In this course, students continue not only to learn how to identify and collect digital evidence through forensics search tools, but also to study the emerging data mining techniques. The topics include how to design a plan for a computer crime investigation; how to select a computer software tool to perform the investigation; how to articulate the laws applying to the appropriation of computers for forensics analysis; how to verify the integrity of the evidence being obtained; how to prepare the evidence collected for the use in the court; and how to present the evidence as an expert eyewitness in court. Some hypothetical and real cases are also discussed in class.

Full course description for Digital Evidence Analysis

The purpose of this course is to introduce students to the fundamental concepts and techniques of production and operations management for both service and manufacturing organizations. It will address the role of operations in relation to other functions and the methods to increase organizational effectiveness and efficiency. Topics covered include: product and service design, capacity planning, design of work systems, location planning and analysis, material requirements planning, supply-chain management, enterprise resource planning, inventory management, total quality management, Six Sigma, lean enterprise and kaizen approaches, aggregate planning, just-in-time systems, scheduling, and project planning. Also included are tools and processes used in operations decisions such as forecasting, breakeven analysis, and critical path method using available software.

Full course description for Introduction to Operations Management

This course prepares students for the task of analyzing primary and secondary economic data in order to assist decision makers in profit, nonprofit and public organizations. It also provides an introduction to econometrics: regression models, serial correlation, forecasting, simultaneous equation estimation, model building, time series and simulations. Students work on a major project during the course.

Full course description for Economic Research and Forecasting

Covers concepts and methods in the definition, creation and management of databases. Emphasis is placed on usage of appropriate methods and tools to design and implement databases to meet identified business needs. Topics include conceptual, logical and physical database design theories and techniques, such as use of Entity Relationship diagrams, query tools and SQL; responsibilities of data and database administrators; database integrity, security and privacy; and current and emerging trends. Use of database management systems such as MySQL. Coverage of HCI (Human Computer Interaction) topics. Development of GUI front ends to databases with application of HCI principles to provide a high level usability experience. Overlap: ICS 311T Database Management Systems.

Full course description for Database Management Systems

This is a calculus-based probability course. It covers the following topics. (1) General Probability: set notation and basic elements of probability, combinatorial probability, conditional probability and independent events, and Bayes Theorem. (2) Single-Variable Probability: binomial, geometric, hypergeometric, Poisson, uniform, exponential, gamma and normal distributions, cumulative distribution functions, mean, variance and standard deviation, moments and moment-generating functions, and Chebysheff Theorem. (3) Multi-Variable Probability: joint probability functions and joint density functions, joint cumulative distribution functions, central limit theorem, conditional and marginal probability, moments and moment-generating functions, variance, covariance and correlation, and transformations. (4) Application to problems in medical testing, insurance, political survey, social inequity, gaming, and other fields of interest.

Full course description for Probability

Competence in management and use of organizational and external databases is a skill needed by all business people and critical to management information systems effectiveness, especially in the new era of "big data". This course teaches the development and accessing of internal and external information resources. Topics include: ensuring the availability of appropriate data; interrelating and applying data to typical business problems; normalized database design; protecting and managing information resources; scalability; and compatibility issues.

Full course description for Management and Use of Databases

Business Intelligence is the user-centered process of exploring data, data relationships and trends - thus helping to improve overall decision making for enterprises. This course addresses the iterative processes of accessing data (ideally stored in the enterprise data warehouse) and analyzing data in order to derive insights and communicate findings. Moreover, the course also addresses the use of software tools for analysis and visualization of data, especially report design along with the use of dashboards.

Full course description for Business Intelligence and Analytics

This course examines the processes and techniques used in gathering, analyzing and reporting information that forms the basis for managerial and marketing decision making. The course content includes the study of both secondary research methods and primary research methods, with the emphasis on survey methods. There is a strong statistical analysis component, and students learn to use SPSS, statistical software used extensively in organizations that perform quantitative research. Students design and implement a marketing research study.

Full course description for Marketing Research

This course covers fundamental and intermediate topics in biostatistics, and builds on the ideas of hypothesis testing learned in STAT 201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use SPSS to do the analyses. Topics include designing studies in biostatistics, ANOVA, correlation, linear regression, survival analysis, categorical data analysis, logistic regression, nonparametric statistical methods, and issues in the analysis of clinical trials.

Full course description for Biostatistics

This course covers the fundamental to intermediate ideas of nonparametric statistical analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include nonparametric methods for paired data, Wilcoxon Rank-Sum Tests, Kruskal-Wallis Tests, goodness-of-fit tests, nonparametric linear correlation and regression. Completion of STAT201 (Statistics I) is a prerequisite for this course.

Full course description for Nonparametric Statistical Methods

This course covers the fundamental to intermediate ideas of the statistical analysis of categorical data. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include analysis of 2x2 tables, stratified categorical analyses, estimation of odds ratios, analysis of general two-way and three-way tables, probit analysis, and analysis of loglinear models. Completion of STAT201 (Statistics I) is a prerequisite.

Full course description for Analysis of Categorical Data

This course covers the intermediate statistical methods in analyzing environmental and biological datasets. This course is built on the knowledge of an introductory statistics and hypothesis testing. The contents of the course include paired T-test, unpaired T-test, F-tests, one-way and two-way ANOVA, multivariate ANOVA, repeated measures, regression, principle component analysis and cluster analysis. Students will learn how to use statistical software to perform all the analyses. This course covers the intermediate statistical methods in analyzing environmental and biological datasets. This course is built on the knowledge of an introductory statistics and hypothesis testing. The contents of the course include paired T-test, unpaired T-test, F-tests, one-way and two-way ANOVA, multivariate ANOVA, repeated measures, regression, principle component analysis and cluster analysis. Students will learn how to use statistical software to perform all the analyses.

Full course description for Environmental Statistics