Graduate Certificate in Business Analytics

The Graduate Certificate in Business Analytics (GCBA) allows graduate students to gain specialized knowledge in both data analytics and management. Obtaining a graduate-level analytics certificate will provide an advantage to those seeking to advance their careers in the rapidly growing market for data and analytics professionals.

Certificate electives and faculty are shared with the Master of Science in Business Analytics (MSBA) program and provide the same high level of instruction and interaction. If you have questions, please contact the academic advisor of your graduate program.

Curriculum
  • The certificate requires 15 credit hours of coursework.
  • All credits must be completed within three years.
  • Certificate credits can cross-count toward your graduate program requirements.
  • Grades, credits, and the certificate will appear on an official university transcript.
  • A 3.0 cumulative GPA in certificate courses is required for successful completion of the certificate.
Core Course

Course Number: MKTG 6487
Credits: 1.5

This course introduces students to the main elements of business analytics. The domains include framing the business problem, framing the analytics problem, managing data, selecting the methodology, building, the model, deployment and life cycle management. Students will learn and practice the domains using small projects.

Admission Requirements
  • Applicants must be current graduate students at the University of Utah.
  • Applicants must have a minimum 3.0 undergraduate GPA or a minimum 3.0 GPA in their current graduate program.
  • Courses may have individual pre-requisites.
  • A GMAT/GRE score is not required
Application Deadlines
  • Students must apply for the certificate at least one semester prior to the semester in which they will graduate.
  • Applications are accepted throughout the year and reviewed on a rolling basis.
  • Students may start the program in Fall, Spring, or Summer semester.
Elective Courses

Course Number: IS 6420
Credits: 3

Advanced topics in database theory and design, including hands-on development of a working database system. Topics covered include the relational database model, foundations in relational algebra, design techniques, SQL, distributed databases, multimedia databases, and knowledge bases.

Course Number: IS 6482
Credits: 3

This course introduces data mining technologies that assist in discovery of reliable, understandable and useful patterns in structured, semi-structured and unstructured data. Students will practice core data mining technologies, analyze cases, and explore real world applications and issues.

Course Number: IS 6489
Credits: 3

This is a graduate level course in statistics, with an emphasis on developing predictive models using an open source statistical programming language. The engaged student should expect to develop foundational skills for data analysis. Topics covered will include some or all of the following: descriptive statistics, non-parametric regression, probability distributions, linear and logistic regression, tree-based methods, model assumptions and model checking, cross-validation, simulation, resampling, visualization, and reproducible research.

Course Number: IS 6493
Credits: 3

Successful corporations can utilize data science techniques to help drive business decision making by analyzing datasets of varying sizes. In this course, a hands-on practitioner’s approach is taken to learning the fundamental knowledge, techniques and tools required for leading data science teams and analyzing big data. This course will utilize popular open source technologies and libraries in use today to learn how to collect, pre-process and visualize data, as well as build and test models for inference and prediction. We will examine the unique challenges posed by big data and complex models, and learn how to address them using distributed computing frameworks such as Dask, Hadoop and Spark. The course is taught in Python, and will offer a bootcamp in the first few weeks to help everyone get comfortable with the language.

Course Number: IS 6491
Credits: 1.5

Data Visualization is the graphical representation of information. Data Visualization and related technologies create value within organizations by providing insight from complex sets of data by communicating key aspects therefrom. This course focuses on how to increase the likelihood of action based on insights from data by telling stories with data that leverage effective Data Visualizations. The course includes a mix of theory and hands-on application using contemporary processes and Data Visualization technologies.

Course Number: MKTG 6600
Credits: 3

The aim of this course is to use algorithms to reach business decisions. The focus will be on understanding the implications of analytics for the formulation and implementation of business strategy. Students will learn about a business situation, learn of an analytic method that can be used to solve the business problem, and use the method in R to solve the business problem. Supervised and unsupervised algorithms such as regression, support vector regression, customer lifetime value, clustering, text analysis, word embedding, and causal methods such as A/B testing would be used to solve business problems such as market segmentation, brand positioning, customer satisfaction, ethical decision-making, financial decisions, healthcare decision-making, product pricing. The following process will be used for each topic: Understanding the Business context -> the data that can help solve the problem -> the analytic method that could be used -> understanding the mathematics and scope of the method ->implementation using R -> combining the business context and the algorithm in a mini-project.

Course Number: MKTG 6620
Credits: 3

Business Analytics is a strategic asset that offers unique opportunities for competitive advantage. It lives at the crossroads of business and technology. As technologies transform the marketplace, companies across the globe are collecting an enormous amount of data that can be used to predict the consequences of alternative courses of action and guide decision-making. The objective of this course is to i) demystify the world of big data analytics and ii) show applications of analytic tools across everyday business decisions. Some of the topics discussed will be applications of supervised, unsupervised machine learning, outlier analysis, and adaptive learning.

Course Number: MKTG 6640
Credits: 3

The web is replete with unstructured data in the form of emails, tweets, blogs, customer reviews, and so on. This course introduces the tools and concepts that allow businesses to analyze textual data and shows how these methods can be used to make better business decisions in online and offline contexts. The course focuses on different analytical techniques and algorithms including preprocessing, text classification, text clustering, topic modeling, document summarization, and sentiment analysis. These methods are applied to different areas of business including marketing, human resource management, business law, accounting, and finance.

Course Number: OSC 6610
Credits: 1.5

Managerial decisions – regardless of their functional orientation – increasingly leverage quantitative models to approximate business problems and provide insights. This course takes a managerial approach to analytical modeling to analyze problems in a wide array of fields such as finance, marketing, operations, information systems, etc. The tools covered in this class are deterministic optimization techniques including linear programming, network models and integer programming.

Course Number: OSC 6611
Credits: 1.5

Like OIS 6610, this course takes a practical approach to analytical modeling. While the emphasis in Analytical Decision Models 1 was on deterministic optimization techniques, models in the course will be probabilistic in nature. The main topics for the course are advanced queuing and simulation. Applications will encompass problems from a variety of business disciplines including production/operations management, marketing, and finance.