【人工智能】面向调线调坡的点云大数据分析及深度模型研究

复旦大学：《商务智能》课程PPT教学课件（商务数据分析）密度聚类——算法详解

复旦大学：《商务智能》课程PPT教学课件（商务数据分析）09 搜索引擎优化 Search Engine Optimization

复旦大学：《商务智能》课程PPT教学课件（商务数据分析）08 知识管理

• Web挖掘的概念 • Web内容挖掘 • Web结构挖掘 • Web日志挖掘

复旦大学：《商务智能》课程教学大纲（混合教学）商务数据分析 Business Intelligence

《应用统计学》 《经管类高等数学选讲(1)》 《经管类高等数学选讲(2)》 《Python 语言程序设计》 《微积分 B》 《应用工程数学 B》 《微积分 A》 《机电设备》 《跨境电商实战》 《商业智能数据分析》

油气资源大数据智能平台的总体框架应以数据资源为基础、大数据平台算力为支撑、人工智能算法为核心，面向油气行业生产需求，构建集勘探、开发、生产数据于一体的油气数据资源池，通过数据清洗与融合提升数据质量，整合物理模拟与数据挖掘等手段，实现服务功能模块化，并在PC端、管控大屏、手机移动APP等多维平台实现智能监测、预警与展示。通过对深度学习等人工智能方法在油气工业领域的应用案例分析，表明其具有较好的应用前景。未来石油公司应与科研院所通力合作，挖掘石油工业数据的巨大潜能，实现降本增效，建设全新的智能油气工业生态圈，完成产业升级

6.1 Understand the applications of prescriptive analytics techniques in combination with reporting and predictive analytics 6.2 Understand the basic concepts of analytical decision modeling 6.3 Understand the concepts of analytical models for selected decision problems, including linear programming and simulation models for decision support 6.4 Describe how spreadsheets can be used for analytical modeling and solutions 6.5 Explain the basic concepts of optimization and when to use them 6.6 Describe how to structure a linear programming model 6.7 Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking 6.8 Understand the concepts and applications of different types of simulation 6.9 Understand potential applications of discrete event simulation

7.1 Learn what Big Data is and how it is changing the world of analytics 7.2 Understand the motivation for and business drivers of Big Data analytics 7.3 Become familiar with the wide range of enabling technologies for Big Data analytics 7.4 Learn about Hadoop, MapReduce, and NoSQL as they relate to Big Data analytics 7.5 Compare and contrast the complementary uses of data warehousing and Big Data technologies 7.6 Become familiar with select Big Data platforms and services 7.7 Understand the need for and appreciate the capabilities of stream analytics 7.8 Learn about the applications of stream analytics