Courses

Below is a directory of data science and data science-adjacent courses taught at New Mexico State University. Please direct any questions regarding a course to the department offerring the class. Please also clarify with the departments whether or not the course is ever offered online.

 

Department Course Number Course Name Course Description Course Catalog Entry
SOCI 444 Networked and Connected Introduction to social network analysis in sociology. First half of the course focuses on understanding the structure of social networks. Second half of the course involves examining real-world social networks ranging from romantic relationships to political parties. Click Here
SOCI 445 Textual Analysis of Digital and Social Media Introduction to some of the methods that social scientists use to analyze digital and social media. Focus is on developing the fundamentals for designing and conducting text analysis projects. Click Here
SOCI 446 Visualizing Social Life Introduction to how to communicate sociological findings using graphics. Emphasis is on finding meaningful trends in real-world social science data and creating graphics that best communicate those associations or trends. Click Here
SOCI 544 Advanced Seminar in Social Networks Advanced introduction to social network methods. First half of the course focuses on understanding the structure of social networks. Second half of the course involves examining real-world social networks ranging from romantic relationships to political parties. Includes hands-on experience with the R statistical computing environment. Click Here
SOCI 545 Advanced Seminar in Text Analysis for the Social Sciences Advanced exploration into some of the methods that social scientists use to analyze digital and social media. Focus is on developing the fundamentals for designing and conducting text analysis projects. Includes hands-on experience with the R statistical computing environment. Click Here
SOCI 546 Advanced Seminar in Data Visualization Advanced exploration into how to communicate sociological findings using graphics. Emphasis is on finding meaningful trends in real-world social science data and creating graphics that best communicate those associations or trends. Includes hands-on experience with the R statistical computing environment, especially the ggplot2 package. Click Here
C S 453 Python Programming I This course is an introduction to programming in the Python language, covering fundamental scripts, data types and variables, functions, and simple object creation and usage. The focus will be on preparing students to use Python in their own areas. No prior programming experience is required. Click Here
C S 454 Python Programming II This course covers advanced Python programming, including classes, objects, and inheritance, embedded programming in domain applications, database interaction, and advanced data and text processing. The focus will be on preparing students to use Python in their own areas. For graduate students only. Click Here
C S 458 R Programming I This course is an introduction to data processing in the R language, covering fundamental script configuration, data types and data collections, R control structures, and basic creation of graphs and data visualizations. This course will not focus on the statistical capabilities of R, though some basic statistical computations will be used. For graduate students only. Click Here
C S 508 Introduction to Data Mining Techniques for exploring large data sets and discovering patterns in them. Data mining concepts, metrics to measure its effectiveness. Methods in classification, clustering, frequent pattern analysis. Selected topics from current advances in data mining. Students are expected to have a preparation in Discrete Mathematics and Data Structures equivalent to C S 272 and C S 278. Click Here
C S 519 Applied Machine Learning I An introductory course on practical machine learning. An overview of concepts for both unsupervised and supervised learning. Topics include classification, regression, clustering, and dimension reduction. Classical methods and algorithms such as linear regression, neural networks, support vector machines, and ensemble approaches. Recent techniques such as deep learning. Focused on applying of machine learning techniques in application domains. Click Here
C S 502 Database Management Systems I Database design and implementation; models of database management systems; privacy, security, protection, recovery; taught with C S 482; requires more advanced graduate work than C S 482. Students are expected to have solid knowledge of data structures and discrete mathematics. Click Here
C S 482 Database Management Systems I Database design and implementation; models of database management systems; privacy, security, protection, recovery. Not for C S graduate students. Click Here
C S 153 Python Programming I This course is an introduction to programming in the Python language, covering fundamental scripts, data types and variables, functions, and simple object creation and usage. The focus will be on preparing students to use Python in their own areas. No prior programming experience is required.  Click Here
C S 154 Python Programming II This course covers advanced Python programming, including classes, objects, and inheritance, embedded programming in domain applications, database interaction, and advanced data and text processing. The focus will be on preparing students to use Python in their own areas. Click Here
C S 158 R Programming I This course is an introduction to data processing in the R language, covering fundamental script configuration, data types and data collections, R control structures, and basic creation of graphs and data visualizations. This course will not focus on the statistical capabilities of R, though some basic statistical computations will be used. Click Here
C S 488 Introduction to Data Mining Techniques for exploring large data sets and discovering patterns in them. Data mining concepts, metrics to measure its effectiveness. Methods in classification, clustering, frequent pattern analysis. Selected topics from current advances in data mining. Click Here
C S 487 Applied Machine Learning I An introductory course on practical machine learning. An overview of concepts for both unsupervised and supervised learning. Topics include classification, regression, clustering, and dimension reduction. Classical methods and algorithms such as linear regression, neural networks, support vector machines, and ensemble approaches. Recent techniques such as deep learning. Focused on applying of machine learning techniques in application domains. Not for Graduate Majors. Click Here
A ST 555 Applied Multivariate Analysis Statistical methods for analysis of multivariate data, including tests on one or two mean vectors, multivariate analysis of variance, discriminant analysis, classification analysis, principal components analysis, and cluster analysis.  Click Here
A ST 505 Statistical Inference I A qualitative introduction to the concepts and methods of statistical inference. Sampling, frequency distributions (z, t, x2, F), estimation, and testing. One-way analysis of variance. Simple linear regression. Click Here
A ST 507 Advanced Regression Examination of multiple regression; residual analysis, collinearity, variable selection, weighted least squares, polynomial models, and nonlinear regression: linearizable and intrinsically nonlinear models. Click Here
A ST 515 Statistical Analysis with R Introduction to R data types, basic calculations and programming, data input and manipulation, one and two sample tests, ANOVA, regression, diagnostics, graphics, probability distributions, and basic simulations in the R software environment. Click Here
C S 582 Database Management Systems II Advanced data models and abstractions, dependencies, implementations, languages, database machines, and other advanced topics. Students are expected to have knowledge of data base management systems equivalent to C S 482. Click Here
C S 579 Special Topics: Text Mining and NLP Topic announced in the Schedule of Classes. Text Mining and NLP has been a topic. Click Here
C S 479 Special Topics: Text Mining and NLP Topics announced in the Schedule of Classes. May be repeated under different subtitles. Not for C S graduate students. Text Mining and NLP has been a topic. Click Here
E E 565 Machine Learning I A graduate-level introduction to machine learning algorithms, including supervised and unsupervised learning methods. Topics covered include clustering, linear regression models, linear discriminant functions, feed-forward neural networks, statistical pattern classification and regression, maximum likelihood, naive Bayes, non-parametric density estimation, mixture models, decision trees, and ensemble learning. Click Here
E E 465 Machine Learning I An undergraduate-level introduction to machine learning algorithms, including supervised and unsupervised learning methods. Topics covered include clustering, linear regression models, linear discriminant functions, feed-forward neural networks, statistical pattern classification and regression, maximum likelihood, naive Bayes, non-parametric density estimation, mixture models, decision trees, and ensemble learning. Click Here
C S 579 Special Topics: Big Data Topic announced in the Schedule of Classes. Big Data has been a topic. Click Here
C S 479 Special Topics: Big Data Topics announced in the Schedule of Classes. May be repeated under different subtitles. Not for C S graduate students. Big Data has been a topic. Click Here
E E 325 Signals and Systems II Introduction to communication systems including amplitude and frequency modulation. Introduction to control systems including linear feedback systems, root-locus analysis, and graphical representations. Introduction to digital signal processing including sampling, digital filtering, and spectral analysis. Click Here
E E 395 Introduction to Digital Signal Processing Undergraduate treatment of sampling/reconstruction, quantization, discrete-time systems, digital filtering, z-transforms, transfer functions, digital filter realizations, discrete Fourier transform (DFT) and fast Fourier transform (FFT), finite impulse response (FIR) and infinite impulse response (IIR) filter design, and digital signal processing (DSP) applications. Laboratory will emphasize practical implementation of signal processing including real-time signal processing. Click Here
E E 446 Digital Image Processing Two-dimensional transform theory, color images, image enhancement, restoration, segmentation, compression and understanding. Click Here
E E 596 Digital Image Processing Two-dimensional transform theory, color images, image enhancement, restoration, segmentation, compression and understanding. Click Here
E E 444 Advanced Image Processing Advanced topics in image processing including segmentation, feature extraction, object recognition, image understanding, big data, and applications. Click Here
E E 588 Advanced Image Processing Advanced topics in image processing including segmentation, feature extraction, object recognition, image understanding, big data, and applications Click Here
BCIS 575 Database Management Systems Design, development, and use of database management systems in the business environment. Taught with BCIS 475 with differentiated assignments for graduate students. Click Here
BCIS 475 Database Management Systems Design, development, and use of database management systems in the business environment. Click Here
BCIS 561 Business Analytics I This course provides an understanding of how organizations can utilize technology to successfully collect, organize, manipulate, use, and present data. The course blends the use of current technology with the managerial practices involving business analytics. The emphasis of the course will be on data management practices and the production of descriptive analytics. Not open to students who have taken BCIS 461. Taught with BCIS 461 with differentiated assignments for graduate students. Click Here
BCIS 461 Business Analytics I This course provides an understanding of how organizations can utilize technology to successfully collect, organize, manipulate, use, and present data. The course blends the use of current technology with the managerial practices involving business analytics. The emphasis of the course will be on data management practices and the production of descriptive analytics. Click Here
BCIS 566 Business Analytics II This course provides an understanding of how organizations can build and test predictive models, utilizing business-related data to estimate model parameters. The emphasis of the course will be on utilizing data management systems to produce useful predictive analytics. Not open to students who have taken BCIS 466. Taught with BCIS 466 with differentiated assignments for graduate students Click Here
BCIS 466 Business Analytics II This course provides an understanding of how organizations can build and test predictive models, utilizing business-related data to estimate model parameters. The emphasis of the course will be on utilizing data management systems to produce useful predictive analytics. Click Here
BCIS 585 Enterprise Resource Planning & Business Processes Enterprise-wide information systems and their use in enterprise resource planning (ERP). This course will examine the many cross-functional business processes. Other topics include ERP implementation issues, change management, and business process re-engineering. Hands-on exercises use SAP/3 Enterprise software. Taught with BCIS 485 with differentiated assignments for graduate students. Click Here
BCIS 485 Enterprise Resource Planning This course covers concepts in enterprise resource planning (ERP). Topics include how ERP integrates business processes across functional areas--such as the procurement process and the sales order process--and how businesses use ERP information systems in day-to-day operations as well as for performance monitoring. SAP R/3 software will be used in several hands-on examples of ERP software as a real-world example of an ERP system. Click Here
ICT 458 Web Development and Database Applications Design, planning, and building of interactive and dynamic web applications. Topics include relational databases, object oriented programming, and web security. Click Here
MATH 472 Fourier Series and Boundary Value Problems Fourier series and methods of solution of the boundary value problems of applied mathematics. Click Here
MATH 518 Fourier Series and Boundary Value Problems Same as MATH 472 with additional work for graduate students. Click Here
I E 545 Characterizing Time-Dependent Engineering Data Theory and techniques employed in the characterization of stochastic processes commonly found in engineering applications. Distribution models include exponential, gamma, Weibull, and extreme value. Design and analysis of experiments involving complete and censored data and elevated stress. Analytical techniques include parametric, nonparametric, and graphical approaches with emphasis on modern computer tools. Exact and approximate maximum-likelihood techniques are stressed. Click Here
I E 515 Stochastic Processes Modeling Introduction to the use of stochastic processes in the modeling of physical and natural systems. Use of generating functions, conditional probability and expectation, Poisson processes, random walk models, Markov chains, branching processes, Markov processes, and queuing processes in an applied setting. Click Here
I E 522 Queuing Systems Elements and classification of queuing systems, single server models, multi-server models, cost analysis and applications. Click Here
I E 567 Design and Implementation of Discrete-Event Simulation Basic modeling concepts, organizations of simulations, input data analysis, random variate generation, simulation design and analysis, model validation, output analysis, and management of simulations. Taught with I E 467 with differentiated assignments for graduate students. Click Here
I E 467 Discrete-Event Simulation Modeling Basic modeling concepts, organizations of simulations, input data analysis, random variate generation, simulation design and analysis, model validation, output analysis, and management of simulations. Differentiated graduate assignments. Click Here
ICT 460 Multimedia Tools and Support Introduction to video, audio and other digital presentation methods. Addresses the latest multimedia technology advances and how they apply to the information and communication technology fields. Sample tools like ffmpeg, and Audacity are covered. Click Here
C S 506 Computer Graphics I Languages, programming, devices, and data structures for representation and interactive display of complex objects. Taught with C S 476. Requires more advanced graduate work than C S 476. Students are expected to have knowledge of compilers design and software engineering equivalent to C S 370 and C S 371. Click Here
C S 476 Computer Graphics I Languages, programming, devices, and data structures for representation and interactive display of complex objects. Not for C S graduate students. Click Here
ASTR 630 Numerical and Statistical Methods in Astrophysics Provides basic background in numerical and statistical methods relevant to astrophysical research. Topics include a review of probability and probability distribution functions, Bayesian and frequentist approaches, data simulation, parameter estimation, Markov Chain Monte Carlo, and other topics. Click Here
BIOL 566 Advanced Bioinformatics and NCBI Database The course discusses how to use NCBI database and bioinformatic tools for research with genomics approaches. The topics include nucleotide and protein sequence analysis, similarity search with blast algorithms, gene/genome annotation, protein structure analysis, gene expression analysis, and metagenomic study. Click Here
ENGL 543 Multimedia Theory and Production Issues, theories, and production practices underlying design of multimedia, including rhetorical choices, aesthetic approaches, usability concerns, and diverse academic and popular discourses contributing to continued development of digital texts. Taught with ENGL 643. Click Here
ENGL 643 Multimedia Theory and Production Issues, theories, and production practices underlying design of multimedia, including rhetorical choices, aesthetic approaches, usability concerns, and diverse academic and popular discourses contributing to continued development of digital texts. Taught with ENGL 543. Click Here
COMM 550 Seminar in Communication Technologies Seminar on design, usage, and social impact of electronic mail, communication through computer networks, and new technologies of organizational communication such as group decision support systems (GDSS). Each student will study an actual application of a major communication technology in an organization. Click Here
STAT 535 Elementary Stochastic Processes Markov chains, Poisson processes, Brownian motion, branching processes, and queuing processes, with applications to the physical, biological, and social sciences. Click Here
STAT 572 Linear Models and Statistical Learning Core topics include distribution of quadratic forms, theory of regression, analysis of variance and covariance in linear models. Advanced topics chosen from random and mixed linear models, growth curve, classification, resampling methods, ridge regression, LASSO, and supervised and unsupervised learning, and support vector machine Click Here
STAT 480 Statistics: Theory and Applications Sampling distribution, sufficient statistics, point and interval estimation, large sample theory, hypothesis testing, and chi-square tests. Click Here
STAT 525 Statistics: Theory and Applications Same as STAT 480 with additional work for graduate students. Click Here
C S 579 Special Topics: Deep Learning Topic announced in the Schedule of Classes. Deep Learning has been a topic. Click Here
C S 479 Special Topics: Deep Learning Topics announced in the Schedule of Classes. May be repeated under different subtitles. Not for C S graduate students. Deep Learning has been a topic. Click Here
C S 579 Special Topics: Graph Data Mining Topic announced in the Schedule of Classes. Graph Data Mining has been a topic. Click Here
C S 479 Special Topics: Graph Data Mining Topics announced in the Schedule of Classes. May be repeated under different subtitles. Not for C S graduate students. Graph Data Mining has been a topic. Click Here
A ST 511 Statistical Methods for Data Analytics Statistics fundamentals, with an emphasis on inferential methods and linear regression, with practical experience in data analysis. Click Here