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Fall 2017
Feb 20, 2018
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STAT 104 - Elementary Statistics
Elementary Statistics Skill Area II Prereq.: MATH 101 (C- or higher) or placement exam. Intuitive treatment of some fundamental concepts involved in collecting, presenting, and analyzing data. Topics include frequency distributions, graphical presentations, measures of relative position, measures of variability, probability, probability distributions (binomial and normal), sampling theory, regression, and correlation. No credit given to students with credit for STAT 108, 200, 215, 314 or 315.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction

Mathematics Department

Course Attributes:
SK2- Mathematics Requirement

STAT 200 - Business Statistics I
Business Statistics I Skill Area II Prereq.: MATH 101 (C- or higher) or placement exam. Application of statistical methods used for a description of analysis of business problems. The development of analytic skills is enhanced by use of one of the widely available statistical packages and a graphing calculator. Topics include frequency distributions, graphical presentations, measures of relative position, measures of central tendency and variability, probability distributions including binomial and normal, confidence intervals, and hypothesis testing. No credit given to students with credit for STAT 104, 108, 215, 314, or 315.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction

Mathematics Department

Course Attributes:
SK2- Mathematics Requirement

STAT 201 - Business Statistics II
Business Statistics II Skill area II Prereq.: STAT 200 or equivalent (C- or higher). Application of statistical methods used for a description and analysis of business problems. The development of analytical skills is enhanced by use of one of the widely available statistical packages. Topics include continuation of hypothesis testing, multiple regression and correlation analysis, residual analysis, variable selection techniques, analysis of variance and design of experiments, goodness of fit, and tests of independence. No credit given to students with credit for STAT 216, 416 or 453.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction
All Sections for this Course

Mathematics Department

Course Attributes:
SK2- Mathematics Requirement

STAT 215 - Stat for Behavioral Sci I
Statistics for Behavioral Sciences I Skill Area II Prereq.: MATH 101 (C- or higher) or placement exam. Introductory treatment of research statistics used in behavioral sciences. Quantitative descriptive statistics, including frequency distributions, measures of central tendency and variability, correlation, and regression. A treatment of probability distributions including binomial and normal. Introduction to the idea of hypothesis testing. No credit given to students with credit for STAT 104, 108, 200, 314 or 315.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction
All Sections for this Course

Mathematics Department

Course Attributes:
SK2- Mathematics Requirement

STAT 216 - Stat for Behavioral Sci II
Statistics for Behavioral Sciences II Spring. Skill Area II Prereq.: STAT 215 or permission of instructor. Continuation of STAT 215. Survey of statistical tests and methods of research used in behavioral sciences, including parametric and nonparametric methods. No credit given to students with credit for STAT 201, 416 or 453.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

Course Attributes:
SK2- Mathematics Requirement

STAT 314 - Intro Stat Secondary Teachers
Introductory Statistics for Secondary Teachers Fall. Prereq.: MATH 218 and 221. Techniques in probability and statistics necessary for secondary school teaching. Topics include sampling, probability, probability distributions, simulation, statistical inference, and the design and execution of a statistical study. Computers and graphing calculators will be used. No credit given to those with credit for STAT 201, 216 or 453. Graphing calculator required.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 315 - Mathematical Statistics I
Mathematical Statistics I Fall. Prereq.: MATH 221; and MATH 218 or permission of department chair. Theory and applications in statistical analysis. Combinations, permutations, probability, distributions of discrete and continuous random variables, expectation, and common distributions (including normal).

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 416 - Mathematical Statistics II
Mathematical Statistics II [GR] Prereq.: STAT 315. Continuation of theory and applications of statistical inference. Elements of sampling, point and interval estimation of population parameters, tests of hypotheses, and the study of multivariate distributions.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

Course Attributes:
400 level - Grad Credit

STAT 425 - Loss/Freq Dist Crdblty Thry
Loss and Frequency Distributions and Credibility Theory Spring. [GR] Prereq.: STAT 416 (may be taken concurrently). Topics chosen from credibility theory, loss distributions, simulation, and time series.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

Course Attributes:
400 level - Grad Credit

STAT 453 - Applied Statistical Inference
Applied Statistical Inference Spring, Summer. [GR] Prereq.: Graduate standing with at least one course in statistics or STAT 315 or permission of instructor. Statistical techniques used to make inferences in experiments in social, physical, and biological sciences, and in education and psychology. Topics included are populations and samples, tests of significance concerning means, variances and proportions, and analysis of variance. No credit given to students with credit for STAT 201 or 216.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

Course Attributes:
400 level - Grad Credit

STAT 455 - Experimental Design
Experimental Design Fall. (O) [GR] Prereq.: STAT 201 or 216 or 416 or permission of instructor. Introduction to experimental designs in statistics. Topics include completely randomized blocks, Latin square, and factorial experiments.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

Course Attributes:
400 level - Grad Credit

STAT 456 - Fundamentals of SAS
Fundamentals of SAS Spring. (E) [GR] Prereq.: CS 151 and STAT 201 or 216 or equivalent. Introduction to statistical software. Topics may include creation and manipulation of SAS data sets; and SAS implementation of the following statistical analyses: basic descriptive statistics, hypotheses tests, multiple regression, generalized linear models, discriminant analysis, clustering and analysis, factor analysis, logistic analysis and model evaluation. This course is cross listed with MKT 444. No credit given to students with credit for MKT 444.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

Course Attributes:
400 level - Grad Credit

STAT 465 - Nonparametric Statistics
Nonparametric Statistics Fall. (E) [GR] Prereq.: STAT 201 or 216 or 416 or permission of instructor. General survey of nonparametric or distribution-free test procedures and estimation techniques. Topics include one-sample, paired-sample, two-sample, and k-sample problems as well as regression, correlation, and contingency tables. Comparisons with the standard parametric procedures will be made, and efficiency and applicability discussed.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction

Mathematics Department

Course Attributes:
400 level - Grad Credit

STAT 476 - Topics in Statistics
Topics in Statistics Spring. (O) [GR] Prereq.: Permission of instructor. Topics depending on interest and qualifications of the students will be chosen from sampling theory, decision theory, probability theory, Bayesian statistics, hypothesis testing, time series or advanced topics in other areas. May be repeated under different topics to a maximum of 6 credits.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction
All Sections for this Course

Mathematics Department

Course Attributes:
400 level - Grad Credit

STAT 520 - Multvriate Anlsis Data Mining
Multivariate Analysis for Data Mining Fall. Prereq.: Two semesters of applied statistics (such as STAT 104/453, STAT 200/201, or STAT 215/216), or two semesters of statistics approved by advisor, or permission of department chair. Concept-based introduction to multivariate analysis, useful for data mining and predictive modeling, with emphasis given to interpreting output and checking model assumptions using one of the standard statistical packages. Topics may include: multivariate normal distribution, simultaneous inferences, one-and two-way MANOVA, multivariate multiple regression and ANACOVA, correlation, principle component and factor analysis, discriminant analysis, cluster analysis and multidimentional scaling, path analysis, structural equation modeling, and longitudinal data analysis.

4.000 Credit hours
4.000 Lecture hours

Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 521 - Introduction to Data Mining
Introduction to Data Mining Prereq.: STAT 104 or STAT 200 or STAT 215 or STAT 315 or permission of department chair. Data mining models and methodologies. Topics may include data preparation, data cleaning, exploratory data analysis, statistical estimation and prediction, regression modeling, multiple regression, model building, classification and regression trees, and report writing.

4.000 Credit hours
4.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 522 - Clustering and Affinity Anlsis
Clustering and Affinity Analysis Spring. Prereq.: STAT 521 or permission of department chair. Investigation and application of methods and models used for clustering and affinity analysis. Topics may include dimension reduction methods, k-means clustering, hierarchical clustering, Kohonen networks clustering, BIRCH clustering, anomaly detection, market basket analysis, and association rules using the a priori and generalized rule induction algoriths.

4.000 Credit hours
4.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 523 - Predictive Analytics
Predictive Analytics Fall. Prereq.: STAT 521 or permission of department chair. Investigation and application of methods and models used for predictive modeling and predictive analytics. Topics may include neural networks, logistic regression, k-nearest neighbor classification, the C4.5 algorithm, CHAID and QUEST decision trees, feature selection, boosting, naive Bayes classification and Bayesian networks, time series, and model evaluation techniques.

4.000 Credit hours
4.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 525 - Web Mining
Web Mining Spring. Prereq.: STAT 521 or permission of department chair. Methods and techniques for mining information from web structure, content, and usage. Topics may include web log cleaning and filtering, de-spidering, user identification, session identification, path completion exploratory data analysis for web mining, and modeling for web mining, including clustering, association, and classification.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 526 - Data Mining-Genomics&Proteomcs
Data Mining for Genomics and Proteomics Fall. Prereq.: STAT 521 or permission of the instructor. Topics include selection of data mining methods appropriate for the goals of a biomedical study (supervised versus unsupervised, univariate versus multivariate), analysis of gene expression microarray data, biomarker discovery, feature selection, building and validation of classification models for medical diagnosis, prognosis, drug discovery, random forests and ensemble classifiers.

4.000 Credit hours
4.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 527 - Text Mining
Text Mining Spring. Prereq.: STAT 521 or permission of the instructor. Intensive investigation of text mining methodologies, including pattern matching with regular expressions, reformatting data, contingency tables, part-of-speech tagging, top-down parsing, probability and text sampling, the bag-of-words model and the effect of sample size. Extensive use of Perl and Perl modules to analyze text documents.

4.000 Credit hours
4.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 529 - Current Issues in Data Mining
Current Issues in Data Mining Irregular. Prereq.: Admission to the M.S. Data Mining program or permission of department chair. Topics depending on interest and qualifications of the students will be chosen from recent developments in data mining, including statistical pattern recognition, statistical natural language processing, bioinformatics, text mining, and analytical CRM. Use of statistical and data mining software. May be repeated under different topics to a maximum of 9 credits. Migration and Attrition. Extensive use of SPSS' Clementine data mining software is required.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 534 - Appld Catgorcl Data Analsis
Applied Categorical Data Analysis Fall. Prereq.: STAT 201 or STAT 216, or equivalent, or permission of department chair. Introduction to analysis and interpretation of categorical data using analysis of variance or regression analogs. Topics may include contingency tables, generalized linear models, logistic regression, log-linear models, models for matched pairs, and modeling correlated and clustered responses; use of computer software such as SAS and R.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 551 - Applied Stochastic Processes
Applied Stochastic Processes Fall. (O) Prereq.: STAT 315 and MATH 228 or permission of instructor. An introduction to stochastic processes. Topics include Markov, Poisson, birth and death, renewal, and stationary processes. Statistical inferences of Markov processes are discussed.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Hybrid: Online/On-Ground Combo, Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 567 - Linear Models & Time Series
Linear Models and Time Series Spring. Prereq.: STAT 416. Introduction to the methods of least squares. Topics include general linear models, least squares estimators, inference, hypothesis testing, and forecasting with ARIMA models.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 570 - Applied Multivariate Analysis
Applied Multivariate Analysis Spring. (O) Prereq.: MATH 228; STAT 416 or, with permission of instructor, STAT 201, 216, or 453. Introduction to analysis of multivariate data with examples from economics, education, psychology, and health care. Topics include multivariate normal distribution, Hotelling's T2, multivariate regression, analysis of variance, discriminant analysis, factor analysis and cluster analysis. Computer packages assist in the design and interpretation of multivariate data.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 575 - Mathematical Statistics III
Mathematical Statistics III Fall. (E) Prereq.: STAT 416 or equivalent. Continuation of theory and applications of statistical inference. Advanced topics in the estimation of population parameters and the testing of hypotheses. Introduction to Bayesian methods, regression, correlation and the analysis of variance.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 576 - Advanced Topics in Statistics
Advanced Topics in Statistics Irregular. Prereq.: Permission of instructor. Seminar in probability theory, sampling theory, decision theory, Bayesian statistics, hypothesis testing, or other advanced area. Topic depending on needs and qualifications of students. May be repeated under different topic to a maximum of 6 credits.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Independent Study, Lecture, Online Instruction

Mathematics Department

STAT 599 - Thesis
Thesis On demand. Prereq.: Permission of advisor, and a 3.00 overall GPA. Preparation of thesis under guidance of thesis advisor for students completing master's requirements under M.S. Plan A in Data Mining.

3.000 Credit hours
3.000 Lecture hours

Schedule Types: Thesis

Mathematics Department


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