Master of Science in Business Analytics Online

The 100% online Master of Science in Business Analytics program from the University of Texas Rio Grande Valley AACSB-accredited Robert C. Vackar College of Business & Entrepreneurship enables data-driven thinkers develop their abilities to transform complex data into insights that guide their organizations in making more educated, actionable decisions – skills required for success in today's competitive job market.

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Business Analytics Master's Student

About the Program

The Master of Science in Business Analytics program will prepare students to solve problems in business and organizational contexts which require the application of information systems to carry out sophisticated business analytics techniques. Students will be knowledgeable and skilled in data selection, collection, preparation and storage, visualization, and analysis using data analytics techniques such as data mining, machine learning, text mining, social media analytics, big data, and enterprise analytics. Students will obtain hands-on experience with cutting-edge analytics tools and software such as R, Python and Tableau. Our graduates can find diverse careers in the business analytics profession such as data analysts, business analytics consultants, business intelligence analysts, business analytics manager, big data analytics specialists, data scientists and predictive modelers.

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Courses

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.
This course discusses the process of business analytics based on data modeling 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 perform analyses with various software packages in business contexts.
This course teaches students how to apply computing tools to novel analytic challenges in organizational contexts. For a series of organizational analysis case problems, students will learn how to choose appropriate data, store and format it for analysis, create customized computing solutions based on programming and scripting languages, and present the results in a variety of forms, including tabular and graphic/visualization methods. Students will apply software languages such as R and Python, in desktop, cloud and high-performance computing contexts. Prerequisite: QUMT 6303 or QUMT 3341 or equivalent.
This course introduces students to the concept of social media analytics and techniques used to analyze social media data such as texts, networks, actions, hyperlinks, mobile, location and search engine data. Students will learn how businesses align social media analytics with their business strategies and gain insights on trends and user behaviors. An emphasis is placed on using various software tools to analyze real-world social media data.
This course focuses on realizing the business advantage of utilizing data to support managerial decisions. Students will employ a variety of software tools in the development of data warehouse, including ETL (extraction, transformation and loading), and visual data representations (e.g., dashboards, data cubes). Hands-on exercises include dimensional modeling, MS-Excel, and Tableau, among others.
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
This course introduces students to various prescriptive analytic techniques and tools that can be used to analyze business decision problems and create business value. Topics may include deploy analytics such as aggregate planning models and complex problem solving. Analytical packages and modeling software such as Excel solver for linear and integer programming will be extensively used throughout the course. The emphasis of this course will be placed on the application of techniques and interpretation of the results.
This course introduces the principles and techniques of prescriptive analytics. 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 analytical models to evaluate uncertainty that is prevalent in many business decisions. Since business problems often have alternative solutions, students will learn how to use analytical models to assess various business solutions and identify the best course of action. This course involves a hands-on learning experience with spreadsheet modeling and other analytical packages. The emphasis is on how to employ these analytical methods to facilitate managerial decision-making in diverse industries and functional areas.
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.
This course discusses the process of business analytics based on data modeling 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 perform analyses with various software packages in business contexts.
This course teaches students how to apply computing tools to novel analytic challenges in organizational contexts. For a series of organizational analysis case problems, students will learn how to choose appropriate data, store and format it for analysis, create customized computing solutions based on programming and scripting languages, and present the results in a variety of forms, including tabular and graphic/visualization methods. Students will apply software languages such as R and Python, in desktop, cloud and high-performance computing contexts. Prerequisite: QUMT 6303 or QUMT 3341 or equivalent.
This course introduces students to the concept of social media analytics and techniques used to analyze social media data such as texts, networks, actions, hyperlinks, mobile, location and search engine data. Students will learn how businesses align social media analytics with their business strategies and gain insights on trends and user behaviors. An emphasis is placed on using various software tools to analyze real-world social media data.
This course focuses on realizing the business advantage of utilizing data to support managerial decisions. Students will employ a variety of software tools in the development of data warehouse, including ETL (extraction, transformation and loading), and visual data representations (e.g., dashboards, data cubes). Hands-on exercises include dimensional modeling, MS-Excel, and Tableau, among others.
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
This course introduces students to various prescriptive analytic techniques and tools that can be used to analyze business decision problems and create business value. Topics may include deploy analytics such as aggregate planning models and complex problem solving. Analytical packages and modeling software such as Excel solver for linear and integer programming will be extensively used throughout the course. The emphasis of this course will be placed on the application of techniques and interpretation of the results.
This course introduces the principles and techniques of prescriptive analytics. 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 analytical models to evaluate uncertainty that is prevalent in many business decisions. Since business problems often have alternative solutions, students will learn how to use analytical models to assess various business solutions and identify the best course of action. This course involves a hands-on learning experience with spreadsheet modeling and other analytical packages. The emphasis is on how to employ these analytical methods to facilitate managerial decision-making in diverse industries and functional areas.
This course provides the knowledge about fundamentals of health Information Systems and the role of Information systems in efficient operation of healthcare organizations. The course specifically focuses on: Evolution of HMIS, HMIS components and basic HMIS functions, technology infrastructure for healthcare organizations, basic concepts such as HER, HIE, CPOE, and CDSS, HMIS standards such as HIPPA, HL7, and DICOM, strategic information systems planning for healthcare organizations, systems analysis and project management, information security issues, and role of HMIS professionals in health organizations.
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. 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.
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.
This course is targeted towards graduate students and practitioners as it focuses on the significance of Information Security in present-day business organizations. The objective of this course is to provide students with a comprehensive understanding of the problems related to Information Security, and solutions to these problems. Students will receive theoretical and practical instructions in both managerial and technical aspects of securing information in organizations. The course will be helpful to students who are interested in attaining Certified Information Systems Security Professional certification and/or careers in Information Security. Prerequisite: INFS 6330 or equivalent, or by permission of the instructor.
This course provides the knowledge about fundamentals of health Information Systems and the role of Information systems in efficient operation of healthcare organizations. The course specifically focuses on: Evolution of HMIS, HMIS components and basic HMIS functions, technology infrastructure for healthcare organizations, basic concepts such as HER, HIE, CPOE, and CDSS, HMIS standards such as HIPPA, HL7, and DICOM, strategic information systems planning for healthcare organizations, systems analysis and project management, information security issues, and role of HMIS professionals in health organizations.
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. 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.
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.
This course is targeted towards graduate students and practitioners as it focuses on the significance of Information Security in present-day business organizations. The objective of this course is to provide students with a comprehensive understanding of the problems related to Information Security, and solutions to these problems. Students will receive theoretical and practical instructions in both managerial and technical aspects of securing information in organizations. The course will be helpful to students who are interested in attaining Certified Information Systems Security Professional certification and/or careers in Information Security. Prerequisite: INFS 6330 or equivalent, or by permission of the instructor.

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Application Deadline

Registration and Payment Deadline

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Tuition & Financial Aid

UT Rio Grande Valley's 100% online accelerated graduate programs offer affordable tuition, and financial aid is available for those who qualify.

Total Program Cost

Per Credit Hour

Per 3-Credit-Course

*We estimate that tuition and fees will total no more than the rates shown above; however, rates are subject to change.

Scholarships

UTRGV offers Dean’s New Graduate Student Scholarship towards tuition and fees for new Master’s students starting in Fall 2018 and over 200 other scholarships to help you make your graduate education a reality. For more information, please visit our Scholarships page.

Financial Aid

UTRGV is an equal opportunity institution in the administration of its financial aid programs. In keeping with this policy, financial aid is extended to students without regard to race, creed, sex, national origin, veteran status, religion, age or disability. For additional information regarding funding please visit our Financial Aid for Accelerated Online Programs page.

Additional Fees

Domestic Applicant Fee: $50
International Applicant Fee: $100
Graduation Fee: $32


Admissions

Please review all the admission requirements for the Master of Science in Business Analytics Online degree program. For specific questions or more details, contact an enrollment specialist at 1-833-887-4842.

Admissions Criteria

Online Application

Submit your application and one-time $50 application fee (for domestic applications) online.

Official Transcript

Submit transcripts from all colleges/universities

GPA

3.0 (on a 4.0 scale)

GMAT or GRE

Required for admission. GMAT/GRE waiver available


Videos

Master of Science in Business Analytics Online

UTRGV offers an online Business Analytics (MS) degree that will enable data-driven thinkers to develop their abilities to transform complex data into insights that guide their organizations in making more educated, actionable decisions – skills required for success in today's competitive job market.


Defining Business Analytics

I would define business analytics as simply as the use of a set of components whether it is people, technologies and tools in order to transform data into insightful decisions and actions.

Murad Moqbel, Ph.D.