Business Analytics Certificate Program Requirements
GRBA 851 is required as the first course in the certificate program. This course provides an introduction to the three main areas of business analytics: descriptive, predictive and prescriptive methods. Upon completion of GRBA 851, the other three courses, two of which are required and one of which can be chosen from the list of approved electives, may be taken in any sequence.
Please view the online course schedule to determine when each of these courses will be offered.
Please refer to the Nebraska Course Catalog for detailed descriptions of courses.
Three Required Courses:
GRBA 851: Business Analytics
This course provides a broad overview of important business analytic topics and how they can be used to support decision-making in all areas of business, government, education, and agriculture. Emphasis will be placed on the procedures that are used to describe data, make predictions and recommend actions to improve performance.
ECON 817: Introductory Econometrics
This course is designed to provide necessary background in statistical methods. The course offers an overview of key statistical concepts and techniques, while incorporating hands-on data analysis. Hypothesis formulation and testing, economic prediction and problems in analyzing cross-section and time series data are considered.
MRKT 850: Data Driven Marketing Strategy
This course considers the strategic use of large marketing databases, including how market data can be translated into insights for decisions like pricing, advertising response, resource allocation, and new product development. Topics covered include market response models, clustering and classification, conjoint analysis, resource allocation models, forecasting, customer profitability analysis, customer choice modeling, value pricing, product line decisions and other strategic marketing issues facing today's managers.
Select one elective from the following courses:
MRKT 845: Advanced Marketing Analytics
This course provides an overview of web, social media, and consumer text analytics; analyzing consumer data streams from the Internet, mobile devices, and sensors; handling very large volumes of data; general data analysis software operation for various marketing problems; marketing platform software for general and specific tasks; and learning machines in marketing.
SCMA 837: Risk and Simulation Modeling
Simulation is the process of building a computer model of a system and experimenting with the model to obtain insight into the system’s behavior. This course considers the simulation of business systems that are subject to uncertainty and risk. Students will learn the entire process of designing a simulation model, implementing it in software, executing the simulation, collecting output data, and using the results to evaluate decision options.
SCMA 851: Predictive Analytics
Predictive analytics extends statistical and/or artificial intelligence methods to provide forecasting capability. In today’s environment, it involves applying knowledge management to analyze large quantities of data. Students will be exposed to the analytic methods and statistical programming languages, such as R, that are commonly used for predictive analytics in business.
SCMA 852: Data Management and Organization
The use of information technology and statistical analysis continues to grow in all sectors of the economy. Along with that growth comes a need for individuals with advanced training in the technology of databases. In this course, databases are studied from the perspective of their logical organization and physical design, as well as from the perspective of managers and applications programmers. The database querying language SQL will be introduced and practical applications of databases will be considered.
SCMA 853: Business Data Mining and Descriptive Analytics
Data mining applies quantitative analysis to support humans in identifying actionable information from large amounts of data. This course focuses on how data mining has been successfully applied in business. It will describe the statistical and artificial intelligence-based tools commonly used in data mining. The course will also address ethical issues related to the use of information obtained through data mining.
SCMA 854: Advanced Descriptive Analytics
Descriptive analytics focuses on descriptions and reports of what has happened. This course seeks to provide explanations and demonstrations of advanced descriptive analytics tools such as cluster analysis, text mining and link analysis. The course will expose students to the concepts of Big Data and Internet of Things. Students will learn how to use descriptive analytics and relevant software packages to solve business problems.
SCMA 855: Prescriptive Analytics
This course will focus on how optimization modeling techniques can be used to make the best decisions in a variety of business analytics applications. The emphasis will be on the formulation of different optimization problems and the use of the correct quantitative techniques to solve these problems.
SRAM 816: Principles of Survey Analysis
This course provides an introduction to the basic principles of causality and inductive logic in contemporary social and behavioral science. Topics include one, two, and multi-way layouts in analysis of variance; fixed effects models and linear regression in several variables; the Gauss-Markov-Theorem; multiple regression analysis; and basic principles of experimental and quasi-experimental designs.
SRAM 819: Applied Sampling
This course considers the design of probability samples, issues related to sampling populations of humans, and the unique challenges posed by such populations, restricted by cost and available sampling frames. Topics incluse simple random sampling, stratification, cluster sampling, systematic sampling, multistage sampling, probability proportional to size sampling, area probability sampling, and telephone samples.
SRAM 865: Survey Analysis and Design
This course considers the basic issues related to the design and analysis of sample surveys. Topics include the basics of questionnaire construction, sampling, data collection, analysis and data presentation.