● Supply Chain Management 丨 供应链管理

Examination of concepts, issues, and methodologies related to design and administration of supply chain systems. Emphasis on executive-level decision making and the impact of supply chain management on organizational performance. Includes supply chain strategy, strategic alliances, supplier and customer relationships, use of technology, and the integration of logistical operations in the attainment of organizational objectives in a global competitive environment.

检查与供应链系统的设计和管理相关的概念、问题和方法。强调执行层面的决策和供应链管理对组织绩效的影响。包括供应链战略、战略联盟、供应商和客户关系、技术的使用以及在全球竞争环境中实现组织目标的物流运营整合。

● Process and Quality Improvement丨 过程和质量改善

Detailed examination of the issues, techniques, and methodologies for planning and controlling continuous quality improvement in manufacturing and service organizations.

详细检查在制造和服务组织中规划和控制持续质量改进的问题、技术和方法。

● Enterprise Resource Planning 丨 企业资源规划

Provides foundational skills and knowledge for enterprise process management using SAP’s Enterprise Resource Planning System (ERP). Laboratory exercises focus on the integration of data and processes into the design of SAP ERP software. Other topics include business process analysis, information technology implementation, and a survey of underlying technologies utilized by ERP systems. 

使用 SAP 的企业资源规划系统 (ERP) 为企业流程管理提供基础技能和知识。实验室练习侧重于将数据和流程集成到 SAP ERP 软件的设计中。其他主题包括业务流程分析、信息技术实施以及对 ERP 系统使用的基础技术的调查。

● MSDA 628X - Supply Chain Business Analytics (3) / MSDA 628X - 供应链商业分析（3）

Crosslisted as OMIS 628. Development and application of optimization methods to analyze supply chain issues. Covers linear programming, network optimization, integer programming, and nonlinear programming with an emphasis on model formulation, solution techniques, and interpretation of results.

OMIS 628的交叉课程。开发和应用优化方法来分析供应链问题。涵盖线性规划、网络优化、整数规划和非线性规划，重点介绍模型公式、求解技术和结果解释。

● MSDA 673 - Business Data Visualization (3) / MSDA 673 - 商业数据可视化（3）

Visualization design and evaluation principles. Creating visualizations of various types of data to unlock hidden patterns and implications. Comprehensive understanding of design principles for better communication using visualizations. Required use of Tableau, SAS Visual Analytics, and Python. Application of related concepts and techniques in case studies to lead data-driven decisions in the business context.

可视化设计和评估原理。创建各种类型数据的可视化以解开隐藏的模式和影响。全面了解设计原理，以便使用可视化进行更好的沟通。需要使用 Tableau、SAS Visual Analytics 和 Python。在案例研究中应用相关概念和技术，以在业务环境中引导数据驱动的决策。

● MSDA 690 - Data Analytics Project Management (3) / MSDA 690 - 数据分析项目管理（3）

Exploration of project management for the development of data analytical solutions, as well as using data analytics in support of project management decision-making. Project management concepts and methodologies will be examined including Agile and Scrum. Modern tools for the management of projects will be utilized. Topics will also include risk management and knowledge management as they relate to data analytics projects.

探索项目管理以开发数据分析解决方案，以及使用数据分析支持项目管理决策。将检查项目管理概念和方法，包括敏捷和 Scrum。将利用现代项目管理工具。主题还将包括与数据分析项目相关的风险管理和知识管理。

● MSDA 645X - Applied Statistics for Business Analytics Using SAS (3) / MSDA 645X - 使用SAS的商业分析的应用统计（3）

Crosslisted as OMIS 645. Comprehensive study of statistical methods in business analytics using SAS. Emphasis on the appropriate data analyses and interpretation of the results to assist business leaders in decision making.

● OMIS 645的交叉课程。

使用 SAS 对商业分析中的统计方法进行综合研究。强调适当的数据分析和结果的解释，以帮助企业领导者进行决策。

● MSDA 652X - Business Applications of Database Management Systems (3) / MSDA 652X - 数据库管理系统的商业应用（3）

Crosslisted as OMIS 652. Critical examination of the design, implementation, and management of database systems. Topics include the relational database model, entity-relationship modeling, normalization, the logical implementation of databases, transaction management, distributed databases, object-oriented databases, client/server systems, data warehousing, database administration, and the use of databases in Website design. Laboratory experience with current database software.

OMIS 652的交叉课程。对数据库系统的设计、实施和管理进行严格审查。主题包括关系数据库模型、实体关系建模、规范化、数据库的逻辑实现、事务管理、分布式数据库、面向对象的数据库、客户端/服务器系统、数据仓库、数据库管理以及数据库在网站设计中的使用。使用当前数据库软件的实践课程。

● OMIS 661 - Business Intelligence Applications and Tools （3） 丨 OMIS 661 - 商业智能应用和工具  （3）

Provides a foundation in the area of business intelligence (BI). Introduction to various BI technologies such as Microsoft SQL Server Management Studio, Analysis Services, Reporting Services, and/or SAP Business Objects to analyze enterprise data. Use of software tools to build an end-to-end BI solution.

提供商业智能（BI）领域的基础。介绍用于分析企业数据的各种BI技术，例如 Microsoft SQL Server Management Studio、Analysis Services、Reporting Services 和/或 SAP Business Objects。使用软件工具构建端到端的BI解决方案。

● MSDA 665X - Big Data Analytics for Business (3) / MSDA 665X - 商业大数据分析（3）

Crosslisted as OMIS 665. In-depth study of the concepts, methods, and tools for Data Science and Big Data Analytics with the focus on business scenarios. Topics include the Data Analytics Lifecycle, Basic Data Analytics Methods using the open-source RStudio, Advanced Analytics Theories and Methods including Clustering, Association Rules, Linear and Logistic Regression, Classification and Time Series Analysis, and Advanced Analytics Technology and Tools including the open-source software MapReduce and Hadoop.

OMIS 665的交叉课程。深入研究数据科学和大数据分析的概念、方法和工具，重点是商业场景。主题包括数据分析生命周期、使用开源 RStudio 的基本数据分析方法、高级分析理论和方法，包括聚类、关联规则、线性和逻辑回归、分类和时间序列分析，以及高级分析技术和工具，包括开放源软件 MapReduce 和 Hadoop。

● MSDA 681 - Machine Learning and Advanced Predictive Analytics (3) / MSDA 681 - 机器学习和高级预测分析（3）

Comprehensive study of analytical methods used by machine learning algorithms to predict future events or to discover meaningful patterns. Emphasis on configuring automated systems to process large volumes of data to build predictive models with minimal human intervention. Topics include common algorithms used in machine learning, predictive model assessment, and advanced topics in machine learning.

对机器学习算法用于预测未来事件或发现有意义模式的分析方法的综合研究。强调配置自动化系统来处理大量数据，从而以最少的人工干预构建预测模型。主题包括机器学习中使用的常用算法、预测模型评估和机器学习中的高级主题。

● MSDA 683X - Business Applications of Text Mining (3) / 文本挖掘的商业应用（3）

Crosslisted as OMIS 683. Introduction to the power of large amounts of text data and the computational methods to find patterns in such large texts using R. Focus will be geared more towards the application of various text mining techniques to business problems, rather than on the intricacies of different algorithms.

OMIS 683的交叉课程。介绍大量文本数据的力量以及使用 R 在如此大的文本中查找模式的计算方法。重点将更多地侧重于将各种文本挖掘技术应用于商业问题，而不是不同算法的复杂内容。

● Decision Analysis丨 决策分析

Analysis of the conceptual and analytical approaches to the solution of significant operations problems. Emphasis on case analyses and quantitative solutions within a global competitive environment.

分析解决重大运营问题的概念和分析方法。强调在全球竞争环境中的案例分析和定量解决方案。

● Service Operations丨 服务运营

Examination of the issues, problems, and possible solutions for operations managers in the manufacturing and service environments. Topics include product planning, facility location, process design, capacity planning, quality management, inventory management, and operations planning and control systems.

为生产和服务环境中的运营经理检查问题和可能的解决方案。主题包括产品规划、设施选址、工艺设计、产能规划、质量管理、库存管理以及运营规划和控制系统。

● Manufacturing Systems Management丨 制造系统管理

Study of operations strategy within the context of manufacturing and service organizations. Emphasis on the use of case analysis in strategy development and policy formulation.

在制造和服务组织的背景下研究运营战略。强调在战略制定和政策制定中使用案例分析。

● Data Visualization丨 数据可视化

Visualization design and evaluation principles. Creating visualizations of various types of data to unlock hidden patterns and implications. Comprehensive understanding of design principles for better communication using visualizations. Required use of Tableau, SAS Visual Analytics, and Python. Application of related concepts and techniques in case studies to lead data-driven decisions in the business context.

可视化设计和评估原则。创建各种类型数据的可视化，以解开隐藏的模式和影响。全面了解设计原则，以便使用可视化进行更好的沟通。需要使用 Tableau、SAS Visual Analytics 和 Python。在案例研究中应用相关概念和技术，以在业务环境中引导数据驱动的决策。

● Business Intelligence丨 业务智能

Provides a foundation in the area of business intelligence (BI). Introduction to various BI technologies such as Microsoft SQL Server Management Studio, Analysis Services, Reporting Services, and/or SAP Business Objects to analyze enterprise data. Use of software tools to build an end-to-end BI solution.

提供商业智能 (BI) 领域的基础。介绍用于分析企业数据的各种 BI 技术，例如 Microsoft SQL Server Management Studio、Analysis Services、Reporting Services 和/或 SAP Business Objects。使用软件工具构建端到端的 BI 解决方案。

● Predictive Analytics丨 预测式分析

Comprehensive study of analytical methods used by machine learning algorithms to predict future events or to discover meaningful patterns. Emphasis on configuring automated systems to process large volumes of data to build predictive models with minimal human intervention. Topics include common algorithms used in machine learning, predictive model assessment, and advanced topics in machine learning.

对机器学习算法用于预测未来事件或发现有意义模式的分析方法的综合研究。强调配置自动化系统来处理大量数据，从而以最少的人工干预构建预测模型。主题包括机器学习中使用的常用算法、预测模型评估和机器学习中的高级主题。

● Business Systems Analysis, Design and Development丨 业务系统分析，设计和开发

Comparison of various methodologies and techniques used in the analysis and design of business systems with emphasis on selecting appropriate techniques for evolving development contexts. Projects utilize these analysis and design techniques to identify business problems and design appropriate solutions. Emphasis on teamwork and communication skills.

比较在业务系统的分析和设计中使用的各种方法和技术，重点是为发展的开发环境选择合适的技术。项目利用这些分析和设计技术来识别业务问题并设计适当的解决方案。强调团队合作和沟通技巧。