This course focuses on spreadsheet modeling to support decision making by organizations in service industries, 比如医疗保健, 银行, 分布, 和教育. 学生培养批判性思维和解决问题的能力，以解决现实世界的问题. 所获得的电子表格建模功能对管理人员和管理员来说非常实用. 课程主题涵盖显示图表、数据探索. 决策逻辑, 引用函数, 贷款和投资的财务影响, 项目管理, 假设分析, 目的寻求. Visual basic编程等高级工具.
This course discusses the 过程 of business analytics by developing a business intelligence solution, 包括问题定义, 数据准备, 描述性和预测性分析, 评价结果, 实现和部署. Data-oriented methods using spreadsheet and structured query language (SQL) are emphasized for business transaction capturing, 数据聚合和在线分析处理（OLAP）. 学生将在数据仓库的开发中使用各种软件工具, 包括ETL（提取）, 转换和加载)和可视化数据表示(例如.g.，数据集).
Novel problems require innovative solutions - this course introduces students to the power and flexibility of programming and scripting languages such as R and Python, 应用于商业分析中的问题. 学生将学习如何获取和部署与他们的问题相关的软件包, 然后将它们与SQL等工具一起使用来收集和准备数据, 根据具体需求定制分析, 并创建有效传达结果的输出.
This course introduces students to the concept of social media analytics and 技术 used to 分析 social media data such as texts, 网络, 和行动. Students will learn how to extract data from popular social media platforms and 分析 such data using software tools such as R to identify trends, 情绪, 意见领袖和社区.
本课程向学生介绍数据可视化和仪表板. 学生将学习数据可视化的最佳实践, 使用结构化查询语言（SQL）进行数据检索, 提高分析能力, 并学习如何设计仪表板来支持管理决策. 学生将有机会获得数据检索和可视化方面的实践经验. Students will use Tableau as their main tool for 数据可视化 and dashboarding but will develop transferable skills which can apply to most common software packages in the field.
This course provides students with knowledge and skills in the various decision analytical 技术 for managerial decision making including big data analytics. 许多定义良好的数据挖掘技术，如分类, 估计, 预测, 亲和分组和集群, 以及数据可视化. 数据挖掘跨行业标准流程（CRISP-DM）也将被讨论. The data mining 技术 will be applied to diverse business applications including: target marketing, 信用风险管理, 信用评分, 欺诈检测, 医学信息学, 电信和网络分析.
This course introduces students to the management and coordination of enterprise data resources to improve enterprise-wide decision-making. 学生将学习如何从企业数据中识别关键绩效指标, 如何将企业分析与其他形式的分析区分开来, how to determine what proprietary data will provide analytical advantage to maximize the impact on the enterprise, 最新的分析技术和来自最新案例的最佳实践. Students will engage in an iterative 过程 of exploring data from multiple functional areas within an organization to derive actionable insights as well as communicate findings to help enterprises improve the quality of their decisions.
This course provides students with comprehensive understanding of problems and solutions related to information 安全 and information assurance in organizational contexts. Students learn how to conduct quantitative and qualitative 安全 risk assessment analyses related to site safety and 安全, 硬件和软件的可靠性和风险, 以及网络的可靠性和安全性. Students will carry out data collection and analytics methodologies which address expected failures, 发作的发生率和严重程度, 事故和自然行为, 以及它们对运营和预算的影响.
介绍统计方法，包括概率概念, 推理技术, 方差分析, 回归分析, 卡方分布和其他非参数分析. 本课程侧重于使用计算机进行统计分析.
*没有统计学背景的学生建议选修QUMT 6303
This course introduces students to modern machine-learning methods that can be applied to build predictive models & 发现数据中的模式，以便更好地进行业务决策. Students will learn implementation of the machine learning 技术 in R programming language for understanding complex datasets. This course will enable students to approach business problems by identifying opportunities to derive business value from data-driven business intelligence. 先决条件：QUMT 6303或QUMT 3341或同等学历.
本课程介绍规范分析的原理和技术. 它们为业务实体和政策制定者提供了评估绩效的合理工具, 做决定, 设计策略, 管理风险. Students will learn how to use analytical models to evaluate uncertainty that is prevalent in many business decisions. 因为业务问题通常有可选的解决方案, students will learn how to use analytical models to assess various business solutions and identify the best course of action. This course involves a hands-on learning experience with spreadsheet modeling and other analytical packages. The emphasis is on how to employ these analytical methods to facilitate managerial decision making in diverse industries and functional areas.
