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Business Analytics (Certificate)

Business decisions in the 21st century are increasingly dependent on smart analytics which extract and apply the value inherent in data. Using current powerful data analytics tools and methods, students in this program learn how to select, collect and analyze information, and effectively communicate results by taking four courses from among the courses available in the Master of Science in Business Analytics program.


Admission Requirements & Courses

Program Details

Next Start Date
Fall I Module
Application Deadline
Estimated Program Length
Credit Hours
Course Length
Cost Per Credit

About the Program

Business decisions in the 21st century is increasingly dependent on smart analytics which extract and apply the value inherent in data. Using current powerful data analytics tools and methods, students in this program learn how to select, collect and analyze information, and effectively communicate results.

Student Learning Outcomes
Be able to frame problems in various areas for the application of business analytics techniques.
Be able to use contemporary information systems and analytical tools to acquire and manage data for business analytics.
Be able to use contemporary information systems and business analytical tools to perform analyses.
Be able to effectively communicate business analytics results.

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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.
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.
Upon completion of 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.
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 healht. 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 discuss challenges related to healthcare analytics such as data privacy, security, and interoperability.
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).
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.
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.
This course introduces students to data visualization and dash-boarding. 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 decision. Student 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 dash-boarding but will develop transferable skills which can apply to most common software packages in the field.
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.
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 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.
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. Prerequisite: Admission to the MBA Program.
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
This course introduces the principles and techniques of prescriptive analytics. These provide business entities and policy makers with rational tools for 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.
The growth of data in all aspects of life in 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 want 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.

Calendar - Fall I Module

Course Start Date

Application Deadline

Payment Deadline

Registration Deadline

Course End Date

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.


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

No Application Fee
Graduation Fee: $50


Please review all the admission requirements for the Business Analytics (Certificate) degree program. For specific questions or more details, contact an enrollment specialist at 1-833-887-4842.

Admissions Criteria

Online Application

Submit your application online.

Official Transcript

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