Courses

The course serves as an introduction to the exciting field and vocation of engineering and the value of engineering education. This course includes guest lectures describing various engineering disciplines, team-based introductory projects, community service, and weekly readings that focus on how to be successful as an engineering student. The weekly readings include topics on self-discovery, goal setting, work-life balance, and understanding the teaching/learning process. A course emphasis includes Christian perspectives on purpose, integrity, and service as they relate to the vocation of engineering. This is the first course required of all students considering engineering as a major. Note: This is a required course for Engineering, Computer Science, and Construction Management Majors. Pre- or Co- Requisite: ICS 105. (3 units; Fall)

Introduction to fundamental techniques used in engineering design and analysis. Different models of the design process will be examined. A collaborative team oriented design project will be undertaken. (4 units; Spring)

Engineering is a discipline which requires the effective communication of visual information as part of persuasion or education. Excel (beginning and advanced techniques), and a CAD program will be covered to assist in that process for a real current engineering problem of interest. For example this might include the utilization of solar power in Riverside County to address energy consumption concerns. This course lays the foundation for future courses which have elements of data and information presentation. (3 units; Fall/Spring)

This course addresses the basic elements of the Christian faith in the context of participating in Gods global agenda. Topics include Christian worldview, the Kingdom of God, the gospel of Jesus Christ, the work of the Holy Spirit, the mission of the Church, and the role of prayer. (3 units; Fall/Spring)

Preparation for a lifetime of leadership as an engineer. Small group discussion format, with opportunities for student facilitated discussions. Topics include: leadership in organizations, emotional intelligence, the psychology of small group dynamics and team performance, global perspectives of engineering. Written executive summaries as part of a “4MAT” like response will be required prior to discussion. Prerequisite: Permission of the Department Chair. (1 unit; Spring)

Designed to prepare you for the official internship during your junior summer. Discussion and development of the individual's priorities for their learning contract. Topics include: resume and internship writing, finding an internship, how you will be assessed as an intern, the psychology of the workplace, different types of bosses and working on teams, and the different types of work environment. Junior status or greater is required. (1 unit; Fall)

The first of a two course senior capstone design sequence. Various design topics may be discussed including equipment design, the design of process systems, and economics. Student teams select a project which may involve company sponsorship, and proceed through the design methodology introduced in earlier design classes, incorporating appropriate engineering standards and multiple realistic constraints. Every project has a 'customer' which requires the generation of a customer spec. During the sequence students provide detailed schedules for building a prototype system or designing a process and present weekly progress reports. They also produce technical specifications, undergo a preliminary design review (PDR), and build a working prototype system if appropriate. Prerequisite: EGR 302 or 352. (3 units; Fall)

A continuation of EGR 401. Further development of the project will take place and will involve testing as appropriate. Teams will develop proper documentation for their projects and will appropriately communicate and present the results of their project. A final presentation is given to the public which could include members of the engineering advisory council. The presentation will be recorded and included as a part of students' senior portfolios. Prerequisite: EGR 401. (3 units; Spring)

Students will report on their internship experiences. Each student will submit a written report and give an oral presentation. Students will receive faculty and peer evaluations of their presentations. Prerequisite: Permission of the Department Chair. (1 unit; Fall/Spring)

Introduction to computer science. Covers problem solving methods and algorithm development; modern programming methodologies; and fundamentals of high-level block structured language using Python. (3 units; Fall/Spring)

Overview of the software development process. Includes requirements, design, construction, and testing of software. Software project planning. Analysis, architecture, and design of software systems using UML. Evaluating designs. Implementing designs using appropriate data structures, frameworks, and APIs. Prerequisite: EGR 120 or 121. (3 units; Fall/Spring/Summer)

Fundamental data structures for implementation and analysis. Techniques for solving problems by programming. Analysis for complexity and performance trade-offs. Topics include object oriented design, debugging, abstract data types (ADTs), recursion, big-O notation, interfaces, inheritance, and encapsulation, linked lists, stacks, queues, hash tables, heap, trees, searching, sorting. Prerequisite: EGR 222. (3 units; Fall/Spring)

Algorithms are the heart of any computer software; they define the procedure for accomplishing a data-intensive task. This course establishes terms and methods for talking about algorithms, examines some of the basic algorithms for sorting, searching and other fundamental tasks, and explores more advanced algorithms in bioinformatics and other fields. Prerequisite: EGR 227. (3 units; Fall/Spring)

This course will provide an overview of the salient math topics most heavily used in the core sophomore-level engineering courses. These include trigonometry, vectors, complex numbers, sinusoids and harmonic signals, systems of equations and matrices, derivatives, integrals, differential equations and Fourier series within the context of an engineering application. These concepts will be reinforced through extensive examples of their use in the core engineering curriculum. Students may only earn credit for either EGR 182 or EGR 182L. Prerequisite: EGR 181 or MAT 115. (4 units; Fall/Spring)

Introduces the foundations of discrete mathematics as they apply to computer science, focusing on providing a solid theoretical foundation for further work. Topics include functions, relations, sets, simple proof techniques, Boolean algebra, propositional logic, digital logic, elementary number theory, and the fundamentals of counting. (3.0 units; Fall)

Basic concepts of analytical geometry, limits and derivatives, differentials and rates, integration, definite and indefinite integrals, differentiation of logarithmic and exponential functions. Prerequisites: MAT 135, 145, EGR 182, or sufficient SAT, ACT or math placement exam scores and appropriate high school mathematics background. (4 units; Fall/Spring)

Continued study and applications of integration: volumes, lengths, surface of revolution; derivatives and integrals involving trigonometric functions, infinite series, expansion of functions, hyperbolic functions, law of the mean, partial fractions, polar coordinates, and conic sections. Prerequisite: MAT 245. (4 units; Fall/Spring)

Vector spaces; matrices, rank and systems of linear equations; linear transformation; similarity and diagonalization theorems; eigenvectors and eigenvalues. Prerequisite: MAT 313, or 245 and EGR 225. (3 units; Spring)

This course represents a basic concepts and methodology course in regression analysis using application of general linear regression models to real-life situations. Case studies are used to give practice in diagnosing practical problems, deciding on appropriate models, and knowing which inferential technique will answer the researcher’s questions for the purposes of description and prediction. Regression models and model building typical of problems used in the social and behavioral sciences, the natural and health sciences, and many other disciplines are covered. Prerequisite: STA 144 or MAT 245. (3 units; Spring, even years)

The first semester of a two-semester course providing a systematic development of the theories of probability and statistics. Students learn and use fundamental concepts of probability models, random variables and their distributions, reduction of data, estimation, testing of hypotheses, univariate normal inference, and statistical decision theory. The first semester is required for BA and BS statistics majors of all concentrations. Prerequisites: MAT 245, and one of the following: EGR 120, 121, or STA 144. (3 units; Fall)

Second semester course in a systematic development of the theories of probability and statistics. Topics include analysis of categorical data, multivariate distributions, nonparametric inference, linear models and analysis of variance. As time permits, the theory underlying Markov chain, Monte Carlo, quasi-likelihood, empirical likelihood, statistical functionals, generalized estimating equations, the jackknife, and the bootstrap are addressed. Prerequisite: STA 310. (3 units; Spring)

This course provides the tools to create and critically evaluate data visualizations. Focus will be on statistical graphics, graphics that display statistical data. Additionally, recent advances in the field of information visualization will be covered. Prerequisite: STA 144 or MAT 245. (3 units; Spring, even years)

A course to introduce statistical models of advanced least squares regression and standard ANOVA techniques and extensions to categorical data. Students will conceptually understand linear mixed effects models, log linear and generalized linear models for count data; and survival models for the analysis of lifetime data. In addition, students will apply these models to real data, to discern patterns and conclusions, and present their results. Prerequisite: STA 210, EGR 120, or 121. (3 units; Fall, even years)

A course in application of parametric and nonparametric single and multivariable data analytic techniques to sports. Topics will include: linear regression, correlation, confounding and interactions, variable selection, categorical predictors and outcomes, logistic regression, factor analysis, discriminant analysis, and regression techniques with longitudinal data. Prerequisite: STA 210, EGR 120, or 121. (3 units; Spring, odd years)

Delve into the essential principles of cutting-edge Geographic Information Systems (GIS) to grasp its theoretical underpinnings and its widespread application across diverse industries. Over the next few decades, the utilization of GIS is expected to experience rapid expansion in fields spanning the arts and sciences, business, engineering, health sciences, nursing, and more. The demand for a foundational skill set in GIS is pressing, and studying GIS and its functionalities aligns perfectly with this demand. Gain insights into GIS terminology and acquire hands-on experience in map design, manipulation of spatial data, and spatial analysis as innovative means to bolster global initiatives. (3 units; Fall)

Elevate organizational strategic thinking by addressing intricate challenges through the application of established data and cutting-edge Geographic Information System (GIS) solutions. Organizations are increasingly recognizing the benefits of transitioning from the data collection and information era to the age of knowledge engineering. An examination of key GIS tools offers an in-depth, intermediate exploration of harnessing organizational data for theoretical, practical, and problem-solving purposes. This culminates in a robust proficiency in the application of GIS across diverse domains, precisely when the demand for business leaders to develop, execute, and manage advanced GIS solutions is on the rise. Prerequisite: CSC 441. (3 units; Spring)

Explore complex spatial data ecosystems and their impact on Geographic Information System (GIS) solutions. A thorough GIS education, training, and practical experience serves as the foundation for both the theoretical understanding and practical proficiency in crafting GIS solutions for diverse fields and organizational settings. Innovative and collaborative geographic information tools aimed at spatial data analysis will be seamlessly blended with cutting-edge data science theory and capabilities. Proficiency in applying GIS principles through a data science perspective will be assessed through a series of lab assignments and a concluding research project. Prerequisite: CSC 442. (3 units; Fall)

Investigation of modern cloud computing platforms and the practice of creating scalable software solutions that utilize essential cloud computing products like serverless computing, serverless key-value NOSQL databases, cloud storage, publisher/subscriber messaging, identity access and permissions management, machine learning (ML) integration, etc. Prerequisite: EGR 226. (3 units; Spring)

Introduces the fundamentals of operating systems including processes, memory, scheduling, input/output together with the basics of networking protocols. Prerequisite: EGR 222. (3 units; Spring)

Team design of industrial or self-designed projects. Requires the design and development of a process or product with oral and written reports. Includes a review and analysis of professional papers. Pre- or Co- Requisites: EGR 102 and one of the following: CSC 321, 322, or EGR 305, or both CSC 221 and EGR 325. (3 units; Spring)

Overview of current database technologies with an emphasis on relational database technology. Introduction to database design, entity relationship diagraming, structured query language, and stored procedures. Prerequisite: EGR 120 or 121. (3 units; Fall)

In the present-day data-centric landscape, the skill of extracting valuable insights from unprocessed and unstructured data is foundational and in high demand. This course provides students with the essential knowledge and practical expertise required to convert disorganized data into a structured and analytically valuable format. It is intended for individuals entering the fields of data science and data analysis or for those students looking to enhance their proficiency in systematic and reproducible data collection, manipulation, and preparation. Prerequisite: EGR 121. (3 units; Fall)

This course covers introductory machine learning topics including supervised and unsupervised learning, linear and logistic regression, support vector machines, neural networks (MLPs, CNNs, RNNs, GANs) and more. Coursework includes instruction and programming assignments in algorithmic implementations and high-level library usage. Students also apply machine learning techniques to a unique research project. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistic course. (3 units; Fall)

The course commences with an examination of the knowledge discovery process. In particular, the introduction equips students with the strategic thinking skills essential to focus on cutting edge data mining techniques that can be applied in a wide variety of settings, e.g., business, engineering, health care, science, etc. Traditional topics include data mining algorithms and implementation issues, advantages and disadvantages of data mining, and examples of knowledge engineering. Current topics such as ubiquitous, distributed, and spatiotemporal geographic data mining will also be explored. This is a practical hands-on course that culminates in a real-world project implemented via open source tools. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistics course. (3 units; Fall)

Natural language is ubiquitous, e.g., humans speak and write to communicate, to transfer information, and to document knowledge. Natural Language Processing (NLP) is an integral component in countless information systems requiring advanced manipulation of natural language. In this class, students will be introduced to NLP starting with the concept of understanding words in context and the need for natural language processing in the business environment. The discussion continues with a detailed study of words and is a foundational framework supporting phonetics and speech synthesis. Subsequent topics include concepts of how words are grouped together to form unique grammatical units. The last part of the course, explores solving real-world NLP problems and deals with two key areas: corpus building, feature engineering, and application development. Course material is presented via theory-based lectures, group discussion, and practical labs-a culminating research project will be individually crafted. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistics course. (3 units; Spring)