Business Analytics for Managers
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 and evaluate opportunities, and work effectively with data scientists to realize those opportunities.
Upon completion of this course, students will be able to:
• Contrast various data analytic tasks (classification, prediction, association rules, and cluster analysis) in terms of the type and structure of the data, purpose, expected output, underlying algorithms, and common business and public sector applications.
• Translate a business problem into an analytic task, determine the needed data, build a model using XLMiner, evaluate the model’s performance, and identify deployment concerns.
• Interpret the results of a variety of predictive analytic models.
• Explain the value of each stage defined by the Cross Industry Standard Process for Data Mining (CRISP-DM) and how to use this framework to structure a data analytic problem.
• Understand various data management challenges including data availability, data bias, data integration, and data governance. Describe common approaches (technical and organizational) for addressing those challenges.
• Evaluate proposals for data mining projects (spot obvious flaws, unrealistic assumptions, missing pieces) and assess the business value and business risk of the proposal.
90728, 93732, 91801, or 90838