APPM 1235 - Pre-Calculus for Engineers
Primary Instructor
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Spring 2018 / Fall 2018
Prepares students for the challenging content and pace of the calculus sequence required for all engineering majors. Covers algebra, trigonometry and selected topics in analytical geometry. Prepares students for the calculus courses offered for engineering students. Requires students to engage in rigorous work sessions as they review topics that they must be comfortable with to pursue engineering course work. Structured to accustom students to the pace and culture of learning encountered in engineering math courses. For more information about the math placement referred to in the "Enrollment Requirements", please contact your academic advisor. Formerly GEEN 1235. Degree credit not granted for this course and MATH 1021 and MATH 1150.
APPM 1350 - Calculus 1 for Engineers
Primary Instructor
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Spring 2026
Topics in analytical geometry and calculus including limits, rates of change of functions, derivatives and integrals of algebraic and transcendental functions, applications of differentiations and integration. Students who have already earned college credit for calculus 1 are eligible to enroll in this course if they want to solidify their knowledge base in calculus 1. For more information about the math placement referred to in the "Enrollment Requirements", contact your academic advisor. Degree credit not granted for this course and APPM 1345 or ECON 1088 or MATH 1081 or MATH 1300 or MATH 1310 or MATH 1330.
APPM 2360 - Introduction to Differential Equations with Linear Algebra
Primary Instructor
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Fall 2024
Introduces ordinary differential equations, systems of linear equations, matrices, determinants, vector spaces, linear transformations, and systems of linear differential equations. Credit not granted for this course and both MATH 2130 and MATH 3430.
COEN 1236 - Precalculus Work Group
Primary Instructor
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Spring 2018
Develops and enhances problem solving skills for students enrolled in APPM 1235. Course is conducted in a collaborative learning environment with students working in groups under the guide of a facilitator.
CSCI 2820 - Linear Algebra with Computer Science Applications
Primary Instructor
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Spring 2022
Introduces the fundamentals of linear algebra in the context of computer science applications. Includes vector spaces, matrices, linear systems, and eigenvalues. Includes the basics of floating point computation and numerical linear algebra. Same as CSPB 2820.
CSCI 2824 - Discrete Structures
Primary Instructor
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Fall 2018 / Spring 2019 / Fall 2019 / Spring 2021 / Fall 2021
Covers foundational materials for computer science that is often assumed in advanced courses. Topics include set theory, Boolean algebra, functions and relations, graphs, propositional and predicate calculus, proofs, mathematical induction, recurrence relations, combinatorics, discrete probability. Focuses on examples based on diverse applications of computer science. Recommended prerequisite: Calc 2 (APPM 1360 or MATH 2300) is strongly recommended. Same as CSPB 2824.
CSCI 2830 - Special Topics in Computer Science
Primary Instructor
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Spring 2019
Covers topics of interest in computer science at the sophomore level. Content varies from semester to semester. Does not count as Computer Science credit for the Computer Science BA, BS or minor. May be repeated up to 9 total credit hours.
CSCI 2834 - Discrete Structures Workgroup
Primary Instructor
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Spring 2021 / Fall 2021
Provides additional problem-solving practice and guidance for students enrolled in CSCI 2824. Students work in a collaborative environment to further develop their problem-solving skills with the assistance of facilitators. Does not count as Computer Science credit for the Computer Science BA, BS, or minor.
CSCI 3022 - Introduction to Data Science with Probability and Statistics
Primary Instructor
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Fall 2019 / Spring 2020 / Summer 2021
Introduces students to the tools, methods and theory behind extracting insights from data. Covers algorithms of cleaning and munging data, probability theory and common distributions, statistical simulation, drawing inferences from data, and basic statistical modeling. Same as CSPB 3022.
CSCI 3104 - Algorithms
Primary Instructor
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Spring 2021 / Spring 2024
Covers the fundamentals of algorithms and various algorithmic strategies, including time and space complexity, sorting algorithms, recurrence relations, divide and conquer algorithms, greedy algorithms, dynamic programming, linear programming, graph algorithms, problems in P and NP, and approximation algorithms. Same as CSPB 3104.
CSCI 3202 - Introduction to Artificial Intelligence
Primary Instructor
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Spring 2020 / Fall 2021 / Spring 2022 / Spring 2024
Surveys artificial intelligence techniques of search, knowledge representation and reasoning, probabilistic inference, machine learning, and natural language. Knowledge of Python is strongly recommended. Same as CSPB 3202.
CSPB 2820 - Linear Algebra with Computer Science Applications
Primary Instructor
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Summer 2025
Introduces the fundamentals of linear algebra in the context of computer science applications. Includes vector spaces, matrices, linear systems, and eigenvalues. Includes the basics of floating point computation and numerical linear algebra. Same as CSCI 2820.
CSPB 2824 - Discrete Structures
Primary Instructor
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Summer 2024
Covers foundational materials for computer science that is often assumed in advanced courses. Topics include set theory, Boolean algebra, functions and relations, graphs, propositional and predicate calculus, proofs, mathematical induction, recurrence relations, combinatorics, discrete probability. Focuses on examples based on diverse applications of computer science. Same as CSCI 2824.
CSPB 3702 - Cognitive Science
Primary Instructor
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Summer 2024
Introduces cognitive science, drawing from psychology, philosophy, artificial intelligence, neuroscience, and linguistics. Studies the linguistic relativity hypothesis, consciousness, categorization, linguistic rules, the mind-body problem, nature versus nurture, conceptual structure and metaphor, logic/problem solving and judgment. Emphasizes the nature, implications and limitations of the computational model of mind. Recommended prerequisites: LING 2000 or PHIL 2440 or PSYC 2145. Same as INFO 3702 and LING 3005 and PHIL 3310 and PSYC 3005 and SLHS 3003 and CSCI 3702.
DTSC 5930 - Professional Internship
Primary Instructor
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Fall 2025
This class provides a structure for DS graduate students to receive academic credit for internships with industry partners that have an academic component to them suitable for graduate-level work. Participation in the program will consist of an internship agreement between a student and an industry partner who will employ the student in a role that supports the academic goals of the internship. Instructor participation will include facilitation of mid-term and final assessments of student performance as well as support for any academic-related issues that may arise during the internship period. Recommended prerequisite: may be taken during any term following initial enrollment and participation in DS graduate programs. May be repeated up to 6 total credit hours.
STAT 4000 - Statistical Methods and Application I
Primary Instructor
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Fall 2025
Introduces exploratory data analysis, probability theory, statistical inference, and data modeling. Topics include discrete and continuous probability distributions, expectation, laws of large numbers, central limit theorem, statistical parameter estimation, hypothesis testing, and regression analysis. Considerable emphasis on applications in the R programming language. Same as STAT 5000.
STAT 4010 - Statistical Methods and Applications II
Primary Instructor
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Spring 2024 / Spring 2025 / Spring 2026
Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language. Same as STAT 5010.
STAT 5000 - Statistical Methods and Application I
Primary Instructor
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Fall 2024 / Fall 2025
Introduces exploratory data analysis, probability theory, statistical inference, and data modeling. Topics include discrete and continuous probability distributions, expectation, laws of large numbers, central limit theorem, statistical parameter estimation, hypothesis testing, and regression analysis. Considerable emphasis on applications in the R programming language. Recommended prerequisites of APPM 1360 or MATH 2300 or equivalent. Same as STAT 4000.
STAT 5010 - Statistical Methods and Applications II
Primary Instructor
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Spring 2024 / Spring 2025 / Spring 2026
Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language. Same as STAT 4010.
