● MSDA 645X —Applied Statistics for Business Analytics Using SAS (3)丨OMIS 645 — 使用 SAS 的商业分析应用统计 (3)

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.

使用 SAS 全面研究商业分析中的统计方法。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 649 — Business Computing Environment (3)丨OMIS 649 — 商业计算环境 (3)

Introduces students to the fundamentals of data management and analysis using SAS and Python. Emphasis will be placed on the management of large distributed data sets and data manipulation, including reading, processing, recoding, and reformatting of data. Topics of this course include: advanced programming, handling big data, performing complex mathematics, and optimizing SAS and Python programs.

向学生介绍使用 SAS 和 Python 进行数据管理和分析的基础知识。 重点将放在大型分布式数据集和数据操作的管理上，包括数据的读取、处理、重新编码和重新格式化。 本课程的主题包括：高级编程、处理大数据、执行复杂数学以及优化 SAS 和 Python 程序。

● MSDA 681 — Advanced Predictive Data Analytics for Business (3)丨OMIS 681 — 商业高级预测数据分析 (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 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。

● MSDA 683X — 文本挖掘的商业应用 (3)丨OMIS 683 -- Business Applications for Text Mining (3)

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.

介绍大量文本数据的威力以及使用 R 在如此大的文本中查找模式的计算方法。重点将更多地放在针对业务问题的文本挖掘技术的应用，而不是不同算法的复杂性。