Master of Science in Business Analytics Robert C. Vackar College of Business & Entrepreneurship

Required Courses (24 credit hours)

• QUMT 6303 — Statistical Foundations
• INFS 6351 — Developing Customized Solutions for Business Analytics
• QUMT 6350 — Machine Learning for Business Analytics
• INFS 6353 — Social Media Analytics
• INFS 6350 — Business Intelligence and Data Warehousing
• QUMT 6360 — Decision Optimization for Business Analytics
• INFS 6359 — Data mining for Business Analytics
• INFS 6356 — Data Visualization

MSBA Electives (9 credit hours) choose from the following:

• INFS 6333 — Spreadsheet Modeling for Service Industries
• INFS 6363 — Enterprise Analytics
• INFS 6342 — Managing Healthcare Data
• INFS 6343 — Healthcare Analytics
• INFS 6340 — Health Computer Information Systems
• INFS 6391 — Information Security and Risk Assessment Analysis
• QUMT 6370 — Big Data Analytics

Total graduate hours for degree:  33 hours

Course Descriptions

1. QUMT 6303 — Statistical Foundations

   An introduction to statistical methodology to include probability concepts, inference techniques, analysis of variance, regression analysis, chi square and other non-parametric analyses. This course focuses on the use of the computer in performing statistical analysis.

2. INFS 6350 — Business Intelligence and Data Warehousing

   This course discusses the process of business analytics by developing a business intelligence solution, including problem definition, data preparation, descriptive and predictive analyses, evaluation of results, implementation and deployment. Data-oriented methods using spreadsheet and structured query language (SQL) are emphasized for business transaction capturing, data aggregation and online analytic processing (OLAP). Students will employ a variety of software tools in the development of a data warehouse, including ETL (extraction, transformation and loading) and visual data representations (e.g., data cubes).

3. INFS 6351 — Developing Customized Solutions for Business Analytics

   Novel problems require innovative solutions - this course introduces students to the power and flexibility of programming and scripting languages such as R and Python, applied to problems in business analytics. Students will learn how to acquire and deploy software packages relevant to their problem, then use them together with tools such as SQL to collect and prepare data, customized analyses according to specific needs, and create outputs which effectively communicate the results.

4. INFS 6353 — Social Media Analytics (New)

   This course introduces students to the concept of social media analytics and techniques used to analyze social media data such as texts, networks, and actions. Students will learn how to extract data from popular social media platforms and analyze such data using software tools such as R to identify trends, sentiment, opinion leaders and communities.

5. INFS 6356 — Data Visualization

   This course introduces students to data visualization and dashboarding. Students will learn best practices in data visualization, data retrieval using structured query language (SQL), polish analytical skills, and learn how to design dashboards to support managerial decisions. Students will have the opportunity to gain hands-on experience in data retrieval and visualization. Students will use Tableau as their main tool for data visualization and dashboarding but will develop transferable skills which can apply to most common software packages in the field.

6. INFS 6359 — Data Mining for Business Analytics

   This course provides students with knowledge and skills in the various decision analytical techniques for managerial decision making including big data analytics. A number of well-defined data mining techniques such as classification, estimation, prediction, affinity grouping and clustering, and data visualization will be covered. The Cross Industry Standard Process for Data Mining (CRISP-DM) will also be discussed. The data mining techniques will be applied to diverse business applications including: target marketing, credit risk management, credit scoring, fraud detection, medical informatics, telecommunications and web analytics. Prerequisite: QUMT 6303 or QUMT 3341 or equivalent

7. QUMT 6350 — Machine Learning for Business Analytics

   This course introduces students to modern machine-learning methods that can be applied to build predictive models & discover patterns in data for better-informed business decision-making. Students will learn implementation of the machine learning techniques in R programming language for understanding complex datasets. This course will enable students to approach business problems by identifying opportunities to derive business value from data-driven business intelligence. Prerequisites: QUMT 6303 or QUMT 3341 or equivalent

8. QUMT 6360 — Decision Optimization for Business Analytics (New)

   This course introduces students to various prescriptive analytic techniques and tools that can be used to analyze business decision problems and create business value. These provide business entities and policy makers with fundamental rationality in evaluating performance, making decisions, designing strategies, and managing risk. Students will learn how to use dashboards and analytical models to evaluate uncertainty that is prevalent in many business decisions. The hands-on learning experience with analytical packages and modeling software such as Excel solver, Tableau and R programming will be extensively used throughout the course.  Topics may include deploying analytics such as aggregate planning models and complex problem solving.  The advanced Tableau usage with R implementation will be introduced in this course. The emphasis is on how to employ these analytical methods to facilitate managerial decision-making in diverse industries and functional areas.

   Elective courses (9 credits)

9. INFS 6333 — Spreadsheet Modeling for Service Industries
   

   This course focuses on spreadsheet modeling to support decision making by organizations in service industries, such as healthcare, banking, distribution, and education. Students develop critical thinking and problem solving skills to address real-world problems. The spreadsheet modeling capability acquired is highly practical for managers and administrators. Course topics cover display charts, data exploration. decision-making logic, reference functions, financial impact of loans and investments, project management, what-if analysis, goal seek. visual basic programming, and other advanced tools.

10. INFS 6340 — Health Computer Information Systems

    This course provides the knowledge about fundamentals of health information systems (HIS) and the role of information systems in efficient operation of healthcare organizations. The course specifically focuses on: evolution of HIS, HIS components and basic HIS functions, technology infrastructure for healthcare organizations, basic concepts such as EHR, HIE, CPOE, and COSS, HIS standards such as HIPAA, HL7, and DICOM, strategic information systems planning for healthcare organizations, systems analysis and project management, information security issues, and role of HIS professionals in health organizations.

11. INFS 6342 — Managing Healthcare Data

    This course seeks to address the second objective of the MSBA program, "Students will use contemporary information systems and analytical tools to acquire and manage data for business analytics." By completing this course, the students will build skills to acquire, create, examine, and manage healthcare data. The course introduces students to contemporary sophisticated data management and analytics software that is most used by the healthcare industry. The students will develop competency in data formats, data conversion, data export-import, data acquisition and cleansing, data dictionary and data manipulation methods, setting domains, constraints, optimum data types, advanced SQL queries, data visualization, and management of data resources including backups and restore.

12. INFS 6343 — Healthcare Analytics (New)

    This course introduces concepts, techniques, and tools for managing and understanding data in healthcare. The course focuses on teaching students to use healthcare data to make decisions, transform health care delivery, and improve public health. Students will learn how to collect, process, analyze, visualize, and report structured and unstructured clinical and operational data, using software tools and programming languages such as R, Python, and SQL. Topics covered include healthcare data measurement, statistical analysis, and data mining. This course will also discuss challenges related to healthcare analytics such as data privacy, security and interoperability.

13. INFS 6363 — Enterprise Analytics (New)

    This course introduces students to the management and coordination of enterprise data resources to improve enterprise-wide decision-making. Students will learn how to identify key performance indicators from enterprise data, how to differentiate enterprise analytics from other forms of analytics, how to determine what proprietary data will provide analytical advantage to maximize the impact on the enterprise, recent technologies for analytics and best practices from recent cases. Students will engage in an iterative process of exploring data from multiple functional areas within an organization to derive actionable insights as well as communicate findings to help enterprises improve the quality of their decisions.

14. INFS 6391 — Information Security and Risk Assessment Analysis

    This course provides students with comprehensive understanding of problems and solutions related to information security and information assurance in organizational contexts. Students learn how to conduct quantitative and qualitative security risk assessment analyses related to site safety and security, hardware and software reliability and risks, and network reliability and security. Students will carry out data collection and analytics methodologies which address expected failures, incidence and severity of attacks, accidents and acts of nature, and their impacts on operations and budgets.

15. QUMT 6370 Big Data Analytics

    The growth of data in all aspects of life in the forms of emails, weblogs, tweets, sensors, videos and text has necessitated the use of Big Data and advanced analytics techniques to support large-scale data analytics. This course is designed for novice programmers who would like to understand the core platforms and tools to analyze big data. This course brings together key Big Data tools on a cloud-based services platform to show how to efficiently manage big data. Students will be guided with hands-on experience in how data scientists use techniques such as MapReduce, Hive, Pig and Spark to design and build big data applications to solve business problems with large volumes of data. Topics include the Hadoop architecture, social media analytics, link analysis, and stream analytics.