This course introduces students to the use of computer software and computer programming for data exploration, modeling of natural systems (from biology, chemistry, or physics), information visualization, and instrument/robot control. This is done through independent research where students work in groups to design and pursue computational projects and then critically analyze, interpret and present their findings.

This course is for STM majors who are in their last year of the program. Students will produce two major products: (1) a grant proposal to a national or private agency and (2)interdisciplinary group project. In addition, students will discuss future career plans, examine contributions of different deaf scientists to science, and engage in discussions on science ethics and science literacy.

This course introduces students to probability and simulation. Students will learn how probability and simulation are applied in real-world decision-making scenarios. Topics covered include probability distributions (normal, binomial, Bernoulli, and Poisson), Markov chains and random walks, bootstrapping, and randomization tests. Applications will be emphasized throughout. A programming language will be used.

This course covers the fundamentals of machine learning for data scientists. Students will learn about training data, and how to use a set of data to discover potentially predictive relationships. Topics covered include supervised and unsupervised machine learning, generalized linear models including multivariate linear regression and binary logistic regression, automatic feature selection, bootstrapping, simple reinforcement learning, and decision trees. Applications will be emphasized throughout. A programming language will be used.

Ever wondered how companies like Amazon know more about you? Ever wondered how weather data is represented in the news? Using interdisciplinary concepts, we will learn how to tackle big data. Complex data sets are being generated continuously. Many questions arise as to what these data are telling us. Are we missing something? How do we look for signals in these large datasets? Using computer programs like Excel and R programming we will learn how to manage, sort and represent these data. Students will be encouraged to identify a data set related to a real world problem and use the tools learned in class to tell their stories.

Modern day biology has generated massive amounts of data but very few experts to analyze this data. A course in Genomics and Bioinformatics will teach students how to use computer algorithms to analyze the data. Students learn applications of genomics to biomedical and biological research by performing computational exercises using databases. Topics include genome sequencing gene prediction, genetic variation, sequence database searching, multiple sequence alignment, evolutionary tree construction, protein structure prediction, proteomic analysis, interaction networks and use of genome browsers among other topics.

The course introduces students to ArcGIS Online, an online Geographic Information System (GIS) application from Esri. With GIS, the student can explore, visualize, and analyze data; create 2D maps and 3D scenes with several layers of data to visualize multiple data sets at once; and share work to an online portal. GIS analytics tools are used in many disciplines and fields of practice including public health, history, sociology, political science, business, biology, international development, and information technology. In the end of the course, students will have the opportunity to take additional training on GIS applications in their specific field of interest.

In this course, students learn problem-solving and programming coding skills to develop software applications/tools. Students are introduced to a high-level programming language. Topics include data types, selections, loops, methods, arrays, objects and classes, strings and text I/O, arithmetic and logic operations, control structures and error handling. Students will learn techniques to design, code, debug, and document programs through hands-on programming projects.

This course teaches logical and physical characteristics of data and their organization and retrieval in information processing. Topics include database theory and architecture, data modeling, normalization. Students will learn to use PC-based database management system (DBMS) software and design and implement database applications.

In this course, students will be introduced to algorithms, the analysis of algorithms, foundational data structures, and various problem-solving paradigms. Topics covered include: arrays, linked lists, trees, hash tables, divide and conquer, greedy method, dynamic programming, backtracking, and branch and bound technique.

Special topics in the discipline, designed primarily for seniors who are majors or minors.  Students may enroll in 495 Special Topics multiple times, as long as the topics differ.

This course continues the development of the principles of a high-level programming language introduced in the Programming Language I course. Topics include: data abstraction, encapsulation, overloaded and overridden methods, inheritance, polymorphism, even-driven programming, and exception handling.

Special topics in the discipline, designed primarily for seniors who are majors or minors. Students may enroll in 495 Special Topics multiple times, as long as the topics differ.

This course is a continuation of Calculus I. Topics covered include: inverse functions, techniques of integration (integration by parts, trigonometric substitution, and partial fractions), parametric equations, polar coordinates, sequences and series, power series, and Taylor series. Applications are included throughout.

This is an introductory course in cryptography. It covers classical cryptosystems, Shannon's perfect secrecy, block ciphers and the advanced encryption standard, RSA cryptosystem and factoring integers, public-key cryptography and discrete logarithms, and linear and differential cryptanalysis.

This course covers linear programming, the simplex algorithm, duality theory and sensitive analysis, network analysis, transportation, assignment, game theory, inventory theory, and queuing theory.

Numerical differentiation, integration, interpolation, approximation of data, approximation of functions, iterative methods of solving nonlinear equations, and numerical solutions of ordinary and partial differential equations.

This course covers statistical techniques with applications to the type of problems encountered in real-world situations. These topics include categorical data analysis, simple linear regression, multiple regression, and analysis of variance. A statistical software package is used.

Special topics in the discipline, designed primarily for seniors who are majors or minors. Students may enroll in 495 Special Topics multiple times, as long as the topics differ.

This course is an introductory class that aims to show the students the main problems and methods of data science with a minimal mathematical background. The course covers basic data science concepts and algorithms with an emphasis in real-life applications and gaining a broad understanding of the area.

This course introduces fundamental concepts of computer programming. Students learn program logic, flow charting, and problem solving through analysis, development, basic debugging and testing procedures. Topics include variables, expressions, data types, functions, decisions, loops, and arrays. Students will use the knowledge and skills gained throughout this course to develop a variety of simple programs.

This course provides students with the necessary skills to study calculus and various other mathematics, science, and computer related courses. Students will learn the properties of various types of functions, graph them, and solve equations involving these functions. Topics covered include: polynomial, rational, exponential, and logarithmic functions, trigonometric functions and identities, and sequences and series. Applications are included throughout. Passing both MAT 125 College Algebra and MAT 126 Trigonometry is equivalent to passing MAT 130.

Number systems, set theory, functions, combinatorics, algorithms and complexity, and graph theory. Applications to computer science are emphasized.

This course provides students with a comprehensive understanding of differential and integral calculus for single variable functions, including polynomial, exponential, logarithmic, and trigonometric functions. Topics covered include: limits, continuity, differentiation, L’Hôpital’s rule, and the Fundamental Theorem of Calculus. Applications of differentiation and integration to mathematical and physical problems are covered throughout.

This course develops the mathematical concepts and techniques involved in multivariable calculus. Topics covered include: vector calculus, partial derivatives, multiple integrals, cylindrical and spherical coordinates, line integrals, Green's and Stokes’ Theorems. Applications are included throughout.

This course covers the fundamental concepts of vector spaces, linear transformations, systems of linear equations, and matrix algebra from a theoretical and a practical point of view. Results will be illustrated by mathematical and physical examples. Important algebraic (e.g., determinants and eigenvalues), geometric (e.g., orthogonality and the Spectral Theorem), and computational (e.g., Gauss elimination and matrix factorization) aspects will be studied.

This course is the first part of a two-semester sequence with MAT 314, with a focus on basic probability. It covers descriptive statistics, sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distribution of discrete and continuous random variables, joint distributions, and the central limit theorem.

This course is the second part of a two-semester course sequence with MAT 313, with a focus on applied statistics. It covers basic statistical concepts, graphical displays of data, sampling distribution models, hypothesis testing, and confidence intervals. A statistical software package is used.