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

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

采用水热法和还原氮化法合成了菊花状形貌的氮化钛（TiN）纳米材料，并将其与还原氧化石墨烯（rGO）水热复合制备了氮化钛–还原氧化石墨烯（TiN-rGO）复合材料。利用扫描电镜、X射线衍射、X射线光电子能谱等测试方法对材料的形貌和物相进行了表征和分析。结果表明，TiN-rGO复合材料很好地保持了TiN菊花状的三维结构和rGO透明褶皱的形貌，且层状的rGO均匀地包覆在了菊花状的TiN的周围。用TiN-rGO复合材料修饰玻碳电极（GCE）制得了TiN-rGO/GCE电化学传感器，用于测定人体中的生物小分子DA和UA。由于复合材料中TiN和rGO的协同效应，构建的电化学传感器表现出了优秀的电化学性能。检测结果表明：TiN-rGO/GCE传感器对DA和UA的检测限分别为0.11和0.12 μmol·L?1，线性范围分别为0.5～210 μmol·L?1和5～350 μmol·L?1，且具有良好的抗干扰性、重现性和稳定性，且成功应用于人体内真实样品的DA和UA检测

以Al作为研究对象，采用EAM势实施分子动力学模拟.在Ⅰ型和Ⅱ型加载条件下，研究了温度和取向对位错发射、裂纹脆性及韧性扩展的影响.模拟结果表明，升高温度，发射位错的临界应力强度因子按指数规律降低.加载速率在一定范围内将影响临界应力强度因子.临界应力强度因子随着加载速率的增大而增大

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

天津钢管集团股份有限公司150t电弧炉炼钢长期使用四元炉料,其平均炉料配入比为46.2%废钢、9.1%生铁、26.4%热铁水、18.3%DRI,通过物料衡算和能量衡算分析了电弧炉冶炼过程的物耗和能量状况.论文研究得出了单位炉料对物耗和能耗的影响,以及四元炉料结构变化对吨钢电功率输入的需求范围为153~744kW.对大量生产数据进行逐步回归分析,结果表明:直接还原铁配入比RDRI的影响最为显著,每增加1.0%,吨钢电耗大约增加1.4kWh;其次是铁水配入比RHM,其每增加1.0%,吨钢电耗大约降低2.0kWh

分析了单机架平整机轧辊的实际辊形、磨损状况,并通过运用有限元模型计算辊缝曲线的2次和4次分量研究了辊形对板形的影响.结果表明,工作辊磨损较轻,对辊形影响不大,但原始辊形与目标辊形差别较大,是产生1/4浪板形缺陷的因素之一;支持辊局部磨损较严重,但这种不均匀磨损对板形影响较小,在整个服役期内对板形的作用基本不变

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