应用业务分析

This course will provide the student with an overview and framework for understanding business analytics principles, as well as an introduction to strategies, tools, methods, and applications necessary to master the implementation of advanced enterprise analytics to gain competitive advantage.  The course outline will focus on contemporary Business Analytics theories and best practices.  Students will acquire knowledge of the tools, techniques, and processes needed to effectively employ business analytics applications such as machine learning, artificial intelligence, linear/non-linear programming, optimization, and root cause analysis.  Students completing this course will acquire the foundational knowledge needed to fulfill leadership roles in applying business analytics to support decision making in modern organizations.

This course focuses on the theories, strategies, tools, methods, and applications that comprise the Business Analytics or Enterprise Analytics body of knowledge.  Students in this course will review seminal literature and evaluate Business Analytics tools, techniques, knowledge, and skills.   课程 topics include the origins, philosophy, and best practices of Business Analytics, along with business analytics tools, techniques, and processes necessary to effectively employ applications such as machine learning, artificial intelligence, linear/non-linear programming, optimization, and root cause analysis in modern businesses.

先决条件： 

Prerequisite to this course, the student must have taken BUS3310  Competitive Analysis (or its equivalent) or earned a BBA degree

当然能力：

UPON COMPLETION OF THE COURSE, THE STUDENT WILL BE COMPETENT IN: 

1. Identifying and evaluating influential literature relevant to Business Analytics topics
2. Understanding universal ethical principles as applicable in the application of Business Analytics in modern organizations
3. Identifying, evaluating, and applying Business Analytics principles and techniques within the context of the modern organization
4. Understanding the implications of producing valuable insights for decision-making based on in-depth analyses of large amounts of internal and external data
5. Discussing the importance of engaging current and future stakeholders through building an organizational environment that balances decision support needs and expectations against the validity of the data sources available
6. Discussing the challenges of managing data science personnel and resources
7. Analyzing, evaluating, and applying stakeholders’ feedback to prioritize and improve future Business Analytics applications
8. Analyzing, evaluating, and applying Business Analytics best practices for effective coordination and informed decision making in modern organizations
9. Creating an environment of transparency, trust, and conflict resolution that promotes cultivates a culture of high performance in the application of Business Analytics
10. Producing and maintaining an evolving Business Analytics plan, from initiation to closure, based on organizational goals, values, risks, constraints, stakeholder feedback, and review of findings
11. Discussing the importance of evidenced based learning (inspection and adaptation) in Business Analytics applications
12. Integrating continuous improvement of quality, effectiveness, product value, process, and team concepts into Business Analytics