BazookaCow19/class-info huggingface dataset:
SPED 465: Curriculum and Methods in Early Childhood Special Education Introduction to the field of early childhood special education, including its history and major issues; instructional methods used in teaching and facilitating development in young children with disabilities are covered in depth. 3 undergraduate hours. 3 graduate hours.
SPED 470: School-Wide Behavior Supports in Learning Environments Provides participants with an introduction to theoretical frameworks and evidence-based approaches to promote a positive school climate, school-wide social-emotional-behavioral supports, and classroom management in learning environments. The course will focus on building understanding and skills of future educators to support students with disabilities through creating a supportive and effective learning climate, community, and behavioral supports to promote learning outcomes. 3 undergraduate hours. 3 graduate hours. Prerequisite: Admission to the Department of Special Education or consent of instructor.
SPED 471: Individualized Behavior Supports for Students with Disabilities Addresses a behavioral-analytic approach and perspective on assessment, intervention, and support of individuals with disabilities demonstrating behavioral challenges that interfere with learning and academic progress. Participants will become familiar with legal requirements within the Individualized Education Program (IEP) and the collaborative team process and procedures for 1) assessing student preferences, 2) evaluating and conducting functional behavioral assessments (FBA) and 3) developing individualized function-based and culturally-appropriate behavioral intervention plans (BIPs) within school settings 3 undergraduate hours. 3 graduate hours. Prerequisite: SPED 470, and admission to the Department of Special Education, or consent of instructor.
SPED 510: Legal Aspects of Disabilities Study of the legal rights of individuals with disabilities and their families, with emphasis on educational aspects; inter-relationship of constitutional, statute, administrative and case law at the federal, state and local levels. Case study simulations and mock due process hearings are included.
SPED 514: Equity Issues in Special Education A graduate-level overview of issues in equity and access for students with disabilities. Historical and legal foundations are reviewed, but the course focus is issues related to characteristics of individuals with disabilities, challenges in instructional service delivery, including of students with special needs in the general curriculum, and transition of students with disabilities to independent living. Participants reflect on issues in light of their own experiences. Prerequisite: Acceptance into the Master of Education with an emphasis on Diversity and Equity in Education Program or instructor approval.
SPED 517: Disability Issues in Special Education Overview of special education at the graduate level. Focus is placed on issues related to: assessment, identification, and characteristics across all disability areas. The greatest emphasis is placed on strategies for including students with disabilities in the general curriculum. Historical and legal perspectives that provide the foundation for special education are discussed.
SPED 524: Supervised Practice in Special Education Supervised practice in one or more settings in which students with disabilities are served; practicum settings may include day, residential, special, and regular schools which serve students with disabilities. 1 to 8 graduate hours. No professional credit. Approved for S/U grading only. May be repeated in same or subsequent terms to a maximum of 8 hours. Prerequisite: Admission to the graduate program in special education; consent of supervising faculty member.
SPED 525: Seminar in Trauma-Informed Education Explores current issues in the education field (e.g., trauma caused by service systems, seclusion and restraint policies, special education services, etc.) and how they relate to trauma and trauma-informed education. Additionally, there will be a focus on identifying and discussing issues of practice and how to make practice more trauma-informed. 2 graduate hours. No professional credit. This course will be an 8-week course with both large- and small-group synchronous meeting times weekly. Prerequisite: Restricted to students admitted to the EdM program in Curriculum and Instruction with a concentration in Trauma-Informed Practices and Pedagogyor, or with permission from instructor.
SPED 550: Methods of Educational Inquiry Same as CI 550, EPSY 550, and ERAM 550. See CI 550.
SPED 575: Mixed Method Inquiry Same as EPSY 575. See EPSY 575.
SPED 590: Seminar for Advanced Students Seminar in the education of individuals with disabilities; open only to persons who have been admitted for graduate study. 0 to 8 graduate hours. No professional credit. Approved for Letter and S/U grading. May be repeated in separate terms to a maximum of 8 hours.
SPED 591: Field Study and Thesis Seminar Planning field studies and thesis problems by graduate students; students present their studies at each of four stages: (1) the inception, delimitation, tentative design stage; (2) the proposed design stage; (3) the revised design stage; and (4) the final design stage. Students are expected to analyze all presentations critically. May be repeated up to 8 hours. Prerequisite: Admission to graduate studies in Special Education or consent of instructor.
SPED 592: Concepts and Issues in Special Education I Roles and competencies for special education leadership positions; includes literature critique, and preparation and presentation of a major review paper in an area of research interest. Prerequisite: Admission to doctoral studies in Special Education or consent of instructor.
SPED 595: Independent Study Self-directive, independent study, that is, develops the individual's ability as an independent student and enables the student to pursue needed study in a field in which appropriate courses are not being offered during a given term. May be repeated with approval. Prerequisite: Approval of study outline by advisor and the department head prior to enrollment.
SPED 599: Thesis Research Individual direction of research and thesis writing. Approved for S/U grading only. May be repeated.
STAT 100: Statistics First course in probability and statistics at a precalculus level; emphasizes basic concepts, including descriptive statistics, elementary probability, estimation, and hypothesis testing in both nonparametric and normal models. Credit is not given for both STAT 100 and any one of the following: ECON 202, PSYC 235, or SOC 485. Prerequisite: MATH 112.
STAT 107: Data Science Discovery Data Science Discovery is the intersection of statistics, computation, and real-world relevance. As a project-driven course, students perform hands-on-analysis of real-world datasets to analyze and discover the impact of the data. Throughout each experience, students reflect on the social issues surrounding data analysis such as privacy and design. Same as CS 107 and IS 107.
STAT 199: Undergraduate Open Seminar See course schedule for topics. Approved for Letter and S/U grading. May be repeated if topics vary.
STAT 200: Statistical Analysis Survey of statistical concepts, data analysis, designed and observational studies and statistical models. Statistical computing using a statistical package such as R or a spreadsheet. Topics to be covered include data summary and visualization, study design, elementary probability, categorical data, comparative experiments, multiple linear regression, analysis of variance, statistical inferences and model diagnostics. May be taken as a first statistics course for quantitatively oriented students, or as a second course to follow a basic concepts course. Credit is not given for both STAT 200 and STAT 212.
STAT 207: Data Science Exploration Explores the data science pipeline from hypothesis formulation, to data collection and management, to analysis and reporting. Topics include data collection, preprocessing and checking for missing data, data summary and visualization, random sampling and probability models, estimating parameters, uncertainty quantification, hypothesis testing, multiple linear and logistic regression modeling, classification, and machine learning approaches for high dimensional data analysis. Students will learn how to implement the methods using Python programming and Git version control. Prerequisite: STAT 107 or consent of instructor.
STAT 212: Biostatistics Application of statistical reasoning and statistical methodology to biology. Topics include descriptive statistics, graphical methods, experimental design, probability, statistical inference and regression. In addition, techniques of statistical computing are covered. Credit is not given for both STAT 212 and STAT 200.
STAT 361: Probability & Statistics for Computer Science Same as CS 361. See CS 361.
STAT 385: Statistics Programming Methods Statisticians must be savvy in programming methods useful to the wide variety of analysis that they will be expected to perform. This course provides the foundation for writing and packaging statistical algorithms through the creation of functions and object oriented programming. Fundamental programming techniques and considerations will be emphasized. Students will also create dynamic reports that encapsulate their implemented algorithms. Students must have access to a computer on which they can install software. Prerequisite: One of STAT 107, STAT 200, or STAT 212.
STAT 390: Individual Study May be repeated to a maximum of 8 hours. Prerequisite: Consent of instructor.
STAT 391: Honors Individual Study May be repeated to a maximum of 8 hours. Prerequisite: Consent of instructor.
STAT 400: Statistics and Probability I Introduction to mathematical statistics that develops probability as needed; includes the calculus of probability, random variables, expectation, distribution functions, central limit theorem, point estimation, confidence intervals, and hypothesis testing. Offers a basic one-term introduction to statistics and also prepares students for STAT 410 and STAT 425. Same as MATH 463. 4 undergraduate hours. 4 graduate hours. Prerequisite: MATH 231. Concurrent Enrollment in MATH 241 is required. Not intended for first-time freshmen.
STAT 409: Actuarial Statistics II Continuation of STAT 408. Examines parametric point and interval estimation, including maximum likelihood estimation, sufficiency, completeness, and Bayesian estimation; hypothesis testing; linear models; regression and correlation. Same as ASRM 402. 4 undergraduate hours. 4 graduate hours. Credit is not given for both STAT 409 and STAT 410. Prerequisite: STAT 408.
STAT 410: Statistics and Probability II Continuation of STAT 400. Includes moment-generating functions, transformations of random variables, normal sampling theory, sufficiency, best estimators, maximum likelihood estimators, confidence intervals, most powerful tests, unbiased tests, and chi-square tests. Same as MATH 464. 3 undergraduate hours. 4 graduate hours. Credit is not given for both STAT 410 and STAT 409. Prerequisite: MATH 241 and STAT 400.
STAT 420: Methods of Applied Statistics Systematic, calculus-based coverage of the more widely used methods of applied statistics, including simple and multiple regression, correlation, analysis of variance and covariance, multiple comparisons, goodness of fit tests, contingency tables, nonparametric procedures, and power of tests; emphasizes when and why various tests are appropriate and how they are used. Same as ASRM 450. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 408 or STAT 400; MATH 231 or equivalent; knowledge of basic matrix manipulations; or consent of instructor.
STAT 425: Statistical Modeling I This is the foundation for advanced statistical modeling with a focus on multiple strategies for analyzing data. The course explores linear regression, least squares estimates, F-tests, analysis of residuals, regression diagnostics, transformations, model building, generalized and weighted least squares, PCA, A/B testing, randomization tests, ANOVA, random effects, mixed effects, and longitudinal data. Statistical computing is an integral part of the course. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 400; MATH 257 or MATH 415. Concurrent Enrollment in STAT 410 is preferred.
STAT 426: Statistical Modeling II This is a continuation in the study of advanced statistical modeling techniques with a focus on categorical data. The course explores logistic regression, generalized linear models, goodness-of-fit, link functions, count regression, log-linear models, probability models for contingency tables, and ordinal response models. Statistical computing is an integral part of the course. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 410; STAT 425.
STAT 428: Statistical Computing Examines statistical packages, numerical analysis for linear and nonlinear models, graphics, and random number generation and Monte Carlo methods. Same as CSE 428. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 410 or equivalent; knowledge of a programming language.
STAT 429: Time Series Analysis Studies theory and data analysis for time series; examines auto-regressive moving average model building and statistical techniques; and discusses spectral model building and statistical analysis using windowed periodograms and Fast Fourier Transformations. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 410.
STAT 430: Topics in Applied Statistics Formulation and analysis of mathematical models for random phenomena; extensive involvement with the analysis of real data; and instruction in statistical and computing techniques as needed. 3 undergraduate hours. 4 graduate hours. May be repeated in the same or separate terms if topics vary. Prerequisite: STAT 410; STAT 425. Some topics may require additional prerequisites. Read the section text for each topic.
STAT 431: Applied Bayesian Analysis Introduction to the concepts and methodology of Bayesian statistics, for students with fundamental knowledge of mathematical statistics. Topics include Bayes' rule, prior and posterior distributions, conjugacy, Bayesian point estimates and intervals, Bayesian hypothesis testing, noninformative priors, practical Markov chain Monte Carlo, hierarchical models and model graphs, and more advanced topics as time permits. Implementations in R and specialized simulation software. Same as ASRM 453. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 410 and knowledge of R.
STAT 432: Basics of Statistical Learning Topics in supervised and unsupervised learning are covered, including logistic regression, support vector machines, classification trees and nonparametric regression. Model building and feature selection are discussed for these techniques, with a focus on regularization methods, such as lasso and ridge regression, as well as methods for model selection and assessment using cross validation. Cluster analysis and principal components analysis are introduced as examples of unsupervised learning. Same as ASRM 451. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 400, and either STAT 420 or STAT 425.
STAT 433: Stochastic Processes A stochastic process is a random process that represents the evolution of some system over time. Topics may include discrete-time and continuous-time Markov chains, birth-and-death chains, branching chains, stationary distributions, random walks, Markov pure jump processes, birth-and-death processes, renewal processes, Poisson process, queues, second order processes, Brownian motion (Wiener process), and Ito's lemma. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 400 required, STAT 410 preferred, and MATH 225 (or equivalent knowledge of Linear Algebra) highly recommended.
STAT 437: Unsupervised Learning Unsupervised learning is a type of machine learning that deals with finding patterns in data without the use of labeled examples. Two major unsupervised learning techniques, clustering and dimensionality reduction, will be covered with a focus on methods, evaluation metrics, and interpretation of results. The methodologies enable discovery of and inference about hidden insights contained in high-dimensional unlabeled data. Applications on real and artificial datasets are emphasized using programming languages such as Python. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 410 and either MATH 415 or MATH 257.
STAT 440: Statistical Data Management The critical elements of data storage, data cleaning, and data extractions that ultimately lead to data analysis are presented. Includes basic theory and methods of databases, auditing and querying databases, as well as data management and data preparation using standard large-scale statistical software. Students will gain competency in the skills required in storing, cleaning, and managing data, all of which are required prior to data analysis. Same as CSE 440. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 400 or STAT 409.
STAT 443: Professional Statistics This project-based course emphasizes written, visual, and oral communication of statistical results and conclusions. An introduction to statistical consulting is also provided. Additional topics include introductions to statistical methodologies in industry and aspects of careers in statistics. 3 undergraduate hours. 4 graduate hours. Prerequisite: STAT 420 or STAT 425 (completion preferred, but may be taken concurrently) or consent of instructor.
STAT 447: Data Science Programming Methods The field of data science is revolutionizing science and industries. Work across many fields is becoming more data driven, affecting available jobs and required skills. Increasing amounts of data, along with novel ways of analyzing them, lead the economy as well as society and daily life to become more data-dependent. This course aims to provide the principal foundations to working with data at scale. We will cover shell programming, git version control, SQL basics, a lot of R, and some more advanced topics such as Docker and some C++. 3 undergraduate hours. 4 graduate hours.
STAT 448: Advanced Data Analysis Several of the most widely used techniques of data analysis are discussed with an emphasis on statistical computing. Topics include linear regression, analysis of variance, generalized linear models, and analysis of categorical data. In addition, an introduction to data mining is provided considering classification, model building, decision trees, and cluster analysis. Same as CSE 448. 4 undergraduate hours. 4 graduate hours. Prerequisite: STAT 400 or STAT 409, and credit for or concurrent registration in STAT 410.
STAT 480: Big Data Analytics Examines current topics and techniques for efficiently and effectively managing and analyzing large-scale data. The course focuses on applications of advanced statistical analysis in data science for massive data sets. Topics include current best practices and technologies for implementation such as parallel and distributed processing, distributed storage techniques, and modern computational frameworks such as cloud computing. 3 undergraduate hours. 4 graduate hours. Prerequisite: (STAT 440 or STAT 447) and (STAT 420 or STAT 425); or permission of the instructor.
STAT 510: Mathematical Statistics Provides a graduate level foundation in fundamental mathematical statistics topics including order statistics, exponential families, sufficiency, Rao-Blackwell theorem, Cramer-Rao lower bound, point estimation, hypothesis testing and interval estimation, likelihood and Bayesian methods, and large-sample asymptotics. 4 graduate hours. No professional credit. Prerequisite: STAT 410.
STAT 511: Advanced Mathematical Statistics Provides an advanced introduction to rigorous mathematical foundations of statistical inference, covering topics such as distribution theory, point estimation theory, interval estimation, theory of hypothesis testing, and decision theory. 4 graduate hours. No professional credit. Prerequisite: Familiarity with probability theory and statistics at the level of STAT 410. For Graduate Students Only.
STAT 525: Topics in Computational Statistics Various topics in computational statistics, such as optimization, Monte Carlo methods, Bayesian computation, and machine learning. Same as CSE 525. 4 graduate hours. No professional credit. May be repeated if topics vary. Prerequisite: STAT 425, STAT 426, and STAT 510 or 511; or consent of instructor.
STAT 527: Advanced Regression Analysis An advanced introduction to regression analysis with applications to analysing data from disciplines such as biostatistics and economics. The course will introduce classical as well as modern regression methods and goes into the depths of those techniques to understand the motivation, justification, implementation of those methods. An emphasis will be given to understand the statistical properties of those methods along with their practical advantages and limitations. Both theoretical and applied aspects of regression analysis will be discussed. 4 graduate hours. No professional credit. Prerequisite: STAT 410, STAT 510 or STAT 511 (concurrent enrollment is sufficient), and knowledge of R. For Graduate Students Only.
STAT 542: Statistical Learning Modern techniques of predictive modeling, classification, and clustering are discussed. Examples of these are linear regression, nonparametric regression, kernel methods, regularization, cluster analysis, classification trees, neural networks, boosting, discrimination, support vector machines, and model selection. Applications are discussed as well as computation and theory. Same as ASRM 551 and CSE 542. 4 graduate hours. No professional credit. Prerequisite: STAT 410 and STAT 425.
STAT 552: Theory of Probability II Same as MATH 562. See MATH 562.
STAT 553: Probability and Measure I Measures and probabilities; integration and expectation; convergence theorems and inequalities for integrals and expectations; independence; convergence in probability, almost surely, and mean; Three Series Theorem; laws of large numbers. Prerequisite: MATH 447 or consent of instructor.
STAT 555: Applied Stochastic Processes Same as MATH 564. See MATH 564.
STAT 556: Advanced Time Series Analysis This is a graduate-level course on time series analysis, with an emphasis on nonlinear and multivariate time series. Topics include: linear time series, nonlinear time series, continuous-time models, multivariate and high-dimensional models. Students will learn how to build adequate models, perform statistical estimation and inference, conduct prediction, and related topics. Students will also learn some basic mathematical tools (such as Markov chains, martingales, stochastic calculus, concentration inequalities, etc.) for theoretically analyzing large-sample properties of general nonlinear random processes. 4 graduate hours. No professional credit. Prerequisite: STAT 429 or equivalent.
STAT 558: Risk Modeling and Analysis Same as MATH 563. See MATH 563.
STAT 571: Multivariate Analysis Inference in multivariate statistical populations emphasizing the multivariate normal distribution; derivation of tests, estimates, and sampling distributions; and examples from the natural and social sciences. Prerequisite: STAT 410 and MATH 415, or consent of instructor.
STAT 590: Individual Study and Research Directed reading and research. Approved for letter and S/U grading. May be repeated with approval. Prerequisite: Consent of instructor.
STAT 593: STAT Internship Supervised, off-campus experience in a field in which statistical science plays an important role. Approved for letter and S/U grading. Prerequisite: STAT 425 and consent of instructor.
STAT 599: Thesis Research Approved for S/U grading only. May be repeated. Prerequisite: Consent of instructor.
SWAH 201: Elementary Swahili I Beginning standard Swahili; emphasizes grammar, pronunciation, reading and conversation in standard Swahili. Same as AFST 231. Participation in language laboratory required.