematics II
A study of mathematical foundations for advanced mathematics and theoretical computer science. Topics include: mathematical reasoning including mathematical induction, combinatorial analysis including simple probability, discrete structures (such as sets, recursions, relations, and trees), algorithmic thinking, applications, and modeling (such as combinatorial circuits). This is the second semester of a discrete mathematics sequence designed for Mathematics and Computer Information Sciences majors. This course fulfills the general education core requirements for Mathematics, Computer and Information Sciences majors who matriculated prior to Fall 2012 and has been approved by University Studies Curriculum for students matriculating in Fall 2012 or later.
Course type:
Lecture
Course hours:
3 hours per week
MTH 231
Elementary Statistics I: Exploratory Data Analysis
Introduction to exploratory data analysis using R, including graphical techniques, confirmatory statistics, interval estimates, hypothesis tests, bootstrap estimates.
Course type:
Lecture
Course hours:
3 hours per week
MTH 461
Elementary Topology
An introduction to basic ideas of modern topology. Topics chosen from: topological spaces, continuous functions, topological equivalence, identification spaces, surfaces, homotopy, fundamental groups, knots and links.
Course type:
Lecture
Course hours:
3 hours per week
MTH 147
Fundamentals of Statistics
An introduction to statistics and probability. Topics include: measures of central tendency and dispersion, elementary probability, binomial, normal, and t-distributions, hypothesis testing and confidence intervals, and a survey of correlation and regression analysis. It is recommended for Psychology, Sociology, Nursing, or other Liberal Arts and Fine Arts majors whose degree program does not require any other courses in mathematics. The course fulfills the general education requirements for sociology, psychology, nursing, education, and other related majors who matriculated prior to Fall 2012 and has been approved by University Studies Curriculum for students matriculating in Fall 2012 or later.
Course type:
Lecture
Course hours:
3 hours per week
MTH 420
High Performance Scientific Computing
Topics in high performance computing (HPC). Topics will be selected from the following: parallel processing, computer arithmetic, processes and operating systems, memory hierarchies, compilers, run time environment, memory allocation, preprocessors, multi-cores, clusters, and message passing. Introduction to the design, analysis, and implementation, of high-performance computational science and engineering applications.
Course type:
Lecture
Course hours:
3 hours per week
DSC 101
Introduction to Data Science
Foundational topics in data science. Students will learn a broad range of data science skills applicable across different domains, including social sciences, finance, crime and justice, social networks, and engineering. Students will develop statistical and computational thinking skills, and they will apply these skills to real-world datasets. Specific topics include applied data problems, statistical software, data frames, descriptive statistics, natural language processing, data storage, data merging, linear regression, and data mining. The core skills developed in this course lay a foundation for more advanced coursework in data management, visualization, exploratory data analysis, and machine learning. No prior knowledge of programming or statistics is required.
Course type:
Lecture
Course hours:
3 hours per week
MTH 280
Introduction to Scientific Computation
A calculus-based introduction to scientific computation, modeling, simulation and visualization using a variety of mathematics programming tools, scripting languages, and other software tools widely used by mathematicians. This course is project-driven and requires a strong background in mathematics. It is intended for students planning to take upper-level courses in applied or computational mathematics.
Course type:
Lecture
MTH 311
Introductory Real Analysis I
This course is a rigorous analysis of the concept of limits, continuity, the derivative and other selected areas.
Course type:
Lecture
Course hours:
3 hours per week
MTH 312
Introductory Real Analysis II
Continuation of MTH 311 with emphasis on uniform convergence and related topics.
Course type:
Lecture
Course hours:
3 hours per week
MTH 463
Math Modeling
Selected topics from the areas of linear programming, dynamic programming, Markov chains and game theory. Mathematical model building will be developed through the use of numerous case studies from the natural and social sciences, e.g., ecological models, network models, scheduling models, urban structure, traffic flow, growth, etc.
Course type:
Lecture
Course hours:
3 hours per week
MTH 332
Mathematical Statistics
Continuation of MTH 331. Classical estimation methods and hypothesis testing are studied. This course also covers Chi square tests for goodness-of-fit and independence, regression and correlation analysis, and one-way and two-way analysis of variance including factorial designs and tests for the separation of means.
Course type:
Lecture
Course hours:
3 hours per week
DSC 301
Matrix Methods for Data Analysis
Matrix methods with emphasis on applied data analysis. Matrix norms; LU, QR and SV decomposition of matrices; least squares problems, orthogonal vectors and matrices; applications to data analysis.
Course type:
Lecture
Course hours:
3 hours per week
MTH 441
Modern Algebra I
The study of relations, functions, groups, rings and fields.
Course type:
Lecture
Course hours:
3 hours per week
MTH 361
Numerical Analysis I
Theory and computer-oriented practice in obtaining numerical solutions of various problems. Topics include stability and conditioning, nonlinear equations, systems of linear equations, interpolation and approximation theory.
Course type:
Lecture
Course hours:
3 hours per week
MTH 362
Numerical Analysis II
Numerical methods for solving initial value problems. Topics include: numerical differentiation and integration, Euler method and Taylor's series method, Runge-Kutta methods, multi-step methods, and stiff equations
Course type:
Lecture
Course hours:
3 hours per week
MTH 473
Numerical Linear Algebra
An introduction to numerical linear algebra. Numerical linear algebra is fundamental to all areas of computational mathematics. This course will cover direct numerical methods for solving linear systems and linear least squares problems, stability and conditioning, computational methods for finding eigenvalues and eigenvectors, and iterative methods for both linear systems and eigenvalue problems.
Course type:
Lecture
Course hours:
3 hours per week
MTH 474
Numerical Optimization
An introduction to constrained and unconstrained optimization. Numerical optimization is an essential tool in a wide variety of applications. The course covers fundamental topics in unconstrained optimization and also methods for solving linear and nonlinear constrained optimization problems.
Course type:
Lecture
Course hours:
3 hours per week
CIS 180
Object-Oriented Prog I
Basic concepts in programming, and introduction to the object paradigm. The course introduces the concept of the object paradigm and teaches how to design and implement simple programs in an object-oriented language. The course also covers the basics of how to use a computer and basic software tools, including libraries of components, in the process of developing programs.
Course type:
Lecture
Course hours:
5 hours per week
CIS 181
Object-Oriented Prog II
Software development using advanced object paradigm concepts. This course introduces threads, networking, and exception handling and covers in depth the advanced topics of object paradigm such as inheritance and polymorphism. These concepts are introduced in the context of developing software using software tools including the libraries of components.
Course type:
Lecture
Course hours:
5 hours per week
MTH 331
Probability
A calculus-based introduction to statistics. This course covers probability and combinatorial problems, discrete and continuous random variables and various distributions including the binomial, Poisson, hypergeometric normal, gamma and chi-square. Moment generating functions, transformation and sampling distributions are studied.
Course type:
Lecture
Course hours:
3 hours per week
MTH 499
Selected Topics In Math
A special course to meet the needs of students for material not encountered in other courses. Topics dealt with require the approval of the departmental chairperson.
Course type:
Lecture
Course hours:
3 hours per week
MTH 465
Small World Networks
Modeling small-world networks. This experiential-learning course focuses on the simulation and analysis of small Âworld networks, including social networks, food chains and the world-wide web. Models will include regular lattices, random graphs, Strogatz-Watts networks, an random accretion models of Barabasi and Albert and of Aeillo, Chung and Lu.
Course type:
Lecture
MTH 333
Statistical Computing
Introduction to statistical computing with the R programming language. Topics chosen from: numerical and graphical statistical analyses: algorithms in statistical computing; random number generation: generating distributions: random sampling and permutations; bootstrapping: matrix computations in linear models; non-linear optimization; R constructs: functions: objects; data structures; flow control; input and output; debugging; logical design; tidyverse and other R packages.
Enhance your career options by earning a minor in mathematics. You'll develop the analytical and problem-solving skills that are essential in many employment settings.
Community: participate in our chapter of the Society for Industrial and Applied Mathematics or the student-led group, Mathematics and Physics Opportunities for Women in Research
International (F-1) students who receive science, technology, engineering, and mathematics (STEM) degrees may be eligible to apply for a 24-month extension of their post-completion optional practical training (OPT). To learn about the eligibility criteria and detailed steps to apply, please review the International Student & Scholar Center (ISSC) OPT page and USCIS resources. F-1 students must consult with the ISSC to apply for STEM OPT.
Master of Science in Data Science: Through a joint initiative with the Computer and Information Science department, we will be offering a Master's degree in Data Science.
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