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Online Certificate - Course Requirements and Descriptions

Certificate students must complete four 3-unit courses. The only required course is STAT 566 - Theory of Statistics (offered in the Spring). However, STAT 564 - Theory of Probability is a prerequisite for STAT 566, so students must have completed either STAT 564 or an equivalent course before taking STAT 566.

A note about course offerings: Because of how courses are established at the University of Arizona, our online course offerings are housed in departments separate from the Statistics GIDP. For this reason, we are not able to control the offering schedules of these courses. We do our best to keep information on course offerings up to date, but for questions on when a specific course will be offered, please contact the home department (listed below).

STAT 566, Theory of Statistics (3 units) - REQUIRED

Offered in the spring semester

Sampling theory. Point estimation. Limiting distributions. Testing Hypotheses. Confidence intervals. Large sample methods.

Professor: Helen (Hao) Zhang, PhD, haozhang@email.arizona.edu

Textbook: Statistical Inference, by George Casella and Roger L. Berger, 2nd Edition, 2001

Duxbury, ISBN 0-534-24312-6.

Course requisites: STAT/MATH 564

Home department contact: Mathematics, Tina Deemer: deemer@math.arizona.edu

 

STAT 564, Theory of Probability (3 units)

Offered in the fall semester

Probability spaces, random variables, weak law of large numbers, central limit theorem, various discrete and continuous probability distributions. Graduate-level requirements include more extensive problem sets or advanced projects.

Professor: Selena (Yue) Niu, PhD, yueniu@math.arizona.edu

Textbook: Statistical Inference, by George Casella and Roger L. Berger, 2nd Edition, 2001. Duxbury, ISBN 0-534-24312-6.

Course requisites: Calculus through multivariable/vector calculus (at the level of MATH 125, MATH 129, MATH 223).

Home department contact: Mathematics, Tina Deemer: deemer@math.arizona.edu

 

STAT 571A, Advanced Statistical Regression Analysis (3 units)

Offered in the fall semester

Regression analysis including simple linear regression and multiple linear regression. Matrix formulation and analysis of variance for regression models. Residual analysis, transformations, regression diagnostics, multicollinearity, variable selection techniques, and response surfaces. Students will be expected to utilize standard statistical software packages for computational purposes.

Professor: Walter Piegorsch, PhD, piegorsch@math.arizona.edu

Textbook: Kutner, M.H., Nachtsheim, C.J., and Neter, J. (2004). Applied Linear Regression

Models, 4th Edn. Boston: McGraw-Hill Irwin. ISBN: 978-0-07-301344-2.

Course requisites: MATH 410 or MATH 413, or equivalent; MATH 461 or MATH 466, or equivalent.

Home department contact: Mathematics, Tina Deemer: deemer@math.arizona.edu

 

STAT 571B, Design of Experiments (3 units)

Offered in the spring semester

Principles of designing experiments. Randomization, block designs, factorial experiments, response surface designs, repeated measures, analysis of contrasts, multiple comparisons, analysis of variance and covariance, variance components analysis.

Professor: Lingling An, PhD, anling@email.arizona.edu

Textbook: Design and Analysis of Experiments by Montgomery (8th Edition)

Course requisites: MATH 223 or equivalent; STAT/MATH 571A.

Home department contact: Mathematics, Tina Deemer: deemer@math.arizona.edu

 

BIOS 576B, Biostatistics for Research (3 units)

Offered in the spring semester

Descriptive statistics and statistical inference relevant to biomedical research, including data analysis, regression and correlation analysis, analysis of variance, survival analysis, biological assay, statistical methods for epidemiology and statistical evaluation of clinical literature.

Professor: Denise Roe, PhD, droe@email.arizona.edu

Handbook: None

Course requisites: None

Home department contact: Biostatistics & Epidemiology, Laura Shriver: shriverl@email.arizona.edu  

 

ECE 639, Detection and Estimation in Engineering Systems (3 units)

Course not currently available – between professors

Communication, detection and estimation as statistical inference problems. Optimal detection in the presence of Gaussian noise. Extraction of signals in noise via MAP and MMSE techniques.

Professor: Currently unavailable

Course requisites: ECE 503

Home department contact: Electrical & Computer Engineering, Tami Wehlan: whelan@ece.arizona.edu

 

MIS 545, Data Mining for Business Intelligence (3 units)

Offered in the fall and spring semesters

Corporations today are said to be data rich but information poor. For example, retailers can easily process and capture millions of transactions every day. In addition, the widespread proliferation of economic activity on the Internet leaves behind a rich trail of micro-level data on consumers, their purchases, retailers and their offerings, auction bidding, music sharing, so on and so forth. Data mining techniques can help companies discover knowledge and acquire business intelligence from these massive datasets. This course will cover data mining for business intelligence. Data mining refers to extracting or "mining" knowledge from large amounts of data. It consists of several techniques that aim at discovering rich and interesting patterns that can bring value or "business intelligence" to organizations. Examples of such patterns include fraud detection, consumer behavior, and credit approval. The course will cover the most important data mining techniques --- classification, clustering, association rule mining, visualization, prediction --- through a hands-on approach using XL Miner and other specialized software, such as the open-source WEKA software.

Professor: Bin Zhang, PhD, binzhang@email.arizona.edu

Course requisites: None

Home department contact: Management Information Systems, Mona Lopez, mllopez@email.arizona.edu

 

NURS 646 - Healthcare Informatics: Theory and Practice (3 units)

Offered in the spring semester

Focuses on the theoretical basis of healthcare informatics with an emphasis on management and processing of healthcare data, information, and knowledge. Healthcare vocabulary and language systems, and basic database design concepts are addressed.

Professors:      Mary Davis, PhD, mdoyle6@email.arizona.edu

                        Nicolette Estrada, PhD,  estradan@email.arizona.edu 

Course requisites: None

Home department contact: College of Nursing: entry@email.arizona.edu

 

SIE 520 - Stochastic Modeling I (3 units)

Offered in the spring semester

Modeling of stochastic processes from an applied viewpoint. Markov chains in discrete and continuous time, renewal theory, applications to engineering processes.

Professor: Pavlo Krokhmal, PhD, krokhmal@email.arizona.edu

Course requisites: SIE 321

Home department contact: Systems & Industrial Engineering, Linda Cramer: graduateadvisor@sie.arizona.edu

 

SIE 522 - Engineering Decision Making Under Uncertainty (3 units)

Offered in the fall semester

Application of principles of probability and statistics to the design and control of engineering systems in a random or uncertain environment. Emphasis is placed on Bayesian decision analysis. Graduate-level requirements include a semester research project.

Professor: Donald Bruyere, PhD, dbruyere@email.arizona.edu

Course requisites: None

Home department contact: Systems & Industrial Engineering, Linda Cramer: graduateadvisor@sie.arizona.edu

 

SIE 525 - Queuing Theory (3 units)

Course not currently available – between professors

Application of the theory of stochastic processes to queuing phenomena; introduction to semi-Markov processes; steady-state analysis of birth-death, Markovian, and general single- and multiple-channel queuing systems.

Professor: Currently unavailable

Course requisites: None

Home department contact: Systems & Industrial Engineering, Linda Cramer: graduateadvisor@sie.arizona.edu

 

SIE 531 - Simulation Modeling and Analysis (3 units)

Offered in the fall and spring semesters

Discrete event simulation, model development, statistical design and analysis of simulation experiments, variance reduction, random variate generation, Monte Carlo simulation. Graduate-level requirements include a library research report.

Professor: Wei Lin, PhD, weilin@sie.arizona.edu

Course requisites: None

Home department contact: Systems & Industrial Engineering, Linda Cramer: graduateadvisor@sie.arizona.edu

 

SIE 536 - Experiment Design and Regression (3 units)

Course not currently available – between professors

Planning and designing experiments with an emphasis on factorial layout. Includes analysis of experimental and observational data with multiple linear regression and analysis of variance.

Professor: Currently unavailable

Course requisites: SIE 530

Home department contact: Systems & Industrial Engineering, Linda Cramer: graduateadvisor@sie.arizona.edu

 

SIE 545 - Fundamentals of Optimization (3 units)

Offered in the fall semester

Unconstrained and constrained optimization problems from a numerical standpoint. Topics include variable metric methods, optimality conditions, quadratic programming, penalty and barrier function methods, interior point methods, successive quadratic programming methods.

Professor: Jianqiang Cheng, PhD, jqcheng@email.arizona.edu

Course requisites: SIE 340

Home department contact: Systems & Industrial Engineering, Linda Cramer: graduateadvisor@sie.arizona.edu

 

SIE 606 - Advanced Quality Engineering (3 units)

Offered in the spring semester

Advanced techniques for statistical quality assurance, including multivariate statistical inference, multiple regression, multivariate control charting, principal components analysis, factor analysis, multivariate statistical analysis for process fault diagnosis, and select papers from the recent literature.

Professor: Jian Liu, PhD, jianliu@email.arizona.edu

Course requisites: SIE 530, SIE 506

Home department contact: Systems & Industrial Engineering, Linda Cramer: graduateadvisor@sie.arizona.edu

 

Coming Soon: MATH 574M, Statistical Machine Learning (3 units)

Offered in class only. Coming Soon for online students

Basic statistical principles and theory for modern machine learning, high dimensional data analysis, parametric and nonparametric methods, sparse analysis, shrinkage methods, variable selection, model assessment, model averaging, kernel methods, and unsupervised learning.

Professor: Helen (Hao) Zhang, PhD, haozhang@email.arizona.edu

Course requisites: Probability at the level of MATH 464, statistics at the level of MATH 363 or MATH 466, and linear algebra.

Home department contact: Mathematics, Tina Deemer: deemer@math.arizona.edu

 

Prerequisite Course Policy

Prerequisite courses necessary to undertake a course chosen for the Graduate Certificate are the responsibility of the student and may only count towards the Certificate if they are already listed as a Core Course or as Elective Courses.

 

Last updated 12 May 2017