Business Analytics (Certificate)

Business Analytics (Certificate)

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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.


Students who complete the 12 hours (4 courses) required for the graduate certificate may wish to continue on to a full MBA degree, in which 9 hours of analytics courses constitutes a concentration in Business Analytics for their MBA. Continue to watch this space in the future for additional course topics, and new degree programs for Business Analytics! 

Admission Requirements

Step #1: Submit a UTRGV Graduate Application at There is no application fee.

Step #2: Request your official transcripts to be sent electronically to or mailed to:

The University of Texas Rio Grande Valley
The Graduate College
Marialice Shary Shivers Bldg. 1.158
1201 W. University Drive
Edinburg, TX 78539-2999

*Please Note: If you are a graduate of UTPA, UTB/TSC, or UTRGV you do not need to request an official transcript to be sent to the Graduate College.

Review and submit all Program Requirements:

  • Bachelor's degree from a regionally accredited institution in the United States or a recognized international equivalent in a similar or related field.
  • Official transcripts from each institution attended (must be submitted directly to UTRGV).
  • Undergraduate GPA of at least 3.0. If applicant does not meet minimum undergraduate GPA criterion, GRE general test with minimum scores of 150 Verbal, 141 Quantitative, and 4.0 Analytical are required for conditional admission.

Students whose native  language is not English  or  who studied at a University outside the U.S.:

  • TOEFL or IELTS Language Proficiency Test with minimum scores: 550 on paper-based, 213 on computer based, or 79 on Internet-based for the TOEFL; 6.5 for the IELTS. TOEFL and IELTS scores are valid for 2 years. For additional information, visit the English Proficiency Exam section of our website.
  • Certified English translation of educational records.


Program Contact

Program Coordinator: Dr. Jerald Hughes

Phone: 956-665-7317

Office: Edinburg Campus, EMAGC 3114




Applications will be accepted year round and prospective students are encouraged to apply at least 2 months before classes start to ensure a timely application review.  Applying early will also give prospective students the best opportunity to be considered for scholarships and other possible funding opportunities.


  • This program only admits applicants during Fall, Spring, Summer I & Summer II semesters.
  • Students admitted only to a Certificate Program are not eligible to obtain a Student Visa from UTRGV.

Tuition Estimate

Residency Per 3-Credit Hour Course Semester (9-Credit Hours) Total Estimated Cost
Texas Resident $1,481.39 $3,894.17 $5,925.56
Non-Resident/International $2,708.39 $7,575.17 $10,833.56

*We estimate that tuition and fees will closely approximate the rates shown above; however, rates are subject to change. Please note that the rates above are estimated for on-campus students and those enrolled in 16-week online programs. The rate is different for Accelerated Online Programs (AOP). Visit the tuition and fees page for detailed information.

Course Requirements

Required Courses 12
INFS 6340: Health Computer Information Systems   3
INFS 6343: Healthcare Analytics   3
INFS 6350: Foundations of Business Analytics   3
INFS 6351: Application Development for Business Analytics   3
INFS 6353: Social Media Analytics   3
INFS 6356: Data Wareshousing and Visualization   3
INFS 6359: Data Mining for Business Analytics   3
INFS 6333: Spreadsheet Modeling for Service Industries   3
INFS 6363: Enterprise Analytics   3
INFS 6391: Information Security and Assurance Management   3
QUMT 6303: Statistical Foundations   3
QUMT 6350: Decision Modeling for Business Analytics   3
QUMT 6360: Decision Optimization for Business Analytics   3
Total hours required for completion  12


Course Descriptions

INFS 6340: Health Computer Information Systems [3-0]

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.

INFS 6343: Healthcare Analytics [3-0]

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.

INFS 6350: Business Intelligence and Data Warehousing [3-0]

This course discusses the process of business analytics by developing a business intelligence solution, inducing 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 ELT (extraction, transforming and loading) and visual data representations (e.g., data cubes).

INFS 6351: Developing Customized Solutions for Business Analytics [3-0]

Novel problems require innovative solutions- This course introduces student 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.

Prerequisite: QUMT 6303 or QUMT 3341 or equivalent. 

INFS 6353: Social Media Analytics [3-0]

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.

INFS 6356: Data Visualization [3-0]

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.

INFS 6359: Data Mining for Business Analytics [3-0]

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.

INFS 6333: Spreadsheet Modeling for Service Industries [3-0]

This course focuses on spreadsheet modeling to support decision making by organization 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.

INFS 6363: Enterprise Analytics [3-0]

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.

INFS 6391:

Information Security and Risk Assessment Analysis [3-0]

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 risk, 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. 

 QUMT 6303: Statistical Foundations [3-0]

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.

 QUMT 6350: Decision Modeling for Business Analytics [3-0]

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.

 QUMT 6360: Decision Optimization for Business Analytics [3-0]

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, studnets will learn how to use analytical models to assess various business solutions and identify the best course of action. Tjis 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.


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