Business Analytics for Managers
91-729
Units: 6
Description
Business analytics is defined by Thomas Davenport as “the broad use of data and quantitative analysis for decision making within organizations.” Business analytics encompasses both the reporting of performance and the attempt to understand and predict it, emphasizing statistically and mathematically derived insights. This course will cover the underlying fundamental concepts and principles behind business analytics, focusing on those the manager needs to understand to both envision opportunities, and work effectively with data scientists to realize those opportunities. In addition, the course will provide students skill development in the use of an Excel-based analytic tool, XLMiner.
Learning Outcomes
- Translate a business problem into a predictive analytic task, determine the needed data, assess machine learning methods for the task, and identify expected benefits and risks.
- Explain the value of each stage defined by the Cross Industry Standard Process for Data Mining (CRISP-DM).
- Identify how best to use predictive analytics to enhance decision making in your organization.
- Understand various issues raised from the use of predictive analytics including data bias, equity, privacy, and lack of predictive transparency.
- Describe current and evolving approaches (technical and organizational) for addressing those challenges.
- Develop a knowledge base that will serve as a foundation to build on as the field of predictive analytics continues to evolve.
Prerequisites Description
- Introduction to Database Management (90-728 or 93-732);
- Statistics core course (91-801, 90-707 or 90-711)