Master of Science in Business Analytics
Business Analytics refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business Analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. A variety of industries are in need of indivduals who can take on positions of responsibility for collecting, analyzing and interpreting information in order to make sound strategic business decisions. The MSBA program seeks to fill the talent gap in the area and to prepare graduates for this fast-growing field by developing students' creitical skills in data- and model-driven management decision-making in the context of a firm's strategic vision. The program consists of 10 three-credit courses, and it is structured to be completed either in one year's time (full-time) or over two years (part-time). A Marketing Analytics concentration within the MSBA program provides a unique opportunity to tailor the degree for the needs of the marketing profession (e.g., brand management/product development, digital marketing, marketing research, social media).
|IS 0505||Python for Business Analytics||3|
|IS 0510||Databases for Business Analytics||3|
|IS 0540||Data Mining and Business Intelligence||3|
|OM 0400||Principles of Business Analytics||3|
|OM 0500||Business Model Optimization||3|
|QA 0400||Applied Business Statistics||3|
|QA 0500||Business Forecasting and Predictive Analytics||3|
|Select three elective courses in Business Analytics 1||9|
Students are required to complete an additional 9 credits (3 courses) of graduate work. Students are strongly encouraged to select three courses in a specific discipline: e.g., accounting, finance, management, mathematics, or marketing. Please note that one must have the appropriate prerequisites to complete these additional graduate courses.
Marketing Analytics Concentration
To complete a concentration in Marketing Analytics, students take the following courses as their required electives:
|MK 0520||Marketing Research||3|
|MK 0580||Multivariate Data Analysis for Decision Making||3|
|MK 0585||Seminar: Contemporary Topics in Marketing||3|
|or MK 0590||Experimental Research in Marketing|
Note: MK 0400 or its equivalent is required for all upper-level marketing courses.
MBA and MS in Business Analytics Overlap
The MBA with a concentration in Information Systems/Business Analytics (MBA-IS/BA) is a generalist degree that covers all relevant topical areas for a business professional, and gives the students the opportunity to specialize, but not major, in Information Systems. Immediately after completion of the MBA-IS/BA, students sometimes wish to further their graduate study in Information Systems or Analytics. Interested students then may apply for admission to the MS in Business Analytics and, once accepted, can earn the degree by completing an additional 6 courses drawn from the MS in Business Analytics curriculum. Courses are selected in advisement with the Director of Graduate Programs. Students are encouraged to seek individualized advisement well before completing the MBA-IS/BA.
IS 0500 Information Systems and Database Management3 Credits
This course introduces the basic concepts and tools relevant to information systems and database management, and their enabling roles in business strategies and operations. Case studies are used to facilitate discussions of practical applications and issues involving strategic alignments of organizations, resource allocation, integration, planning, and analysis of cost, benefit and performance in light of the big data challenges. Specific emphases involve database design and implementation and emerging strategies and technologies such as business intelligence, big data management, web security, and online business analytics.
IS 0501 International Information Systems3 Credits
Prerequisite: IS 0500.
This course examines information technology environments around the world, and attendant challenges to business strategy and information systems design. The course identifies geographic and institutional variables that create borders in the global Internet economy: material infrastructures, socio-economic elements, and political-legal systems. The course emphasizes national and regional strategies, emergent technologies, hybrid systems, and equity issues.
IS 0505 Python for Business Analytics3 Credits
In this course, we introduce Python as a language and tool for collecting, preprocessing, and visualizing data for business analytics. since Python is one of the most popular programming languages, along with R, in data mining and business analytics, its fundamental programming logic and knowledge is essential for students to apply in data mining and to succeed in the job market. Specifically, this course focuses on the data-engineering phase, which includes collecting, preprocessing, and visualizing data, with respect to applications in business modeling, optimization, and statistical analysis. In addition, a number of mini projects will be used as vehicles to cover the main applications of data analytics, including recommender systems, text analytics, and web analytics.
IS 0510 Databases for Business Analytics3 Credits
Prerequisite: IS 0505.
This course introduces databases and data management in three parts. The first part covers basic database fundamentals. The second part is a hands-on introduction to Structured Query Language (SQL) for defining, manipulating, accessing, and managing data, accompanied by the basics of data modeling and normalization needed to ensure data integrity. The course concludes with a comprehensive database project that gives each student the opportunity to integrate and apply the new knowledge and skills learned from this class. Advanced topics such as distributed database systems, data services, and NoSQL databases are also discussed.
IS 0520 Project Management3 Credits
This course explores the process and practice of project management. Topics to be covered include project lifecycle and organizations, teambuilding and productivity, task scheduling and resource allocation, and progress tracking and control. Cases will be used to consider the implications for change management, consulting, IT implementation, and other related disciplines. Small team projects and experiential exercises will also be used to provide an active learning environment. This course is designed to count toward professional project management certification.
IS 0540 Data Mining and Business Intelligence3 Credits
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, managers rely on intelligent technology to systematically analyze data to improve their decision-making. In many cases, automating analytical and decision-making processes is necessary because of the large volume of data and the speed with which new data are generated. In this course, we will examine how data analysis technologies can be used to improve managerial decision making. We will study the fundamental principles and techniques of data mining through real-world examples and cases to place data mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application of these techniques is as much an art as it is a science. In addition, we will work "hands-on" with contemporary data mining software.
IS 0550 Business Analytics and Big Data Management3 Credits
Prerequisite: IS 0540.
This course will survey state-of-the-art topics in Big Data, looking at data collection (via smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured databases, systems issues (exploiting multicore, security), analytics (machine learning, data compression, efficient algorithms), data visualization, and a range of applications. Each of these five modules will introduce broad concepts as well as provide the most recent developments in the area.
IS 0585 Contemporary Topics in Information Systems and Operations Management3 Credits
Prerequisite: IS 0500.
This course draws from current literature and practice on information systems and/or operations management. The topics change from semester to semester, depending on student and faculty interest and may include: project management, e-business, management science with spreadsheets, e-procurement, executive information systems, ethics, and other socio-economic factors in the use of information technology.
IS 0585B Contemporary Topics: Advanced Data Mining Applications3 Credits
IS 0598 Independent Study in Information Systems and Operations Management3 Credits
This course provides an opportunity for students to complete a project or perform research under the direction of an Information Systems and Operations Management (ISOM) faculty member who has expertise in the topic being investigated. Students are expected to complete a significant project or research paper as the primary requirement of this course. Enrollment by permission of the ISOM Department Chair only.
OM 0400 Principles of Business Analytics3 Credits
This course introduces basic concepts and tools relevant to operations and supply chain management, including process mapping, quality management, decision analysis, capacity planning, supply chain management, project management, and operations strategy. Case studies are used to link the concepts and models to real-world business applications.
OM 0500 Business Model Optimization3 Credits
Prerequisite: OM 0400.
This course introduces basic skills necessary for business analytics such as data analysis using basic statistics, data visualization and summarization, descriptive and inferential statistics, spreadsheet modeling for prediction, linear regression, risk analysis using Monte-Carlo simulation, linear and nonlinear optimization, and decision analysis. Microsoft Excel is used as the platform for conducting analyses and performing statistical calculations.
OM 0525 Process Improvement and Quality Management3 Credits
This course addresses topics and methods related to business process improvement and lean six-sigma quality management so that firms are able to improve their performance along key dimensions such as cost, quality, speed, and flexibility. Through the use of case studies, students learn to approach problems using methods that have proven effective for a variety of organizations. Topics include: financial justification of operational improvements, change management, six-sigma process improvement methods and tools, business process reengineering, and lean production concepts applied in both manufacturing and service organizations. This course will also reinforce skills involved in working in teams and communicating recommendations effectively.
OM 0535 Global Logistics and Supply Chain Management3 Credits
This course emphasizes global logistics as the management of time and place. It takes an integrated cross-functional management approach using strategic infrastructure and resource management to efficiently create customer value. Specifically, it examines the time-related global positioning of resources and the strategic management of the total supply-chain. Topics include procurement, manufacturing, distribution, and waste disposal, and discussion of associated transport, storage, and information technologies.
QA 0400 Applied Business Statistics3 Credits
Using spreadsheet software, this hands-on course teaches a variety of quantitative methods for analyzing data to help make decisions. Topics include: data presentation and communication, probability distributions, sampling, hypothesis testing and regression, and time series analysis. This course uses numerous case studies and examples from finance, marketing, operations, accounting, and other areas of business to illustrate the realistic use of statistical methods.
QA 0500 Business Forecasting and Predictive Analytics3 Credits
Prerequisite: QA 0400.
This course introduces analytical techniques used for decision-making under uncertainty. Topics include time series and other forecasting techniques, such as Monte Carlo simulation, to assess the risk associated with managerial decisions. Specifically, we will cover data collection methods, time dependent models and analysis, advanced solver, time series techniques, exponential smoothing, moving averages, and Box-Jenkins (ARIMA) models. Application examples include financial models - stock prices, risk management - bond ratings, behavior models - customer attrition, customer likes/dislikes, buying patterns - propensity to buy, politics - identify swing voters, and sales.