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

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

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

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

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

环境搭建 关于Docker 安装Docker Docker 命令 Jupyter Jupyter 界面 Jupyter 单元格 Tensorflow 的辅助支持库 NumPy NumPy 数据 NumPy 运算 NumPy 索引 NumPy 合并与分割 Pandas Pandas 对象 Pandas 选择数据 Matplotlib

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

1. 从Netflix Prize说起 2. 集成学习的基本思想 1. 集成学习为何有效 2. 如何构建不同的基学习器 3. 如何综合多个基学习器 3. Bagging 1. Bagging的基本思想 2. 随机森林以及在R中的实际应用 4. Boosting 1. Boosting的基本思想 2. AdaBoost以及在R中的实际应用 5. Stacking 6. 小结以及实用技巧

EDA（Electronics Design Automation）即电子 设计自动化技术，是一种以计算机为基本工 作平台，利用计算机图形学、拓扑逻辑学、 计算数学以及人工智能学等多种计算机应用 学科的最新成果而开发出来的一整套软件工 具，是一种帮助电子设计工程师从事电子元 件产品和系统设计的综合技术