Overview of organizational behavior, operations management, and strategy/entrepreneurship. Topics include: Behavioral background of formal organizational life, organizational design, integrating factors in collective behavior, organizational change, systems analysis, techniques of decision making and control, the organization and its environment, and the nature of management theory. Topics grouped in modules of organizational behavior, operations management, and strategy/entrepreneurship. Prerequisite: junior standing.

Concentration on the federal income taxation of corporations, partnerships, estates and trusts, gifts and estate taxes.

Focus on the use of cost accounting information to help planning and control activities in a business enterprise. Topics include methods for determining the costs of products and services (includes job order, process, standard and activity based cost systems), for assessing product and project profitability, and for budgeting and monitoring costs and profits. Emphasis on usefulness and limitations of cost accounting information.

Continuation of ACCOUNTG 321. Conceptual framework for accounting for a firm's reported liabilities. The nature of liabilities, their recognition, measurement, and disclosure. Motivation of management in choosing among acceptable accounting alternatives in each of these areas, and the economic choices of such consequences. Prerequisite: ACCOUNTG 321. (Those who did not receive at least C should seriously consider taking that course over, prior to enrollment in ACCOUNTG 322.)

Preparation and uses of financial statements of business enterprises; the accounting process of recording, classifying, and interpreting business transactions. Conceptual problems analyzed include accounting for inventories, depreciation, and capital stock.

This course provides an understanding of the strategic role of Information Technologies (IT) in business; how to successfully integrate IT into the organization; how to manage the challenges associated with conceiving, justifying, implementing and maintaining IT-based initiatives ; and how to be an active and productive participant in management decisions that involve IT.

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. Theoretical background in descriptive and inferential statistical methods will be used support knowledge of the appropriate statistical test to perform in a given situation. The statistical methods that are covered in the course equip the student with the analytic reasoning and quantitative arguments that applies to all areas of business and management.

Overview of human resource management theory and practice in organizations. Legal framework; job design; recruitment, selection, performance appraisal, and separation; compensation; benefits; contemporary issues in HRM.

This course covers essential concepts and methods to foster data-driven decision-making to motivate business insight and innovation. Lessons cover topics in big data, algorithmic thinking, machine learning, data visualization, and communication. Projects spanning several weeks focus on business problems that can be solved through the application of cutting-edge Data Science techniques. Students develop proficiencies in the use of Python, R, and Tableau by completing regular exercises and projects using real data.

This course covers essential concepts and methods to foster data-driven decision-making to motivate business insight and innovation. Lessons cover topics in big data, algorithmic thinking, machine learning, data visualization, and communication. Projects spanning several weeks focus on business problems that can be solved through the application of cutting-edge Data Science techniques. Students develop proficiencies in the use of Python, R, and Tableau by completing regular exercises and projects using real data.