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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/dwrelational
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课程名称：关系数据库支持数据仓库 概述：关系数据库支持数据仓库是商业智能数据仓库专业化的第三门课程。在本课程中，学员将运用SQL的分析元素来回答商业智能问题。课程将介绍用于管理商业智能报告中常用汇总数据的关系数据库管理系统的特性。由于数据仓库实施管理的重要性和复杂性，课程还将深入探讨存储架构、可扩展的并行处理、数据治理和大数据的影响。在课程中的任务中，学员可以选择使用Oracle或PostgreSQL。 课程大纲： 1. **DBMS扩展与示例数据仓库**：介绍课程和相关概念，为接下来的内容奠定基础。包括DBMS扩展、模式模式回顾、案例研究的实践问题与任务，以及教育和医疗领域的数据仓库示例。 2. **SQL小计运算符**：学习SQL SELECT语句的小计运算符扩展，并通过实际和评分问题应用所学内容，包括CUBE、ROLLUP和GROUPING SETS运算符。 3. **SQL分析函数**：掌握SQL分析函数的扩展处理模型，支持商业智能应用中的常见分析。通过实践和评分问题学习如何对商业单位进行定性排名，以及窗口比较和定量贡献的分析。 4. **物化视图处理与设计**：学习DBMS扩展，提高查询执行效率。包括创建物化视图的SQL语句、物化视图的处理要求及重写查询的规则，提升查询性能。 5. **物理设计与治理**：探讨物理设计技术和数据治理实践，了解存储架构、可扩展的并行处理、大数据问题及数据治理的概念问题。 6. **数据挖掘输入的SQL**：提供先进的查询制定材料，帮助学员在职场中获得更高的专业地位和价值。课程内容包括数据湖和数据仓库的实例、SQL编码技巧、复杂SQL编程管理以及特定的查询模式。 此课程旨在帮助学员深入理解数据仓库的关系数据库支持，通过理论与实践相结合的方式，掌握商业智能所需的SQL技能和数据管理知识。
Name:DBMS Extensions and Example Data Warehouses
Description:Module 1 introduces the course and covers concepts that provide a context for the remainder of this course. In the first two lessons, you’ll understand the objectives for the course and know what topics and assignments to expect. In the remaining lessons, you will learn about DBMS extensions, a review of schema patterns, data warehouses used in practice problems and assignments, and examples of data warehouses in education and health care. This informational module will ensure that you have the background for success in later modules that emphasize details and hands-on skills. You should also read about the software requirements in the lesson at the end of module 1. I recommend that you install Oracle Cloud or PostgreSQL this week before assignments begin in week 2. If you have taken other courses in the specialization, you may already have installed Oracle Cloud or PostgreSQL.
Name:SQL Subtotal Operators
Description:Now that you have the informational context for relational database support of data warehouses, you’ll start using relational databases to write business intelligence queries! In module 2, you will learn an important extension of the SQL SELECT statement for subtotal operators. You’ll apply what you’ve learned in practice and graded problems using SQL (Oracle or PostgreSQL) for problems involving the CUBE, ROLLUP, and GROUPING SETS operators. Because the subtotal operators are part of the SQL standard, your learning will readily apply to other enterprise DBMSs. At the end of this module, you will have solid background to write queries using the SQL subtotal operators as a data warehouse analyst.
Name:SQL Analytic Functions
Description:After your experience using the SQL subtotal operators, you are ready to learn another important SQL extension for business intelligence applications. In module 3, you will learn about an extended processing model for SQL analytic functions that support common analysis in business intelligence applications. You’ll apply what you’ve learned in practice and graded problems using SQL (Oracle or PostgreSQL) for problems involving qualitative ranking of business units, window comparisons showing relationships of business units over time, and quantitative contributions showing performance thresholds and contributions of individual business units to a whole business. Because analytic functions are part of the SQL standard, your learning will apply to other enterprise DBMSs. At the end of this module, you will have solid background to write queries using the SQL analytic functions as a data warehouse analyst.
Name:Materialized View Processing and Design
Description:After acquiring query formulation skills for development of business intelligence applications, you are ready to learn about DBMS extensions for efficient query execution. Business intelligence queries can use lots of resources so materialized view processing and design has become an important extension of DBMSs. In module 4, you will learn about an SQL statement for creating materialized views, processing requirements for materialized views, and rules for rewriting queries using materialized views. To gain insight about the complexity of query rewriting, you will practice rewriting queries using materialized views. To provide closure about relational database support for data warehouses, you will learn about about Oracle tools for data integration, the Oracle Data Integrator, along with two SQL statements useful for specific data integration tasks. After this module, you will have a solid background to use materialized views to improve query performance and deploy the Extraction, Loading, and Transformation approach for data integration as a data warehouse administrator or analyst.
Name:Physical Design and Governance
Description:Module 5 continues the course with a return to conceptual material about physical design technologies and data governance practices. You will learn about storage architectures, scalable parallel processing, big data issues, and data governance. After this module, you will have background about conceptual issues important for data warehouse administrators.
Name:SQL for Data Mining Input
Description:Module 6 provides optional advanced material on query formulation for learners who seek expert level knowledge and skills. Advanced query formulation can help learners gain an edge in the workplace for expert status and high value to an organization. Module 6 covers original material for advanced query formulation skills that prepare learners to collaborate with data scientists on data mining tasks. The instructor developed material in Module 6 from his long experience using SQL for data mining projects. The SQL coding skills also transfer to other advanced query formulation tasks. Module 6 provides these specific knowledge areas and skills.• Examples and practice with data lakes and data warehouses as data mining projects can involve both types of data sources• SQL coding skills for two prominent data mining tasks, association rule mining and classification algorithms using training data with limited event history• New SQL elements for managing complex SQL coding, array results, independent subqueries with the IN comparison operator, a new analytic function, and conditional assignment of column values• New SQL coding skills for atypical join patterns• Unique pedagogy with statement patterns to write template SELECT statements as an initial step to a complete a SELECT statementDue to advanced material, Module 6 provides Lesson 9 as honors with problems, concept quiz, assignment, and self-evaluation. The concept quiz provides an assessment of learner understanding of the video lessons and associated notes. Learners should complete the concept quiz before starting practice problems and the graded assignment to ensure conceptual understanding of the material.
Relational Database Support for Data Warehouses is the third course in the Data Warehousing for Business Intelligence specialization. In this course, you'll use analytical elements of SQL for answering business intelligence questions. You'll learn features of relational database management systems for managing summary data commonly used in business intelligence reporting. Because of the importance and difficulty of managing implementations of data warehouses, we'll also delve into storage architectures, scalable parallel processing, data governance, and big data impacts. In the assignments in this course, you can use either Oracle or PostgreSQL.
