Graduate-level courses
MSM 567 – Analytics I: Quantitative Methods
This course examines the foundational business analytics skills and tools required to make effective managerial decisions. Specifically, using statistics and probability, this course examines business decisions related to marketing, supply chain, and operations such as market basket analysis, RFM analysis, market segmentation, queuing, quality control, and forecasting consumer demand and input availability, and simple visualization techniques. In addition to Excel, this course incorporates more advanced business analytics software such as SAS Enterprise Miner and Guide.
MSM 568 – Analytics II: Spreadsheet Decision Making
This course is designed to provide students, primarily in the fields of business and economics, with a conceptual understanding of the role management science plays in the decision making process. This course focuses on the development of decision models and their application to management problems. The emphasis is on models that are widely used across a wide variety of industries and functional areas, including operations, supply chain management, finance, accounting, and marketing.
MSM 569 – Analytics III: Business Data Mining
This course will examine how data analysis technologies can be used to improve decision making and study the principles and techniques of data mining. We will examine real-world examples and cases to place data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science. In addition, we will work “hands-on” with data mining software.
MSM 566 – Analytics IV: Programming Basics
Students learn to apply statistical techniques to the processing, manipulation, and interpretation of data from various industries and disciplines using statistical programming packages. Students will use statistical programming to manage data, write subroutines, make informative graphs, and apply advanced modeling techniques to build and evaluate models. This is a project-based course with a strong programming component.
MSM 596 – Analytics V: Analytics Practicum
The purpose of the course is to provide students a real-world hands-on opportunity to work directly with an outside organization in data analytics related activities. Working in groups or individually, students will work under the supervision of a faculty member with an outside organization to help them manage and analyze their data within the organization. Students will review background information on the organization and its strategic objectives and will then interview management to better understand the objectives of the data analytics project and how it aligns with the organizational strategy. The students will develop data analytics models and work with the organization to apply those analytics models to determine the best decision action to take based on the available data.
COM 572 – SEO, Analytics and Social Media
This course develops the ability to use content types, content quality and presentation strategically to engage audiences in online and mobile media. A combination of hands-on assignments, lectures and experiments are used to develop skills with current tools and prepare to learn emerging tools throughout the career.
Undergraduate-level courses
MGT 411 – Process, Systems and Technology
The main objective of this course is to help students acquire a deeper understanding of how systems and technologies are strategically selected, designed, developed and implemented to address the innovative forces driving the transformation of today’s business organizations. The course looks at case studies and projects involving business processes including ERP systems. The course will cover systems and technology applications in different areas of business including marketing, finance, operations and logistics that future business managers need to be exposed to.
MGT 425 – Analytics for Business Strategy
This course demonstrates how business analytics is strategically used by business organizations to gain competitive advantage in today’s agile and intelligence-driven organizations. This course introduces students to various business analytics applications, cases and software tools to help understand, interpret, and visualize business data and valuable patterns in big data. By intelligently analyzing business data, business managers can gain a deeper and more accurate understanding of customers’ needs, the competitive business environment, and various aspects of the business operations. The new field of business analytics can effectively help organizations improve profit margins, revenue, communication, customer satisfaction, service, sustainability, operational efficiency, and lower the cost of doing business.
MGT 426 – Data Mining for Managerial Decision Making
This course will change the way you think about data and its role in business. Businesses, governments, and individuals create massive collections of data as a byproduct of their activity. Increasingly, decision-makers and systems rely on intelligent technology to analyze data systematically to improve decision-making. Automating analytical and decision-making processes is often necessary because of the volume of data and the speed with which new data are generated. We examine how data analysis technologies can be used to improve decision making and study the principles and techniques of data mining, and we will examine real-world examples and cases to place data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science. In addition, we will work “hands-on” with data mining software.
CSC 111 – Data Science and Visualization
The Internet is full of rich data sources that anyone can use to answer questions and solve problems. How can we process this data to uncover interesting patterns? How can we visualize this data to reveal trends or to spur additional questions? This course teaches students how to access online data, write programs to analyze the data, and use visualization tools to describe the patterns we find in a compelling way. Students of any major are welcome.
CSC 301 – Fundamentals of Database Management and Analysis
This course focuses on designing, implementing and using database systems, with emphasis on relational and object-relational models. Students design and deploy relational database models using commercial database management tools. Students will learn database query languages and will be able to design complex reports and queries to answer domain problems. This course also introduces and implements modern non-relational database systems such as key-value stores and graph databases.
CSC 320 – Data-driven Web Development
This course introduces concepts and methodologies to design and implement dynamic front-end Web applications. Students will design and create complex and interactive Web sites using industry-standard front-end technologies. In addition to the substantial programming foundations developed in this course, other topics will also include information architecture, testing, multimedia integration, browser compatibility, Web standards, and site maintenance.
CSC 401 – Data Mining and Analytics
This course provides an introduction to the concepts of data mining and machine learning. Students will be introduced to the many steps in the data mining process including: collection, cleaning, aggregation, transformation, mining and analysis, evaluation, presentation. Analysis techniques include: decision trees, clustering, Bayesian classifiers, text mining, association rules, social network analysis.
COM 260 – Understanding Audiences
Engaging with audiences is a bedrock practice for media organizations. This course explores the complex relationships between the producers of media messages and their audiences, users and participants. The course probes theories, past and present, to understand the communication process.
COM 319 – Communicating Media Insights
Writing is a central component for effectively communicating media research. Through research reports, policy briefs and executive summaries, students develop writing skills to report media research and create media messages. Topics include communicating online and social media measurement procedures, the relationship between words and data, and recommendations for effective decision-making.
COM 329 – Applied Media Analytics
Media organizations rely on analytics to measure their audiences and the use of media content. The course highlights traditional performance indicators such as newspaper circulation and broadcast audience estimates, as well as metrics for emerging media such as websites, blogs, social media and mobile media. Students learn concepts, issues, analytical tools, procedures, data visualization, and ethical responsibilities when using data.
COM 359 – Strategies for Emerging Media
Emerging media challenge the definition and measurement of audiences, users and participants. In this course, students confront the realities of analyzing and interpreting metrics to guide decision-making in competitive media environments. Strategies may include social media monitoring, targeted and customized messaging, forecasting, search engine optimization, and utilizing loyal followers and paid media.
COM 460 – Measuring Media Impact
Students apply techniques to measure media impact for real-world clients and develop effective strategies. In the course, students use commercial and open-source tools for audience measurement, develop business models reflecting the strategic positioning of clients, and engage audiences using social, mobile and other media platforms. Capstone course in the Media Analytics major.
STS 212 – Statistics in Application
An introduction to concepts in statistics at a deeper quantitative level than that offered in MTH 110. This course emphasizes rationales, applications and interpretations using advanced statistical software. Examples are drawn primarily from economics, education, psychology, sociology, political science, biology and medicine. Topics include introductory design of experiments, data acquisition, graphical exploration and presentation, descriptive statistics, one- and two-sample inferential techniques, simple/multiple regression, goodness of fit and independence, one-way/two-way analysis of variance (ANOVA). Written reports link statistical theory and practice with communication of results. Recommended for students pursuing quantitatively-based careers.
STS 213 – Survey Sampling Methods
An introduction to the concepts and methods of statistical reasoning associated with sample surveys. This course emphasizes rationales, applications and interpretations of sampling strategies used for estimation. Advanced statistical software such as SAS or SPlus may be used. Case studies of survey methods are drawn primarily from the social sciences while field sampling applications to ecological and environmental research may be used. Topics include survey design issues, simple random sampling, stratified sampling, single- and two-stage cluster sampling, systematic sampling, parameter estimation and sample size calculation. Written reports link statistical theory and practice with communication of results.
STS 232 – Statistical Modeling
This course emphasizes rationales, applications and interpretations of regression methods using a case study approach. Advanced statistical software such as SAS or SPlus may be used. Topics include simple linear regression, multiple linear regression, indicator variables, robustness, influence diagnostics, model selection, logistic regression for dichotomous response variables and binomial counts and non-linear regression models. Written reports link statistical theory and practice with communication of results.
STS 256 – Applied Nonparametric Statistics
This course focuses on data-oriented approaches to statistical estimation and inference using techniques that do not depend on the distribution of the variable(s) being assessed. Topics include classical rank-based methods, as well as modern tools such as permutation tests and bootstrap methods. Advanced statistical software such as SAS or SPlus may be used, and written reports will link statistical theory and practice with communication of results.
STS 325 – Design and Analysis of Experiments
This course explores methods of designing and analyzing scientific experiments to address research questions. Emphasis is placed on statistical thinking and applications using real data, as well as on the underlying mathematical structures and theory. Topics include completely randomized designs, randomized block designs, factorial treatment designs, hierarchical designs, split-plot designs and analysis of covariance. Advanced statistical software such as SAS or SPlus may be used, and written reports will link statistical theory and practice with communication of results.
STS 327 – Statistical Computing for Simulation and Theory
An intermediary course in statistical computing using SAS software. This course emphasizes issues with messy data entry, data management, macro writing and analysis using SAS software. Topics include using SAS software to read in large data, sub-setting data, merging data sets, graphical descriptive statistics, numerical descriptive statistics, macros, and standard statistical analysis for students pursuing a statistics major or minor.