Computer Science I

This course serves as an introduction to computational thinking using a problem-centered approach. Specific syllabus covered include: expression of algorithms in pseudo code and a programming language; functional and imperative programming techniques; control structures; problem solving using recursion; basic searching and sorting; elementary data structures such as lists, trees, and graphs; and correctness, testing and debugging. Assignments (both in class and for homework) requiring a pseudo code solution and an implementation are an integral part of the course. An end-of-term project is also required. Lec/Lab 6 (Fall, Spring).

Computer Science II

This course delves further into problem solving by continuing the discussion of data structure use and design, but now from an object-oriented perspective. Key syllabus include more information on tree and graph structures, nested data structures, objects, classes, inheritance, interfaces, object-oriented collection class libraries for abstract data types (e.g. stacks, queues, maps, and trees), and static vs. dynamic data types. Concepts of object-oriented design are a large part of the course. Software qualities related to object orientation, namely cohesion, minimal coupling, modifiability, and extensibility, are all introduced in this course, as well as a few elementary object-oriented design patterns. Input and output streams, graphical user interfaces, and exception handling are covered. Students will also be introduced to a modern integrated software development environment (IDE). Programming projects will be required. (Prerequisites: CSCI-141 with a grade of C- or better or equivalent course.) Lec/Lab 6 (Fall, Spring, Summer).

General Education – Mathematical Perspective A: Project-Based Calculus I

This is the first in a two-course sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers functions, limits, continuity, the derivative, rules of differentiation, applications of the derivative, Riemann sums, definite integrals, and indefinite integrals. (Prerequisite: A- or better in MATH-111 or A- or better in ((NMTH-260 or NMTH-272 or NMTH-275) and NMTH-220) or a math placement exam score greater than or equal to 70 or department permission to enroll in this class.) Lecture 6 (Fall, Spring, Summer).

General Education – Mathematical Perspective B: Project-Based Calculus II

This is the second in a two-course sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers techniques of integration including integration by parts, partial fractions, improper integrals, applications of integration, representing functions by infinite series, convergence and divergence of series, parametric curves, and polar coordinates. (Prerequisites: C- or better in (MATH-181 or MATH-173 or 1016-282) or (MATH-171 and MATH-180) or equivalent course(s).) Lecture 6 (Fall, Spring, Summer).

General Education – Elective: Discrete Mathematics for Computing

This course introduces students to ideas and techniques from discrete mathematics that are widely used in Computer Science. Students will learn about the fundamentals of propositional and predicate calculus, set theory, relations, recursive structures and counting. This course will help increase students’ mathematical sophistication and their ability to handle abstract problems. (Co-requisites: MATH-182 or MATH-182A or MATH-172 or equivalent courses.) Lecture 3 (Fall, Spring).

RIT 365: RIT Connections

RIT 365 students participate in experiential learning opportunities designed to launch them into their career at RIT, support them in making multiple and varied connections across the university, and immerse them in processes of competency development. Students will plan for and reflect on their first-year experiences, receive feedback, and develop a personal plan for future action in order to develop foundational self-awareness and recognize broad-based professional competencies. Lecture 1 (Fall, Spring).

General Education – First Year Writing (WI)

General Education – Ethical Perspective

General Education – Artistic Perspective

General Education – Global Perspective

General Education – Social Perspective

Undergraduate Co-operative Education Seminar

This seminar helps students prepare for Computer Science co-operative education employment (“co-op”) by developing job search strategies and materials, and reviewing relevant policies. Students are introduced to RIT’s Office of Career Services and Cooperative Education, and learn about professional and ethical responsibilities for their co-op and subsequent professional experiences. Completion of this seminar and the related assignments are required before a CS student can be registered for co-op. (Prerequisites: This class is restricted to COMPSCI-BS or COMPEX-UND Major students with at least 2nd year standing.) Lecture 1 (Fall, Spring).

The Mechanics of Programming

Students will be introduced to the details of program structure and the mechanics of execution as well as supportive operating system features. Security and performance issues in program design will be discussed. The program translation process will be examined. Programming assignments will be required. (Prerequisite: C- or better in CSCI-140 or CSCI-142 or CSCI-242 or SWEN-124 or CSEC-124 or GCIS-124 or equivalent course.) Lecture 3 (Fall, Spring, Summer).

CS Undergraduate Summer Co-op (summer)

Students perform professional work related to Computer Science for which they are paid. Students must complete a student co-op work report for each term for which they are registered; students are also evaluated each term by their employer. A satisfactory grade is given for co-op when both a completed student co-op work report and a completed, corresponding employer evaluation are received and when both documents are generally consistent. When registered for co-op, students are considered by RIT to have full-time status. In order to register for co-op for summer term, we expect that students will work a minimum of 10 weeks and work a minimum of 35 hours per week. CO OP (Summer).

3

   Introduction to Computer Science Theory

This course provides an introduction to the theory of computation, including formal languages, grammars, auto-mata theory, computability, and complexity. (Prerequisites: (MATH-190 or MATH-200) and (CSCI-140 or CSCI-141 or CSCI-242 or SWEN-123 or SWEN-124 or CSECI-123 or CSEC-124 or GCIS-123 or GCIS-124) or equivalent courses.) Lecture 3 (Fall, Spring, Summer).

   Honors Introduction to Computer Science Theory

This course provides a challenging introduction to the theory of computation with an emphasis on problem solving. syllabus include formal languages, grammars, auto-mata theory, computability, and complexity. (Prerequisites: (MATH-190 or MATH-200) and (CSCI-140 or CSCI-141 or CSCI-242 or SWEN-123 or SWEN-124 or CSECI-123 or CSEC-124 or GCIS-123 or GCIS-124) or equivalent courses.) Lecture 3 (Fall).

General Education – Elective: Linear Algebra

This course is an introduction to the basic concepts of linear algebra, and techniques of matrix manipulation. syllabus include linear transformations, Gaussian elimination, matrix arithmetic, determinants, vector spaces, linear independence, basis, null space, row space, and column space of a matrix, eigenvalues, eigenvectors, change of basis, similarity and diagonalization. Various applications are studied throughout the course. (Prerequisites: MATH-190 or MATH-200 or MATH-219 or MATH-220 or MATH-221 or MATH-221H or equivalent course.) Lecture 3 (Fall, Spring).

General Education – Elective: Probability and Statistics I

This course introduces trial spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH-173 or MATH-182 or MATH 182A or equivalent course.) Lecture 3 (Fall, Spring, Summer).

3

   Analysis of Algorithms

This course provides an introduction to the design and analysis of algorithms. It covers a variety of classical algorithms and data structures and their complexity and will equip students with the intellectual tools to design, analyze, implement, and evaluate their own algorithms. (Prerequisites: (CSCI-243 or SWEN-262) and (MATH-190 or MATH-200) or equivalent courses.) Lecture 3 (Fall, Spring).

   Honors Analysis of Algorithms

This course provides a challenging introduction to the design and analysis of algorithms with an emphasis on problem solving and algorithmic research. It covers a variety of classical algorithms and data structures and their complexity, as well as deeper coverage of more advanced material; for example, linear programming, approximation algorithms, and randomized algorithms. The course will equip students with the intellectual tools to design, analyze, implement, and evaluate their own algorithms. (Prerequisites: (CSCI-243 or SWEN-262) and (MATH-190 or MATH-200) or equivalent courses.) Lecture 3 (Fall).

Introduction to Software Engineering

An introductory course in software engineering, emphasizing the organizational aspects of software development and software design and implementation by individuals and small teams within a process/product framework. syllabus include the software lifecycle, software design, user interface issues, specification and implementation of components, assessing design quality, design reviews and code inspections, software testing, basic support tools, technical communications and system documentation, team-based development. A term-long, team-based project done in a studio format is used to reinforce concepts presented in class. (Prerequisite: CSCI-140 or CSCI-142 or CSCI-242 or SWEN-124 or CSEC-124 or GCIS-124 or equivalent course.) Lec/Lab 3 (Fall, Spring).

General Education – Elective: Lab Science II†

General Education – Elective

General Education – Natural Science Perspective: Lab Science I‡

General Education – Scientific Principles Perspective†

Concepts of Computer Systems

An introduction to the hardware and software organization of computer systems. The course emphasizes a multilevel model of computer organization. syllabus include the digital logic level; the micro architecture level; the machine instruction set level; the operating system level; and the assembly language level. Programming assignments will be required. (Prerequisites: (CSCI-243 or 4003-334) and (MATH-190 or MATH-200 or 1016-366) or equivalent courses.) Lecture 3 (Fall, Spring, Summer).

Principles of Data Management

This course provides a broad introduction to the principles and practice of modern data management, with an emphasis on the relational database model. syllabus in relational database systems include data modeling; the relational model; relational algebra; Structured Query Language (SQL); and data quality, transactions, integrity and security. Students will also learn approaches to building relational database application programs. Additional syllabus include object-oriented and object-relational databases; semi-structured databases (such as XML); and information retrieval. A database project is required. (Prerequisites: (MATH-190 or MATH-200) and (CSCI-140 or CSCI-142 or CSCI-242 or SWEN-124 or CSEC-124 or GCIS-124) or equivalent courses.) Lecture 3 (Fall, Spring, Summer).

Introduction to Artificial Intelligence

An introduction to the theories and algorithms used to create artificial intelligence (AI) systems. syllabus include search algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. Programming assignments are an integral part of the course. (Prerequisites: (CSCI-243 or SWEN-262) and (MATH-251 or STAT-205) or equivalent courses. Students cannot take and receive credit for this course if they have taken CSCI-630.) Lecture 3 (Fall, Spring, Summer).

Computer Science Undergraduate Co-op (spring)

Students perform professional work related to Computer Science for which they are paid. Students work full time during the term for which they are registered. Students must complete a student co-op work report for each term for which they are registered; students are also evaluated each term by their employer. A satisfactory grade is given for co-op when both a completed student co-op work report and a completed, corresponding employer evaluation are received and when both documents are generally consistent. (Enrollment in this course requires permission from the department offering the course.) CO OP (Fall, Spring).

General Education – Science Elective‡

General Education – Immersion 1 (WI)

Concepts of Parallel and Distributed Systems

This course is an introduction to the organization and programming of systems comprising multiple computers. syllabus include the organization of multi-core computers, parallel computer clusters, computing grids, client-server systems, and peer-to-peer systems; computer networks and network protocols; network security; multi-threaded programming; and network programming. Programming projects will be required. (Prerequisites: CSCI-243 or SWEN-262 or equivalent courses.) Lecture 3 (Fall, Spring).

Programming Language Concepts

This course is a study of the syntax and semantics of a diverse set of high-level programming languages. The languages chosen are compared and contrasted in order to demonstrate general principles of programming language design and implementation. The course emphasizes the concepts underpinning modern languages rather than the mastery of particular language details. Programming projects will be required. (Prerequisites: CSCI-243 or SWEN-250 or IGME-309 or 4003-334 or 4010-361 or 4080-487) and (MATH-190 or MATH-200) or equivalent courses.) Lecture 3 (Fall, Spring, Summer).

Professional Communications (WI-PR)

This course focuses on developing and improving verbal and written communication skills specific to the discipline of computer science. syllabus include the different forms of writing in computer science (books, theses, journal articles, technical reports, manuscripts, etc.), writing styles of computer scientists, document readability and usability, documents for career readiness, effective presentations, teamwork and peer review, research methods, experimentation, documenting mathematics and algorithms, proper formatting of graphs, figures, and tables, and ethical, social, and professional issues facing Computer Scientists. This course is approved as Writing Intensive. (This class is restricted to students with at least 4th year standing COMPSCI-BS or COMPSCI-2M) Lecture 3 (Fall, Spring, Summer).

Collaborative Software Development 

This course covers processes, tools, and techniques for software development, in general, and collaborative, distributed software development, in particular. Students will learn how to design a process specific to their organization and development project needs. This includes how to select a software development life-cycle model, how to select and sequence the development and management activities of a collaborative, distributed software development team structure and dynamics, and how to define the work products, tools, and methods used to perform those activities. The Software Process Engineering Metamodel (SPEM, an Object Management Group standard) will serve to graphically describe, analyze, discuss, and Boost software development processes. Special attention will be given to collaboration needs and approaches for small and large teams that may be globally distributed. (Prerequisites: This course is restricted to students with graduate standing in Software Engineering program or GCCIS graduate programs who have completed SWEN-601 and SWEN-610 or equivalent courses.) Lecture 3 (Fall).

Model-Driven Development 

Software models help the software engineer to understand, specify, and analyze software requirements, designs, and implementations (code components, databases, support files, etc.). Model-driven development is a software engineering practice that uses tool-enabled transformation of requirements models to design models and then to code and associated implementation artifacts. Students will use the Unified Modeling Language (UML) and other modeling techniques to capture software requirements, designs, and implementations. Students will also use formal modeling methods to semi-automatically transform among the various models and to study the quality attributes of the modeled software, such as performance, reliability, security, and other qualities. (Co-requisites: SWEN-601 and SWEN-610 or equivalent courses.) Lecture 3 (Fall).

General Education – Immersion 2

Computer Science Electives

General Education – Science Elective‡

Computer Science Undergraduate Co-op (fall)

Students perform professional work related to Computer Science for which they are paid. Students work full time during the term for which they are registered. Students must complete a student co-op work report for each term for which they are registered; students are also evaluated each term by their employer. A satisfactory grade is given for co-op when both a completed student co-op work report and a completed, corresponding employer evaluation are received and when both documents are generally consistent. (Enrollment in this course requires permission from the department offering the course.) CO OP (Fall, Spring).

Computer Science Elective

General Education – Immersion 3

General Education – Elective

Open Electives

Research Methods

Overview of the academic research methodologies used in graduate level work. syllabus include: Writing style, Audience analysis, Research Planning, Experiment design and result analysis, Document structure, Research validation, and the process for submission and review to conferences and journals. In this course the student will identify and develop a detailed thesis or capstone proposal that may be continued in a subsequent course. An in-depth study of a software engineering course will be research focused. The student selects a research problem, conducts background research, and selects appropriate technology and methodologies needed to fully conduct the project. The course is selected by the student and is in agreement with the student’s advisor and committee. The proposal is presented in a scholarly format for approval by the advisor and committee. (Graduate Computing and Information Sciences) Lecture 3 (Spring).

Software Architecture

A system’s software architecture is the first technical artifact that illustrates a proposed solution to a stated problem. For all but the simplest system, the achievement of qualities such as flexibility, modifiability, security, and reliability is critically dependent on the components and interactions defined by the architecture. The course focuses on the definition of architectural structures, the analysis of architectures in terms of trade-offs among conflicting constraints, the documentation of architecture for use over a product’s life cycle, and the role of architecture during coding activities. (Prerequisites: SWEN-601 and SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall).

Software Quality Assurance

This course explores the concepts of process and product quality assurance and introduces approaches and support tools used to extract the information needed to assess and evaluate the quality of existing software systems. Major maintenance activities are detailed including unit and regression testing, test case generation, software refactoring, API migrations, bug localization and triage, and predicting technical debt. Students will participate in an active learning approach by exercising and practicing code reviews, software testing tools, and quality frameworks. (Prerequisites: SWEN-601 and SWEN-610 or equivalent courses.) Lecture 3 (Spring).

Independent Study

This course provides the graduate student an opportunity to explore an aspect of software engineering in depth, under the direction of an adviser. The student selects a topic, conducts background research, develops the system, analyses results, and disseminates the project work. The report explains the topic/problem, the student's approach and the results. (Completion of 9 semester hours is needed for enrollment) (Enrollment in this course requires permission from the department offering the course.) Ind Study (Fall, Spring, Summer).

Graduate Elective

Thesis

This course provides the student with an opportunity to execute a thesis project, analyze and document the project in thesis document form. An in-depth study of a software engineering course will be research focused, having built upon the thesis proposal developed prior to this course. The student is advised by their primary faculty adviser and committee. The thesis and thesis defense is presented for approval by the thesis adviser and committee. (Enrollment requires completion of all core courses and permission from the department offering the course.) Thesis 6 (Fall, Spring, Summer).

Graduate Elective

150

In UMass Lowell's B.S. in Electrical Engineering, you will learn the fundamentals of electrical engineering beginning with basic mathematics and science, followed by their application to courses in engineering science and engineering design. 

Courses in engineering science and design provide a balanced view of hardware, software, application tradeoffs, basic modeling techniques and the use of computer-aided design tools. 

You will also take courses in the humanities and social sciences that help broaden your understanding of the role that non-technical knowledge plays in determining a high level of professional responsibility.

An important aspect of our curriculum is the senior-year technical elective program intended to broaden or deepen technical knowledge according to students’ interests and competencies. In addition, the project-based capstone experience challenges students to develop a custom-designed product for a client with a disability through the Assistive Technology Program.

Accredited by ABET, our hands-on program emphasizes experimental science and technology through investigative laboratory work and classroom lectures and demonstrations.

Visit the Academic Catalog for a complete course listing and to learn about the Electrical Engineering/Computer Science Double Major and Electrical Engineering/Physics Double Major.

Tech layoffs may take a toll on the financial outlook of this year's crop of college graduates, with a new survey predicting that 2023 computer science majors will earn salaries 4% below a year ago. Yet communications and humanities majors are likely to see healthy boosts in starting salaries, the study found.

Major tech companies such as Microsoft, Alphabet and Meta have laid off tens of thousands of workers in exact weeks, reversing a hiring spree that surged during the pandemic as millions of Americans worked remotely and demand jumped for digital products.

But a slowing economy is putting pressure on the industry to trim expenses, which in turn could translate into lower salaries for 2023 grads with computer science degrees, according to the new study from the National Association of Colleges and Employers, which surveyed 170 employers about their salary plans. Despite the decline, computer science grads will still enjoy a relatively high starting salary, at almost $73,000.

The tech layoffs "are most likely the cause" of the expectations for lower salaries for CS majors, said Andrea Koncz, senior research manager at the National Association of Colleges and Employers.

It's unclear whether the impact will be short-lived, but the issue could echo throughout new grads' careers — especially if the U.S. dips into a recession, as some economists predict — given that entering the workforce during a downturn can crimp a worker's earnings for up to a decade, research has found.

But, Koncz added, "Salaries to new college graduates appear to be stable and there are increases for the Class of 2023 graduates in all but two areas of study compared to the Class of 2022." 

Social science grads are expected to receive pay that is 1.7% lower than a year earlier, the study found. The previous time employers expected to pay computer science majors less was in 2020, although that was a dip of 0.2%, Koncz added.

On the other hand, grads with humanities degrees will enjoy a salary boost of 4.5%, while communications majors will receive a 4.8% increase, the survey found. 

"Humanities and communications majors are most likely keeping up with the typical increases seen in new graduate salaries," Koncz said. 

Even with those bumps, these majors are likely to earn far less than computer science graduates, the study found. For instance, starting salaries for humanities majors — such as philosophy, history, literature and the arts — are likely to be about $53,000 this year, or almost $20,000 less than CS majors.

The study found that more than half of employers intend to increase salaries for new college graduates, with the typical increase between 3% to 5%. 

Program Educational Objectives are defined as the expected accomplishments of graduates of the program in first few years after graduation. Graduates of the BSE Computer or Electrical Engineering program at the University of Massachusetts at Lowell will be able to:

Be established and recognized as a valued professional and effective communicator in industries related to electrical, computer and electronic technologies. 

Practice their profession in a collaborative, team-oriented manner that embraces the multidisciplinary and multicultural environment of today’s business world. 

Engage in lifelong learning and professional development via post graduate education and participation in professional organizations. 

Function as a responsible member of society with willingness to mentor fellow employees and an understanding of the ethical, social and economic impact of their work in a global context. 

The student outcomes for the BSE degree in electrical or computer engineering at UMass Lowell are as follows. At graduation students should: 

• A strong grounding in the fundamentals including the ability to formulate and solve engineering problems by applying the principles of mathematics, science and electrical & computer engineering. 
• Ability to analyze and synthesize engineering problems including design and conduct experiments, use standard test equipment and interpret experimental data. 
• Ability to design reliable systems, devices or processes from initial specifications to a deliverable system. 
• Ability to work in a multidisciplinary team environment. Ability to communicate effectively in both verbal and written forms. 
• Ability to appreciate the complexities of professional environments, including taking responsibility for oneself, working effectively and professionally as a team member, and being mindful of ethical, economic, and contemporary concerns. 
• Competence in taking the initiative for one's own professional development and recognition of the need and ability in engaging in post graduate education and lifelong continual learning. 
• Ability to independently accomplish engineering tasks. 
• Ability to enter industry with the engineering techniques, skills, and tools required to be able to solve real-world problems in electrical and computer engineering.

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Electrical and computer engineering has an enormously successful history of revolutionizing our lives through inventions such as computers, cell phones, and digital cameras. Northwestern is building on this history by conducting groundbreaking research and preparing the next generation of engineers to address global challenges: by developing new wearable systems to Boost health care, designing the future smart grid to provide greener energy, or integrating communications and computation to enable intelligent transportation systems. 

Our Department of Electrical and Computer Engineering prides itself on both research and teaching. We create a collaborative environment in which interdisciplinary work is encouraged and students develop close relationships with faculty and get involved in cutting-edge research. Areas of focus include computational imaging, computer architecture, embedded systems, information theory, machine learning and signal processing, physical electronics, photonics, quantum devices, VLSI, and wireless communication.