Courses

First semester of a two-semester, comprehensive general chemistry course that introduces students to the basic principles of chemistry with an emphasis on the relationships between the microscopic structure and the macroscopic properties of matter.

Introduction to quantitative experimentation in chemistry.

Introduction to digital systems and their design. 

Microprocessor architecture; memory system design; assembly language programming; interrupts; the stacks and subroutines; memory and I/O inter-facing; serial I/O and data communications; microprocessors applications.

Resource management in computer systems.

Data transmission, encoding, link control techniques, network architecture, design, protocols, and multiple access.

Introduction to programming techniques design and implementation of algorithms. Structured programming. Problem-solving techniques. Introduction to a high-level language, including arrays, procedures, and recursion

Object-oriented programming, recursion, fundamental data structures (including stacks, queues, linked lists, hash tables, trees, and graphs), the basics of algorithmic analysis, and an introduction to the principles of language translation.

Discrete mathematics and foundations for modern computer science.

Topics include: conceptual data modeling, relational data model, relational query languages, schema normalization, database/Internet applications, and database system issues.

Fundamental concepts of programming language design, specifications, and implementation; programming language paradigms and features; program verification.

Fundamental concepts of computer science: data structures, analysis of algorithms, recursion, trees, sets, graphs, sorting.

Writing for students in scientific and technical disciplines.

Introduces and develops the mathematical skills required for analyzing change and creating mathematical models that replicate real-life phenomena. Develops student knowledge of calculus techniques and how to use the calculus environment to develop critical thinking and problem-solving skills.

Derivatives, integrals, applications; sequences and series; analytic geometry; polar coordinates.

Systems of linear equations; matrix algebra; eigenvalues and eigenvectors; linear systems of differential equations.

First- and second-order equations; special functions; Laplace transform solutions; higher order equations.

Examination of fundamental principles and processes applicable to the study of management. Not available to students who have taken BA 304.

Calculus-based study of the basic concepts of mechanics: motion, force, Newton's laws, energy, collisions, and rotation.

Calculus-based study of the basic concepts of electricity and magnetism.

Combinatorial analysis, axioms of probability, conditional probability and independence, discrete and continuous random variables, expectation, limit theorems, additional topics

Design, documentation, testing, and construction of software using software engineering strategies embodied in object-oriented programming languages.

Software engineering principles including lifecycle, dependability, process modeling, project management, requires specification, design analysis, implementation, testing, and maintenance.

The analysis and design of software systems using canonical design patterns.

Introduction to methods of software verification, validation, and testing; mathematical foundations of testing, reliability models; statistical testing.

The design and implementation of real time systems.

Concepts of engineering ethics, economy, and project management, senior capstone project selection, and technical communication skills.

Capstone group design projects in software engineering.

D.C. and A.C. circuits, transformers, single and three-phase distribution systems, A.C. motors and generators.

This course will start with an overview of supervised and unsupervised learning, and introduce the associated libraries. It covers basic machine learning concepts, tasks, and workflow using an example classification problem based on K-nearest neighbors, Naïve Bayes, Support Vector Machine (SVM), K-means, and implementation using Python libraries.

This course introduces software quality, the associated quality factors, software errors and the software quality assurance system.