4.1 Define data mining as an enabling technology for business analytics 4.2 Understand the objectives and benefits of data mining 4.3 Become familiar with the wide range of applications of data mining 4.4 Learn the standardized data mining processes 4.5 Learn different methods and algorithms of data mining 4.6 Build awareness of the existing data mining software tools 4.7 Understand the privacy issues, pitfalls, and myths of data mining

3.1 Understand the basic definitions and concepts of data warehousing 3.2 Understand data warehousing architectures 3.3 Describe the processes used in developing and managing data warehouses 3.4 Explain data warehousing operations 3.5 Explain the role of data warehouses in decision support

2.1 Understand the nature of data as it relates to business intelligence (BI) and analytics 2.2 Learn the methods used to make real-world data analytics ready 2.3 Describe statistical modeling and its relationship to business analytics 2.4 Learn about descriptive and inferential statistics 2.5 Define business reporting, and understand its historical evolution

5.1 Describe text mining and understand the need for text mining 5.2 Differentiate among text analytics, text mining, and data mining 5.3 Understand the different application areas for text mining 5.4 Know the process of carrying out a text mining project 5.5 Appreciate the different methods to introduce structure to text-based data 5.6 Describe sentiment analysis 5.7 Develop familiarity with popular applications of sentiment analysis 5.8 Learn the common methods for sentiment analysis 5.9 Become familiar with speech analytics as it relates to sentiment analysis

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

8.1 Explore some of the emerging technologies that may impact analytics, business intelligence (BI), and decision support 8.2 Describe the emerging Internet of Things (IoT) phenomenon, potential applications, and the IoT ecosystem 8.3 Describe the current and future use of cloud computing in business analytics 8.4 Describe how geospatial and location-based analytics are assisting organizations 8.5 Describe the organizational impacts of analytics applications 8.6 List and describe the major ethical and legal issues of analytics implementation 8.7 Identify key characteristics of a successful data science professional

1.1 Understand the need for computerized support of managerial decision making 1.2 Recognize the evolution of such computerized support to the current state—analytics/data science 1.3 Describe the business intelligence (BI) methodology and concepts 1.4 Understand the various types of analytics, and see selected applications 1.5 Understand the analytics ecosystem to identify various key players and career opportunities

《专业导论》课程大纲. 1 《管理学》课程大纲. 6 《会计学基础》课程大纲. 28 《经济法》课程大纲. 40 《微观经济学》课程大纲. 51 《宏观经济学》课程大纲. 64 《统计学》课程大纲. 75 《金融学》课程大纲. 88 《中级财务会计（一）》课程大纲. 109 《中级财务会计（二）》课程大纲. 121 《财务管理学》课程大纲. 132 《成本与管理会计》课程大纲. 146 《财务分析》课程大纲. 158 《公司治理》课程大纲. 168 《高级财务管理》课程大纲. 179 《资产评估》课程大纲. 193 《计量经济学》课程大纲. 207 《python 在财务管理中应用》课程大纲.219 《会计综合模拟实训》课程大纲. 227 《大数据财务分析与决策》课程大纲.232 《ERP 沙盘模拟实训》课程大纲.240 《经济管理数据分析》课程大纲. 247 《市场营销》课程大纲. 257 《资本市场》课程大纲. 269 《高级财务会计》课程大纲. 278 《管理会计信息化》课程大纲. 289 《虚拟商业社会环境（vbse）》课程大纲.298 《会计信息系统实训》课程大纲. 311 《论文写作专题》课程大纲. 324 《财务决策支持系统实训》课程大纲.331 《财务共享理论与实务》课程大纲. 338 《国际财务管理》课程大纲. 347 《组织行为学》课程大纲. 356 《晋商票号文化》课程大纲. 370 《税法》课程大纲. 382 《证券投资学》课程大纲. 390 《会计》课程大纲. 405 《审计原理》课程大纲. 414 《公司战略与风险管理》课程大纲. 427 《大数据审计》课程大纲. 436 《审计实务》课程大纲. 441 《证券市场基础》课程大纲. 452 《投资学原理与应用》课程大纲. 465 《商业银行业务与经营》课程大纲. 476 《金融大数据》课程大纲. 487 《公司金融》课程大纲. 493 《税法（一）》课程大纲. 503 《税法（二）》课程大纲. 509 《纳税筹划》课程大纲. 518 《财税一体化实训》课程大纲. 530 《纳税实务》课程大纲. 534 《学年论文》课程大纲. 541 《财务管理课程设计》课程大纲. 544 《专业实习》课程大纲. 548 《毕业实习》课程大纲. 555 《毕业论文（设计）》课程大纲. 560

在地下水研究中应用GIS技术,是由于地下水研究需要组织、定量和解释大量的水文 地理数据。早期的地下水模拟研究需要把地图图表等信息转换成计算机可读的格式。这 些工作费时、冗长,并且容易出现误差。水文信息如降水、参数信息(如水力传导度)、参 数格式(如井的位置和流量值)以及辅助信息(如边界条件)都需要海量数据的组织和管 理。事实上,所有这些信息都是空间分布的,在某些情况下,还是时间分布的。其中许多 数据在计算机中以地图形式存在,如栅格形式矢量格式或数据表形式的图像。由于计算 机图形技术的发展,现在这些信息可以有效地通过GIS系统来存取