Examines information technology from a business enterprise perspective. Topics include: the components and development of business information technology systems, their use in enhancing and facilitating management practices and decision making and their impact on management decisions.

This course covers topics in managerial data mining, texting mining, and web mining, and related data retrieval and manipulation. Models in regression, clustering, neural nets, classification, and association rule mining are applied to business data sets. In this managerially focused course, students will learn when and how to use such techniques and how to interpret output. Students will also be introduced to languages such as R and Python to extract and manipulate data.

This course provides an introduction to business intelligence and analytics, including the processes, methodologies, infrastructure, and current practices used to transform business data into useful information and support business decision-making. Business Intelligence requires foundation knowledge in data models and data retrieval, thus this course will review logical data models for both relational database systems and data warehouses. Students will learn to extract and manipulate data from these systems using Structured Query Language (SQL).

This course provides an introduction to business intelligence and analytics, including the processes, methodologies, infrastructure, and current practices used to transform business data into useful information and support business decision-making. Business Intelligence requires foundation knowledge in data models and data retrieval, thus this course will review logical data models for both relational database systems and data warehouses. Students will learn to extract and manipulate data from these systems using Structured Query Language (SQL).

This course provides an introduction to business intelligence and analytics, including the processes, methodologies, infrastructure, and current practices used to transform business data into useful information and support business decision-making. Business Intelligence requires foundation knowledge in data models and data retrieval, thus this course will review logical data models for both relational database systems and data warehouses. Students will learn to extract and manipulate data from these systems using Structured Query Language (SQL).

This course provides an introduction to business intelligence and analytics, including the processes, methodologies, infrastructure, and current practices used to transform business data into useful information and support business decision-making. Business Intelligence requires foundation knowledge in data models and data retrieval, thus this course will review logical data models for both relational database systems and data warehouses. Students will learn to extract and manipulate data from these systems using Structured Query Language (SQL).

This course provides an introduction to business intelligence and analytics, including the processes, methodologies, infrastructure, and current practices used to transform business data into useful information and support business decision-making. Business Intelligence requires foundation knowledge in data models and data retrieval, thus this course will review logical data models for both relational database systems and data warehouses. Students will learn to extract and manipulate data from these systems using Structured Query Language (SQL).

This course covers topics in managerial data mining, texting mining, and web mining, and related data retrieval and manipulation. Models in regression, clustering, neural nets, classification, and association rule mining are applied to business data sets. In this managerially focused course, students will learn when and how to use such techniques and how to interpret output. Students will also be introduced to languages such as R and Python to extract and manipulate data.

This course introduces fundamental concepts and computations for statistical analysis of business data and real-world problems with an emphasis on understanding and interpreting statistical information, and using it to form sound judgments in business situations. The course covers basic descriptive statistical methods, sampling methodology, how to draw inferences from samples to larger populations and how to make predictions based upon historical relationships between variables.

Provides an introduction to project management, focusing on the integration of business operations and information management, and techniques to effectively manage the implementation of such projects.