17 courses with the subject CEMA, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
CEMA 1030Exploratory Data Analysis3
An introduction to data science focusing on the exploratory data analysis (EDA) phase. Topics include an introduction to data science and its life cycle, missing data, biases in data, data pre-processing, no-code data visualization, discovering patterns and anomalies in the data, simple hypothesis testing and simple regression modeling, and writing reproducible reports. Cross-listed With CRJU 1030, NUTR 1030 , ARTV 1030 Click here for the Fall 2026 Class Schedule
Prerequisites: C or better in COMP 1520 . This course will emphasize the method and procedure of problem solving and discovering mathematics through games and puzzles. Click here for the Fall 2026 Class Schedule
C or better in COMP 1520 . This course will emphasize the method and procedure of problem solving and discovering mathematics through games and puzzles. Click here for the Fall 2026 Class Schedule
CEMA 2500Programming Tools for Statistical Computing3
Prerequisites: C or better in COMP 1520 . An introduction to statistical computing through freely available programming tools (R or Python-Pandas). Topics include: importing, exporting and manipulating data, basic data types and operations, vectors, arrays, matrices, lists, data frames, grouping, loops, conditionals, functions, simple and advanced graphics to visualize data and to generate plots/maps, packages/libraries for probability distributions, hypotheses testing and statistical models, writing reproducible reports of statistical analyses. Click here for the Fall 2026 Class Schedule
C or better in COMP 1520 . An introduction to statistical computing through freely available programming tools (R or Python-Pandas). Topics include: importing, exporting and manipulating data, basic data types and operations, vectors, arrays, matrices, lists, data frames, grouping, loops, conditionals, functions, simple and advanced graphics to visualize data and to generate plots/maps, packages/libraries for probability distributions, hypotheses testing and statistical models, writing reproducible reports of statistical analyses. Click here for the Fall 2026 Class Schedule
CEMA 2800Capstone: Data Science Certificate for Non-Computing Majors3
CEMA 3025Partial Differential Equations of Applied Mathematics3
Prerequisites: C or better in MATH 3020 . First-order partial differential equations; method of characteristics; weak solutions; elliptic, parabolic, and hyperbolic equations; Fourier transform; Fourier series; and eigenvalue problems. Click here for the Fall 2026 Class Schedule
C or better in MATH 3020 . First-order partial differential equations; method of characteristics; weak solutions; elliptic, parabolic, and hyperbolic equations; Fourier transform; Fourier series; and eigenvalue problems. Click here for the Fall 2026 Class Schedule
CEMA 3425Linear Algebra with Applications to Engineering3
Prerequisites: C or better with MATH 2020 . This course will present computer-based solution of systems of algebraic equations obtained from engineering problems and eigen-system analysis, Gaussian elimination, effect of round-off error, operation counts, banded matrices arising from discretization of differential equations, ill-conditioned matrices, matrix theory, least square solution of unsolvable systems, solution of non-linear algebraic equations, eigenvalues and eigenvectors, similar matrices, unitary and Hermitian matrices, positive definiteness, Cayley-Hamilton theory and function of a matrix and iterative methods. Click here for the Fall 2026 Class Schedule
Prerequisites: C or better in COMP 2810 . Data Mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It refers to the process of automatic discovery of patterns and knowledge from large data repositories including databases, data warehouses, web, document collections and data streams. It will cover the basic topics of data mining, including data preprocessing, data warehousing and OLAP, data cube, frequent pattern and association rule mining, correlation techniques and applications of data mining in Web, text, big data, social networks, and computational advertising. Click here for the Fall 2026 Class Schedule
Prerequisites: C or better in CEMA 2500 and MATH 4210 . This course covers principles of statistical inference including confidence intervals and hypothesis testing. Topics include convergence of random variables: the law of large numbers and central limit theorem; fundamental concepts in inference: point estimation, confidence intervals, hypothesis testing; estimating the CDF; bootstrapping; hypothesis testing and p -values: Wald test, chi-square distribution, permutation test, likelihood ratio test, goodness-of-fit tests, F-test, etc. Click here for the Fall 2026 Class Schedule
C or better in CEMA 2500 and MATH 4210 . This course covers principles of statistical inference including confidence intervals and hypothesis testing. Topics include convergence of random variables: the law of large numbers and central limit theorem; fundamental concepts in inference: point estimation, confidence intervals, hypothesis testing; estimating the CDF; bootstrapping; hypothesis testing and p -values: Wald test, chi-square distribution, permutation test, likelihood ratio test, goodness-of-fit tests, F-test, etc. Click here for the Fall 2026 Class Schedule
CEMA 4350Regression Modeling3
Prerequisites: C or better in CEMA 2500 and MATH 4210 . This course covers statistical models and methods. Topics include the linear regression model, least squares, least squares estimator, multiple regression, model selection, logistic regression, multivariate models, inference about independence, causal inference, confounding variables, generalized linear regression, analysis of variance, heteroscedasticity. Click here for the Fall 2026 Class Schedule
C or better in CEMA 2500 and MATH 4210 . This course covers statistical models and methods. Topics include the linear regression model, least squares, least squares estimator, multiple regression, model selection, logistic regression, multivariate models, inference about independence, causal inference, confounding variables, generalized linear regression, analysis of variance, heteroscedasticity. Click here for the Fall 2026 Class Schedule
CEMA 4630Introduction to Distributed and Data Intensive Computing3
Prerequisites: C or better in COMP 4850 . This course will introduce and provide the students with theoretical aspects of distributed system and applicable and practical aspects of cloud computing and robust characteristics for distributed and cloud computing. It will also provide a tour to introduce students to various research topics in distributed data-intensive computing, covering topics in cluster computing, grid computing, supercomputing, and cloud computing. The course will explore solutions and learn design principles for building large network-based computational systems to support data intensive computing. This course is suitable for junior/senior level undergraduates and graduate students in mathematics and computing sciences. Click here for the Fall 2026 Class Schedule
Prerequisites: C or better in MATH 3020 . This course will present scientific computation relevant to computational science and engineering, with an emphasis on the process of modeling, simulation, visualization and evaluation. Possible topics related to the five areas include: (modeling) continuous and statistical modeling; (simulation) solving and linear and non-linear systems, interpolation and approximation, numerical differential equations; (analysis) data mining and machine learning; (visualization) scalar and vector field visualization techniques; (evaluation) connection of results back to case-studies of interest from areas such as physics, biology, etc. Programming knowledge at the level of CS 1520, mathematical background at the level of MATH 3020 and basic knowledge of programming, matrix operations is required. Click here for the Fall 2026 Class Schedule
Prerequisites: C or better in MATH 2020 . This course will introduce students to key design principles and techniques for visualizing data including both the principles and techniques. Course will cover design practices, data and image models, visual perception, interaction principles, visualization tools, and applications. Students will learn the value of visualization, specific techniques in information visualization and scientific visualization, and understand how to best leverage visualization methods. Introduction to programming of web-based interactive visualizations. Click here for the Fall 2026 Class Schedule
Prerequisites: C or better in CEMA 4340 and CEMA 4350 . A broad introduction to machine learning and statistical pattern recognition. Topics include fundamental machine learning methods: k-nearest neighbor, decision tree, naive Bayes, artificial neural networks, genetic algorithms, reinforcement learning, support vector machines. Experimental methodologies will be covered including cross-validation, t-test, precision recall and ROC curves. Click here for the Fall 2026 Class Schedule
C or better in CEMA 4340 and CEMA 4350 . A broad introduction to machine learning and statistical pattern recognition. Topics include fundamental machine learning methods: k-nearest neighbor, decision tree, naive Bayes, artificial neural networks, genetic algorithms, reinforcement learning, support vector machines. Experimental methodologies will be covered including cross-validation, t-test, precision recall and ROC curves. Click here for the Fall 2026 Class Schedule
CEMA 4920Capstone Design Project3
Prerequisites: Senior classification and permission of the Department of Mathematics and Physics. Students will design, implement and document a project that ties together the concepts of their chosen concentration. The selection of projects must be approved by the department. The students must present their project in an open forum and submit a written report. Click here for the Fall 2026 Class Schedule
Senior classification and permission of the Department of Mathematics and Physics. Students will design, implement and document a project that ties together the concepts of their chosen concentration. The selection of projects must be approved by the department. The students must present their project in an open forum and submit a written report. Click here for the Fall 2026 Class Schedule
Source: North Carolina Central University's catalog, linked per course · table learning_unit · CourseShelf publish 61
