Curriculum

Foundations

BMIS 326 - Introduction to Data Analytics.  This course introduces the terminology and application of big data and data analytics. Students will complete cases in a variety of disciplines as they become acquainted with some of the software, tools, and techniques of data analytics.

STAT 451 – Statistical Methods I. Intended primarily for non-mathematics majors who will be analyzing data. Graphical and numerical summaries of data, elementary sampling, designing experiments, probability as a model for random phenomena and as a tool for making statistical inferences, random variables, basic ideas of inference and hypothesis testing.

BMKT 560  Marketing & Stats.  (can also be taken as Stats 216 - Introduction to Statistics and BMKT 325 - Marketing Principles) Introduction to marketing principles that create long-term competitive advantage for an organization. Topics include environmental analysis, marketing planning, segmentation analysis, target marketing, and planning for product, price, promotion and distribution. Business statistics covered including t-tests, analysis of variance, regression and correlation analysis; statistics applications in context of marketing research and marketing problems.

Internship or work experience

Required Courses (15 credits)

BMIS 601 – Business Intelligence.  This course intends to provide graduate students with the foundational knowledge necessary to transform big data into useful business intelligence. The course will provide students with the skills, tools, and techniques required to collect, synthesize, and distribute information to support intelligent decision-making at the managerial level.

BMKT 670 - Applied Data Analytics.  This course applies statistical skills and technical expertise to real-world big-data business applications. Students will work with the tools of data science and hone their ability to answer business questions through the analysis of data. 

BMKT 642 - Advanced Marketing Research.  The purpose of the course is to learn how to provide information for better business decision making. Students study the different aspects of marketing research as it relates to business problems and develop a mindset that continually relies on information-based decisions.

BMIS 694 – Text Mining of Unstructured Data.  An integration of data science theory and the actual practice of searching, sorting, relating, and deriving results from textual data.  Students will be exposed to machine learning, natural language processing, as well as other computer assisted data mining techniques and then gain hands-on proficiency in the practice of data science using the software from data mining and document analysis vendors.

BMIS 650 – Quantitative Analysis.  Quantitative methods supporting managerial decision-making. Theory and logic underlying such methods as linear programming and simulation. Solution of complex problems and practice of interpersonal skills in team projects. 

BMKT 591 - Big Data and Innovation.  The course provides an integrative, capstone experience for students to reflect on and apply the data science tools they have learned in the Master of Business Analytics program. In addition, this course will focus on the innovation and creativity aspects of big data, or how big data can unleash new insights and innovations that solve customer and societal problems. The course will train future managers to think strategically and innovatively—about data, about opportunity, about value. It will ensure that students are proficient in strategy, customer value and insights. Students engage in a capstone professional paper/project.

Electives (17 Credits) 

Electives are not limited to courses provided below.

BMKT 420 – Integrated Online Marketing.  Exploration and application of marketing communications principles to the internet environment. Students develop individual WordPress websites/blogs, learn about online marketing techniques, and complete online marketing and social media projects.

CSCI 444 – Data Visualization. Visualization fundamentals and applications using special visualization software; formulation of 3-D empirical models; translation of 3-D models into graphical displays; time sequences and pseudo-animation; interactive versus presentation techniques; special techniques for video, CD and other media.

CSCI 564 – Applications of Mining Big Data.  Introduction to existing data mining software systems and their use, with focus on practical exercises. Topics include data acquisition, data cleansing, feature selection, and data analysis. Credit not allowed for both CSCI 464 and CSCI 564.

CSCI 448 - Pattern Recognition. Introduction to the framework of unsupervised learning techniques such as clustering (agglomerative, fuzzy, graph theory based, etc.), multivariate analysis approaches (PCA, MDS, LDA, etc.), image analysis (edge detection, etc.), as well as feature selection and generation. Emphasis will be on the underlying algorithms and their implementation.

JRNL 414 - Investigations.  Introduction to methods and ethics of investigative reporting, emphasizing computer-assisted research and analysis of public records and databases.

MART 500 – Digital Tech in the Arts I. This course explores the relationship between aesthetics and the emerging capabilities of digital technology. It will cover the historical relationship between science and art up to the end of the 20th century and examine the methodology of critical artistic applications.

BMIS 575 - Introduction to Consulting.  Managerial approach to consulting engagements. Includes scoping and writing proposals, presenting to clients, documenting consulting work, and interpersonal skills necessary for successful consulting. Course does not require a technical background.

BMIS 674 - Mgmt of Information Systems. The tactical/operational responsibilities and roles of the CIO. Includes governance issues, supporting the learning organization, managing the technologies, and managing the development of systems. Focuses on management; does not require a technical background.

BMIS 465 - Introduction to Real-time Data Analytics. Focuses on analyzing big data in motion using commercially available software.

BMIS 471 - Fundamentals of Network & Security Management. Current topics will focus on the impact of network technologies and infrastructures on facilitating and supporting business organizations. Students learn about design, installation, and configuration of networks as well as implementing security, networking protocols, and virtualization technologies.

BMIS 472 - Advanced Network & Security Management. Focuses on network security and how it aligns with organizational strategy, directory services for access to organizational information, and cybersecurity management.

BMIS 478 - E Commerce: A Managerial Perspective. Focuses on the capabilities of the Internet to support and enable commerce. Provides a managerial perspective on topics including effective web site design, emerging technologies, business models, infrastructure architectures, and security.

STAT 452 - Statistical Methods II. Multiple regression, experimental design, analysis of variance, other statistical models.

MATH 461 - Practical Big Data Analytics. The course provides the students with the essential tools for the analysis of big data. The content consists of data dictionaries and data mappings, distributed computing, and related methods. Other topics may include data visualization, regression, and cluster analysis.