Advising Reference
A collection of links I find useful when advising students.
Majors and Programs
- Computer Science: BCS, CS Plus, and CS Minor (same page as CS Plus)
- Data Science major and minor
Consider adding one of the following to your program:
- Cybersecurity Certificate
- For CS students, adding a Data Science minor or a Statistics minor may only require one or two additional courses.
- For Data Science students, consider a Computer Science minor.
- Consider the Information Systems minor or the Entrepreneurship minor
- If you’re adding a minor or second major, you might also consider the Collegiate Scholars program.
Program-Specific Advising Resources
- CS Advising, including the annual schedule
- Math / Statistics Advising homepage:
Core Curriculum
See my Core Advising tool
Student Resources
Spring 2027 Courses
Each course is listed once with all of its open sections. For courses with a lab, register for one lecture section and one lab section; the section letters don’t need to match. Courses marked 1st half or 2nd half meet for only half the semester. Click “Description” to see the catalog description. The calendar shows when everything meets; click a block to jump to that course.
Weekly Calendar
Computer Science (CS)
CS 108 Introduction to Computing
DescriptionAn introduction to computing as a problem-solving discipline. A primary emphasis is on programming as a methodology for problem solving, including: the precise specification of a problem, the design of its solution, the encoding of that solution, and the testing, debugging and maintenance of programs. A secondary emphasis is the discussion of topics from the breadth of computing including historical, theoretical, ethical and biblical perspectives on computing as a discipline. Laboratory. Lab fee - see catalog for details. |
Lecture
Lab: pick any one
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CS 112 Introduction to Data Structures
DescriptionA continuation of CS 108, CS 106, or CS 104, using C++ classes to introduce and implement the elementary data structures including lists, stacks, queues and trees. Advanced programming techniques such as indirection, inheritance, and templates are introduced, along with an emphasis on algorithm analysis, efficiency, and good programming style. Laboratory. Lab fee - see catalog for details. |
Lecture: pick one
Lab: pick any one
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CS 195 Introductory Computing Seminar
DescriptionThis seminar explores a range of current topics in computing, including topics in research and practice. Students intending to major in a computing-related field must take this course three times in their freshman and sophomore years. |
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CS 214 Programming Language Concepts
DescriptionDesign principles and implementation issues of contemporary programming languages. Topics covered include programming paradigms; the syntax and semantics of programming language constructs, including those that support concurrent, parallel, and distributed processing; translation of high-level languages to machine language; formal languages and the automata that recognize them. Several different languages are introduced and examined to illustrate these topics. Lab fee - see catalog for details. |
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CS 232 Operating Systems and Networking
DescriptionAn introduction to the major concepts modern operating systems must address, e.g., processes and threads, inter-process communication and synchronization, scheduling, main and secondary memory management, and file systems. An introduction to computer networking with emphasis on the TCP/IP protocol suite, including network security issues. Lab fee - see catalog for details. |
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CS 295 Computing Seminar
DescriptionThis seminar explores a range of current topics in computing, including topics in research and practice. It is a continuation of CS 195. Department majors must take this course three times during their junior and senior years. |
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CS 300 Special Topics: AI-Enabled Software
DescriptionSpecial Topic: In this course, students work in teams to build a real AI-enabled web application, navigating the entire software lifecycle: from a project brief through specification, design, AI-assisted development, and deployment to a working, usable product. Every project is developed in the open on GitHub and released under an open-source license, giving each student a substantive, real-world artifact to show a future employer. The course combines short lectures, hands-on labs, and team project work. Advanced study of selected topics of current interest in computer science. Topics vary by year. Consult the instructor or the department website for the specific topic the current offering. This course may be repeated for credit if the special topics differ. Lab fee - see catalog for details. |
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CS 326 Embedded Systems and the Internet of Things
DescriptionAn introduction to topics in embedded systems and the Internet of Things (IoT) including hardware and software considerations for special-purpose computing applications that interact with the physical world. Hardware topics include microcontrollers, I/O interfacing, sensors, and actuators. Software topics include scheduling and real-time considerations, IoT network protocols, the Web of things, and embedded programming. Additional topics include discussions of related social and ethical issues such as security, privacy, reliability, and the impact of automation. Lectures will be combined with hands-on lab exercises and a final project. Lab fee - see catalog for details. |
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CS 338 System Administration: Infrastructure
DescriptionThis course provides a holistic introduction to the support of organizational information technology computing and networking infrastructure. It covers computer hardware and servers, designing and maintaining networks, virtualization, and performing basic security configuration. Students work with open source and commercial platforms. Students apply these skills through hands-on system implementation labs and projects. Lab fee - see catalog for details. |
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CS 339 System Administration: Cloud Services
DescriptionThis course provides a holistic introduction to the support of organizational information technology cloud computing and services. It covers cloud computing platforms, services, and their use in business information technology administration. Students work with open source and commercial platforms. Students apply these skills through multiple system implementation labs and team projects. |
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CS 363 Network Security
DescriptionThis course provides an in-depth understanding of the major security issues concerning the Internet infrastructure, including, but not limited to, domain name system (DNS), transport layer, Internet Protocol (IP) layer, and the Transport Layer Security (TLS). In particular, a secure way of applying symmetric encryption algorithms, cryptographic hashing, and asymmetric encryption algorithm to network protocols will be demonstrated through the TLS protocols. This course will emphasize both principles and practices. Lab fee. |
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CS 375 Machine Learning
DescriptionAn introduction to artificial intelligence systems using machine learning, with an emphasis on deep neural networks. Students learn how neural networks compute and are trained, how to train them, and how to apply them to tasks such as image classification. The course emphasizes hands-on implementation, evaluation of system performance, and discernment of philosophical, societal, historical, and religious aspects of AI systems. Lab fee. |
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CS 376 Advanced Machine Learning
DescriptionAn in-depth study of modern generative AI systems, with an emphasis on large language models (LLMs). Students examine computational architectures, learn practical techniques for building and evaluating ML-powered applications, and discern the trajectory and implications of the machine learning approach to AI. As a continuation of CS 375, the course emphasizes hands-on implementation, evaluation of system performance, and discernment of philosophical, societal, historical, and religious aspects of AI systems. Lab fee. |
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CS 384 Perspectives on Computing
DescriptionThis course addresses social, ethical, legal and professional issues that arise in computer science from a reformed, Christian perspective. Social issues concerning the computerization of society include privacy, security, the digital divide and changes in the way people receive information and relate with others. Ethical discussion starts with a survey of ethical theories and covers professional, ethical and legal issues in areas including intellectual property, privacy, liability and professional codes of conduct. In addition, some foundational issues are covered, including materialist vs. Christian view of what it means to be a person. |
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CS 398 Senior Project in Computing II
DescriptionA continuation of CS 396. The student will submit regular progress reports to a supervising faculty member and submit a final report for evaluation by a departmental committee. |
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Data Science (DATA)
DATA 102 Foundations of Data Science and Analytics
DescriptionAn integrated view of the use of desktop applications to access, manage and analyze data typically found in a corporate environment. This includes spreadsheet and database technologies. Students also investigate the ethical considerations of data availability and analysis along with career options in the fields of information technology and data science. |
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DATA 304 Visualizing Data and Models
DescriptionAn introduction to principles and applications for visualizing data and models. Students will learn how to design static and interactive visualizations that accurately, effectively, and aesthetically display information about data and models. Visualizations will be created using multiple visualization tools. |
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DATA 385 Survey Data Analysis
Cross-listed as MATH 385 and STAT 385
DescriptionAdvanced study of selected topics in data science. Consult instructor for course information. This course may be repeated for credit provided the topic is different. |
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DATA 398 Senior Project in Data Science II
DescriptionA continuation of DATA 396. The student will submit regular progress reports to a faculty mentor and submit a final report for evaluation by a departmental committee. |
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Statistics (STAT)
STAT 143 Introduction to Probability and Statistics
DescriptionAn introduction to the concepts and methods of probability and statistics. The course is designed for students interested in the application of probability and statistics in business, economics, and the social and life sciences. Topics include descriptive statistics, probability theory, random variables and probability distributions, sampling distributions, point and interval estimation, hypothesis testing, analysis of variance, and correlation and regression. Students may not receive credit for both STAT 143 and STAT 145. |
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STAT 145 Biostatistics
DescriptionAn introduction to the concepts and methods of probability and statistics for students in life science programs. Topics include descriptive statistics, probability theory, random variables and probability distributions, experimental design, sampling distributions, confidence intervals and hypothesis tests, analysis of variance, and correlation and regression. This course is an alternative to STAT 143 for students in certain life science programs. It is a required course for biology and public health majors and is open to others. Students may not receive credit for both STAT 143 and STAT 145. |
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STAT 241 Engineering Statistics
DescriptionA course in statistics with emphasis on the collection and analysis of data in engineering contexts. Topics include descriptive statistics, experimental design, and inferential statistics. The development of probabilistic models for describing engineering phenomena is emphasized. Statistical software will be used throughout the course. |
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STAT 243 Statistics
DescriptionData analysis, data collection, random sampling, experimental design, descriptive statistics, probability, random variables and standard distributions, Central Limit Theorem, statistical inference, hypothesis tests, point and interval estimates, simple linear regression. Examples will be chosen from a variety of disciplines. Computer software will be used to display, analyze and simulate data. |
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STAT 341 Computational Bayesian Statistics
DescriptionAn introduction to Bayesian statistical methods. Conjugate prior distributions are studied, but computational methods are emphasized and developed in the context of practical data analysis methods. |
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STAT 385 Survey Data Analysis
Cross-listed as DATA 385 and MATH 385
DescriptionAdvanced study of selected topics in statistics. Consult instructor for course information. This course may be repeated for credit provided the topic is different. |
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Mathematics (MATH)
MATH 132 Calculus for Management, Life, and Social Sciences
DescriptionFunctions, limits, and derivatives. Applications of derivatives to maximum-minimum problems, exponential and logarithmic functions, integrals, and functions of several variables. Not open to those who have completed MATH 171. |
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MATH 161 Calculus: Modeling and Computation
DescriptionThis course serves as an introduction to calculus. Topics include functions, limits, derivatives, applications of derivatives, integrals, and applications of integrals. The emphasis is mathematical modeling and using calculus to understand the models. Historical and philosophical aspects are integrated with the development of the mathematical ideas, providing a sense of the context in which calculus was developed. |
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MATH 162 Differential and Integral Calculus
DescriptionDifferentiation, rules of differentiation, applications of differentiation, integration, techniques of integration, applications of integration, infinite sequences and series, Taylor series. |
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MATH 163 Calculus: Modeling, Computation, Techniques of Integration
DescriptionThis course serves as an introduction to calculus. Topics include functions, limits, derivatives, applications of derivatives, integrals, applications of integrals, and techniques of integration. The emphasis is mathematical modeling and using calculus to understand the models. Historical and philosophical aspects are integrated with the development of the mathematical ideas, providing a sense of the context in which calculus was developed. |
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MATH 221 The Real Number System and Methods for Elementary School Teachers
DescriptionThis course provides prospective elementary school teachers with background needed for teaching elementary mathematics. Both content and methodology relevant to school mathematics are considered. Topics covered include the real number system and its sub-systems. Pedagogical issues addressed include the nature of mathematics and of mathematics learning and the role of problem solving and the impact of technology in the elementary school mathematics curriculum. |
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MATH 231 Differential Equations With Linear Algebra
DescriptionAn introduction to solutions and applications of first and second-order ordinary differential equations including Laplace transforms, elementary linear algebra, systems of linear differential equations, numerical methods and non-linear equations. |
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MATH 251 Discrete Mathematics I
DescriptionAn introduction to a number of topics in discrete mathematics, including propositional and predicate logic, functions and sets, induction and other proof techniques, and modular arithmetic. |
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MATH 252 Discrete Mathematics II
DescriptionAn introduction to counting techniques, discrete probability, and graph theory. |
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MATH 271 Multivariable Calculus
DescriptionPartial derivatives, multiple integrals and vector calculus. |
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MATH 324 Teaching Mathematics in Elementary Grades: Internship I
DescriptionThis course provides opportunities to examine issues and enact pedagogical practices that engage Pre-K through 6th-grade learners in mathematics and develop productive mathematical dispositions. Learner-centered and equitable instruction, curriculum, assessment, and partnerships with caregivers as they relate to mathematics are examined and practiced. Teacher candidates in this course will reflect upon the relationship between one’s faith/worldview and teaching of mathematics. The course is taken concurrently with field placements in grades PK-3 and/or 3-6. Corequisites: EDUC 315, EDUC 530 and SCES 310. |
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MATH 359 Mathematics Content and Teaching Methods for High School
DescriptionA course in perspectives on, principles of, and practices in the teaching of mathematics at the secondary level. |
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MATH 361 Real Analysis I
DescriptionThe real number system, sets and cardinality, the topology of the real numbers, numerical sequences and series, real functions, continuity, differentiation, and Riemann Integration. |
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MATH 365 Complex Variables with Topics
DescriptionComplex numbers, functions, integration and the Cauchy integral formula, residues and poles, applications of differentation or integration (depending on section). |
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MATH 367 Transform Theory
DescriptionApplications of the integration results from MATH 365 to engineering, mathematics, and physics. Includes a review of the Residue theorem, transform methods, and analytic continuation. |
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MATH 385 Survey Data Analysis
Cross-listed as DATA 385 and STAT 385
DescriptionAdvanced study of selected topics in mathematics. Consult instructor for course information. This course may be repeated for credit provided the topic is different. Prerequisite: a Mathematics course numbered 231 or above. Some topics may have additional requirements. |
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MATH 391 Colloquium
DescriptionMeets weekly for an hour for the presentation of various topics in Mathematics, computer science, and related disciplines by students, faculty, and visiting speakers. |
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