M.S. 数据科学 & 分析

This concentration prepares students to transform data into actionable information for organizations seeking data-driven recommendations. 本课程介绍用于存储的方法和工具, 访问, 并分析数据以支持业务决策. 学生们学习如何识别, 管理, 检索, and analyze data in order to gain insight and use the resulting information to make informed business decisions. Students select four courses (12 credits) in consultation with the faculty advisor.

选择两门课程(6学分):

• BNAL 503数据探索与可视化(3学分)
• BNAL 515 Advanced Business 分析 with Big Data Applications (3学分)
• BNAL 721 模拟 建模 for Business Systems (3学分) is preferred, 但如果没有提议, BNAL 576 模拟 建模 and Analysis for Business Systems (3 Credits) may be substituted for BNAL 721 with permission of the concentration coordinator if BNAL 721 is not offered.

选择两门课程(6学分):

• IT 650数据库管理系统(3学分)
• 商业智能(3学分)
• IT 652 Information and Communications 技术 for Big Data (3学分)

The purpose of this concentration is to prepare students to enter rapidly emerging fields related to data science and analytics. The coursework addresses relevant data analytics topics such as video analytics, 算法和数据结构, 信息检索. Students learn computational data analysis, data visualization, and natural language 处理.

Students select four courses from the list below (12 credits) in consultation with the faculty advisor:

• CS 522机器学习I(3学分)
• CS 532网络科学(3学分)
• CS 550数据库概念(3学分)
• cs569网络安全数据分析(3学分)
• CS 580 Introduction to Artificial Intelligence (3学分)
• CS 722机器学习II(3学分)
• CS 725信息可视化(3学分)
• 自然语言处理(3学分)
• CS 734信息检索(3学分)

The purpose of this concentration is to provide students with a thorough understanding of the methods and technologies to handle big data and to instill 工程 problem-solving skills rooted in big data solutions. It will further prepare them to become professionals trained in advanced data analytics, with the ability to transform large streams of multiple data sources into understandable and actionable information for the purpose of making decisions. The coursework (12 credits) will enable students to learn and practice the following competencies: data 集合, 数据存储, 处理和分析数据, 报告统计数据和模式, drawing conclusions and insights and making actionable recommendations.

从(6学分)中选择两门核心课程:

• ENMA 754大数据基础(3学分)
• MSIM 715 High Performance Computing and 模拟 (3学分)
• ECE 607机器学习1(3学分)

选择两门选修课程(6学分):

• ECE 784计算机视觉(3学分)
• MSIM 695 Topics in Visualization for 大数据分析 (3学分)
• MSIM 574交通数据分析(3学分)
• MAE 740 Autonomous and Robotic Systems Analysis and Control (3学分)
• 并行集群计算方法(3学分)
• ECE 651统计分析与仿真(3学分)
• ECE 780机器学习II(3学分)

This concentration enables MS Data Science students to develop advanced skills and expertise in geospatial science and technology. 整合地理资讯系统(GIS), 遥感, and location-based data allows data scientists to uncover spatial patterns. The concentration provides for a foundation across the breadth of geospatial technology to prepare data for analysis, 进行适用性分析, 空间预测建模, 地质统计学, 以及时空模式挖掘和目标检测. The concentration coursework (12 credits) incorporates advanced geovisualization and webmapping technology to also enhance cartography analytics and communications.

Required core courses for this concentration (6 credits):

• GEOG 600地理空间数据分析(3学分)
• GEOG 601空间统计与建模(3学分)

选修两门课程(6学分):

• GEOG 525 Internet Geographic Information Systems (3学分)
• GEOG 532 Advanced GIS(3学分)
• 高级空间分析(3学分)
• GEOG 590应用GIS/制图(3学分)
• GEOG 5XX编程GIS(3学分)
• GEOG 519 Spatial Analysis of Coastal Environments (3学分)
• GEOG 520海洋地理(3学分)
• 地理信息系统在应急管理(3学分)
• 主题:地理空间领域技术(3学分)

总集中学时要求:12

DASC顶点项目 (3学分)
硕士DASC学位也需要一个顶点项目. 为寻求这种专注的学生, they must complete a project focusing on geospatial analysis when taking the following:

DASC 690顶点项目