Business Intelligence and Data Analytics

Data analytics help decision makers perform quicker analysis and use the valuable information to make better decisions. This course introduces several commonly used modeling tools to develop and improve students' analytical skills through a variety of realistic situations. The skills learned in this course will help students to recognize a decision situation, understand the business problem, and deal with uncertainty and complex interactions to solve the problem. This course is cross-listed with ISBA 630 and equivalent to ISBA 630.

Prerequisite: BADM 530 or equivalent.

This course focuses on building 'web-intelligence' applications exploiting big data sources arising from social media, mobile devices, and sensors, using new big-data platforms. The past decade has witnessed the development of big data platforms and associated technologies such as distributed file systems, no-SQL databases, and stream computing engines. Online advertising, machine translation, natural language understanding, sentiment mining, personalized medicine, and national security are some examples of web-intelligence applications that are already wide-spread in different business areas. In this course, we explore some such applications and related techniques such as text analytics, social media analysis, cloud and distributed systems, big data visualization using Tableau and GIS along with parallel implementations using map-reduce and related platforms.

Prerequisite: MKTG 640 or consent of instructor.

Data analytics focuses on improving existing operations and supports the enterprise resource planning by streamlining or optimizing business processes. Business analytics help decision makers perform quicker analysis and use the valuable information to make better decisions. This course introduces several commonly used modeling tools to develop and improve students' analytical skills through a variety of realistic situations. The skills learned in this course will help students to recognize a decision situation, understand the business problem, and deal with uncertainty and complex interactions to solve the problem. In order to equip students with these skills, this course is divided into four parts: (I) Descriptive and Predictive Analysis, (II) Prescriptive Analytics with low uncertainty, (III) Prescriptive Analytics with high uncertainty and (IV) Complex Predictive Analytics. Part I will cover forecasting and time series analysis, Part II will cover modeling, optimization, and solvers. Part III will cover decision trees for structuring decision problems under uncertainty, waiting line models, simulation and Part IV will cover multi-criteria decision making, heuristics, and simulation-optimization. Throughout the course, we will use SAS software as a modeling and analysis environment. This course is cross-listed with an equivalent to MGMT 610.

Prerequisite: BIDA 630

This course focuses on the integration of business processes and real-time data analysis on a specific business intelligence platform. Using a variety of globally used, user-friendly technologies students will have hands-on experience and develop powerful analytical capabilities to end users. Major components of business intelligence platforms will be introduced and fundamentals of analytics (reporting, visualization, and prediction) will be taught using SAP Business Intelligence Business Objects. This course uses the latest in technology to show the practice of data analytics in the real world. You will experience practical applications of analytics through guided exercises and case studies

Prerequisite: BIDA 630 and BIDA 650

The prerequisite business analytics course presented predictive and prescriptive analytics tools at the introductory level. This course goes beyond by covering the tools and theory at a deeper level with an emphasis on the challenges that can arise in implementing analytical approaches within an actual organization. With the objective of helping students make better business decisions using advanced analytics, the course emphasizes that business analytics is a practical discipline which requires mastery of both methodology and business applications using special software and tools. Course topics include a review of basis forecasting, large-scale optimization, network optimization, multicriteria-decision making and heuristics algorithms. The course requires students to work on real-world projects where a significant competitive advantage has been obtained through large-scale data analysis. Examples include supply chain management, healthcare, pricing, and revenue management. Throughout the course, we will use SAP Next-Gen Lab, SAS software as a modeling and analysis environment including SAS Enterprise Guide, SAS Enterprise Miner, SAS Forecast Studio and SAS OR.

Prerequisite: BIDA 660

Dr. Burcu Adivar 

Ph.D. from the North Carolina State University, Edward Fitts Department of Industrial and systems engineering
Email: badivar1@uncfsu.edu

Dr. Murat Adivar 

Ph.D. from the Ankara University, Department of Mathematics
Post-Doc. from the Missouri University of Science and Technology  
Email: madivar@uncfsu.edu

Dr. Suzanne Altobello

Email: saltobello@uncfsu.edu

Dr. Aysegul Cansizoglu

Ph.D. from the North Carolina State University, Edward Fitts Department of Industrial and systems engineering
Email: acansizoglu@uncfsu.edu

Dr. Sam Gosh

Ph.D. from the University of Maryland, Department of Civil and Environmental Engineering
Email: sgosh@uncfsu.edu

Dr. Su Dong

Ph.D. from the University of North Carolina in Charlotte
Email:sdong@uncfsu.edu

Dr. Gavin Jiayun Wu

Ph.D. from the University of Arizona in Tucson
Email:jwu2@uncfsu.edu