Business Analytics

BUAN 4090 Business Analytics Internship (0 semester credit hours) This course is designed to further develop a student's knowledge of business analytics through appropriate developmental work experiences in a true organizational setting. Students are required to identify and submit specific business learning objectives (goals) at the beginning of the semester. Student performance is evaluated by the work supervisor. Credit/No Credit only. May be repeated if internships differ. Department consent required. (0-0) S

BUAN 4320 Database Fundamentals for Analytics (3 semester credit hours) This course introduces the basic concepts for the design and development of relational databases and database management. Students will also explore NoSQL database topics. At the end of the course, students will have been exposed to advanced SQL and NoSQL queries and advanced database processing using triggers and stored procedures. Topics include entity-relationship data model, logical database design, data administration, Structured Query Language, query optimization, NoSQL database types, and NoSQL querying. Prerequisites: ITSS 3300 and ITSS 3311 and (MATH 1325 or MATH 2413 or MATH 2417) and (CS 2305 or MATH 2418 or MATH 2333 or OPRE 3333). (3-0) S

BUAN 4337 Marketing Analytics (3 semester credit hours) This course is designed for those interested in an entry-level marketing analytics position. Students will analyze data to make key marketing decisions such as which customers to target to increase profitability or which new products to introduce to build incremental business. Students will also be introduced to software products used in the analysis of sales, marketing, and distribution data. Prerequisite: MKT 3300. (Same as MKT 4337 and OPRE 4337) (3-0) Y

BUAN 4350 Spreadsheet Modeling and Analytics (3 semester credit hours) This course develops the ability to use quantitative methods and software (particularly spreadsheets) to build effective models with analytical views for decision making in areas such as finance and operations. This helps students to gain knowledge about specific techniques for building models to analyze data effectively. Prerequisite or Corequisite: OPRE 3360 or STAT 3360. (Same as OPRE 4350) (3-0) S

BUAN 4351 Foundations of Business Intelligence (3 semester credit hours) Students are introduced to foundational business intelligence (BI) concepts and explore the theory and practice of data warehouses for enterprises. BI concepts including data mart schemas, ETL, OLAP, cubes, and reporting will be covered. The course will also examine the components of an enterprise data warehouse, extract, cleanse, consolidate, and transform heterogeneous data into a single enterprise data warehouse, and run queries using a data warehouse. Prerequisites: ITSS 3300 and (BUAN 4320 or ITSS 4300) and (MATH 1326 or MATH 2414 or MATH 2419 or OPRE 3340) and (CS 2305 or MATH 2418 or MATH 2333 or OPRE 3333). (Same as ITSS 4351) (3-0) S

BUAN 4352 Introduction to Web Analytics (3 semester credit hours) Introduces technologies and tools used to realize the full potential of websites. The course focuses on the collection and use of web data such as web traffic and visitor information to design websites that will enable firms to acquire, convert, and retain customers. Online advertising such as paid search and web analytics tools will also be included. Prerequisites: ITSS 3300 and (MATH 1326 or MATH 2414 or MATH 2419 or OPRE 3340) and (CS 2305 or MATH 2418 or MATH 2333 or OPRE 3333). (Same as ITSS 4352) (3-0) S

BUAN 4353 Business Analytics (3 semester credit hours) This course examines various data mining analytical techniques to extract business intelligence from firms' business data for various applications, including supervised and unsupervised learning analytic techniques, association, customer segmentation, classification, customer relationship management (CRM), personalization, online recommendation systems, and web mining. Students will also be exposed to various business intelligence software such as Python, R, XLMiner, SAS EnterpriseMiner, or SQL Server (depending on availability). Prerequisites: (ITSS 3312 or OPRE 3312 or BUAN 4381 or ITSS 4381) and (MATH 1326 or MATH 2414 or MATH 2419 or OPRE 3340) and (CS 2305 or MATH 2418 or MATH 2333 or OPRE 3333) and OPRE 3360. (Same as ITSS 4353 and OPRE 4353) (3-0) T

BUAN 4354 Advanced Big Data Analytics (3 semester credit hours) Advanced topics in supervised and unsupervised machine learning techniques using big data solutions such as Hive and Spark. Students explore the issues and challenges related to managing data within an organization. This course is designed to equip students with skills to address the business intelligence, data analysis, and data management needs of an organization. Students are introduced to machine learning techniques and big data technologies. Prerequisites: (ITSS 3312 or BUAN 4381 or ITSS 4381) and (BUAN 4320 or ITSS 4300) and (BUAN 4351 or ITSS 4351). (Same as ITSS 4354) (3-0) Y

BUAN 4355 Data Visualization (3 semester credit hours) This course focuses on how to leverage new decision support technologies to improve organizational decision making. Students will explore various data visualization tools and review the foundational principles that guide their use. Prerequisites: (ITSS 3312 or BUAN 4381 or ITSS 4381) and (BUAN 4320 or ITSS 4300) and (BUAN 4351 or ITSS 4351). (Same as ITSS 4355) (3-0) Y

BUAN 4357 Supply Chain Analytics, AI, and Advanced Solutions (3 semester credit hours) This hands-on course equips students with the knowledge and skills to leverage analytical tools and programming languages such as Excel and Python for the practical application of analytical techniques within the realm of supply chain management. Topics covered include demand planning, forecasting, inventory and production optimization, transportation, and sales analysis. Students gain proficiency in using these analytical tools to address real-world challenges within complex supply chain systems. The course also delves into various AI use cases within supply chain operations, highlighting the role of AI-Led Analytics in optimizing supply chain management flows. Prerequisites: (OPRE 3360 or STAT 3360) and OPRE 3310. (Same as OPRE 4357) (3-0) Y

BUAN 4373 Data Science for Business Applications (3 semester credit hours) This course builds on the foundations of Probability and Statistics from OPRE 3360. It further develops knowledge and skills for applying statistical and management science models to business decision-making. Topics include hypothesis testing for several populations, intermediate multiple linear regression, variable transformation, model selection procedures, chi-square tests and contingency tables, design of experiments and Analysis of Variance (ANOVA), logistic regression, and non-parametric methods. The course uses statistical software. Prerequisite: OPRE 3360. (Same as OPRE 4373) (3-0) S

BUAN 4381 Object Oriented Programming with Python (3 semester credit hours) Students will learn basic concepts of Object-Oriented Programming (OOP) and implement the ideas using Python, a scripting language. The classes will consist of lectures interwoven with hands-on coding that reinforces the language constructs as well as using functions from basic libraries. The students are required to bring in laptops to the class so that they can practice coding as a follow through during the lectures. The lectures will provide opportunities for the students to collaborate and learn (paired programming). Prerequisites: ITSS 3311 and (MATH 1326 or MATH 2414 or MATH 2419 or OPRE 3340) and (CS 2305 or MATH 2333 or MATH 2418 or OPRE 3333). (Same as ITSS 4381) (3-0) S

BUAN 4382 Applied Artificial Intelligence/Machine Learning (3 semester credit hours) This course provides a broad and detailed introduction to machine learning, data mining, and statistical pattern recognition. In this class, students will learn about the most effective machine learning techniques, and gain practice applying them to analyze business data. Regular course lectures will be used to deliver the main concepts and methods related to machine learning and AI. A number of in-class, hands-on exercises will be created to help students understand how to apply those techniques to solve some real-world business problems. Prerequisite: ITSS 3312 or BUAN 4381 or ITSS 4381. (Same as ITSS 4382) (3-0) S

BUAN 4383 Advanced Applied Artificial Intelligence/Machine Learning (3 semester credit hours) This course builds on the Applied Artificial Intelligence/Machine Learning course and covers more advanced topics, like deep learning and reinforcement learning. The course will emphasize the application of these methods to business problems. Prerequisites: (ITSS 3312 or BUAN 4381 or ITSS 4381) and (BUAN 4382 or ITSS 4382). (Same as ITSS 4383) (3-0) S

BUAN 4395 Capstone Senior Project - Business Analytics (3 semester credit hours) This course is intended to complement theory and provide an in-depth, hands-on experience in all aspects of a real analytics business project. Students will work in teams as consultants on projects of interest to the industry and will be involved in specifying the problem and its solution, designing and analyzing the solution, and developing recommended solutions. The deliverables will include reports that document these steps as well as a final project report, including the challenges faced by the team. Teams will also make presentations. Student groups will apply business analytics concepts and techniques in developing the report. Prerequisites: (BUAN 4320 or ITSS 4300) and (BUAN 4373 or OPRE 4373) and (BUAN 4355 or ITSS 4355) and (BUAN 4381 or ITSS 4381). (3-0) S