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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/getting-started-with-data-warehousing-and-bi-analytics
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课程名称：数据仓库与商业智能分析入门 课程概述：数据是组织最宝贵的资产之一。组织如何有效利用数据？如何确定哪些数据在商业决策中是最新、准确和有用的？本课程将帮助您了解各种数据存储库，包括数据集市、数据湖和数据水库，并解释它们的功能和用途。 数据仓库是一个大型的数据存储库，存储经过清洗并达到一致质量的数据。并非所有数据存储库都以相同的方式使用或在选择存储的数据时需要相同的严谨性。数据仓库旨在通过准确和灵活的报告及数据分析来促进快速的商业决策，是当前最基本的商业智能工具之一，成功的数据工程师必须理解它。 您还将学习到数据仓库如何为组织的当前和历史数据提供单一的数据真相来源。组织通过分析和商业智能应用生成数据价值。通过体验ELT过程，您将获得使用IBM Cognos及其报告、仪表板功能（包括可视化能力）的实际分析和商业智能经验。 最后，您将完成一个可分享的最终项目，以展示您在每个模块中所获得的技能。 课程大纲： 1. 数据仓库、数据集市和数据湖 - 该模块介绍数据仓库系统、数据湖和数据集市。您将能够识别和比较这三种数据存储实体的架构，并理解组织如何从中受益。您还将了解如何评估新的数据仓库系统。 2. 数据仓库的设计、建模与实施 - 本模块探讨企业数据仓库架构的基本知识，学习如何利用数据立方体和星型模式进行数据管理。您将了解数据组织、规范化及其如何创建雪花模式，并掌握数据仓库的填充和查询过程。 3. 数据仓库分析 - 本模块将通过使用IBM Cognos Analytics获得数据分析经验。您将创建可视化并建立简单仪表板，体验平台的高级功能。 4. 最终作业与考试 - 在此模块中，您将完成最终项目，设计并加载数据到数据仓库，编写聚合查询，并使用IBM Cognos创建分析仪表板。 通过本课程，您将全面掌握数据仓库和商业智能分析的核心概念与实践。
Name:Data Warehouses, Data Marts, and Data Lakes
Description:Welcome to your first module! This module provides a gentle but thorough introduction to data warehouse systems, data lakes, and data marts. When you complete this module, you’ll be able to identify and compare data warehouse systems, data mart, and data lake architecture, and understand how organizations can benefit from each of these three data storage entities. Optionally, you’ll explore the workings of IBM Db2 data warehouse system architecture, view use cases, and understand the key capabilities and integrations available with IBM Db2 Warehouse. Then, you’ll learn about three types of data warehouse systems and popular data warehouse system vendors. You will be ready to help your organization assess new data warehouse system offerings when you know the five essential, critical criteria, including total cost of ownership, to evaluate before changing to a new data warehouse system.
Name:Designing, Modeling and Implementing Data Warehouses
Description:In this knowledge-packed module, you’ll explore general and reference enterprise data warehousing architecture. You’ll discover how data cubes relate to star schemas. Then you’ll learn how to slice, dice, drill up or down, roll up, and pivot relative to data cubes. Next, you will examine the capabilities of materialized views, their benefits, and how to apply them. You’ll learn how data organization using facts and dimensions and their related tables organizes information. Then, you will explore how to use normalization to create a snowflake schema as an extension of the star schema. You will learn about populating a data warehouse, incremental data updates, verifying data, querying data, interpreting an entity-relationship diagram for a star schema, creating a materialized view, and applying the CUBE and ROLLUP options. You’ll also discover how organizations can benefit by implementing staging.
Name:Data Warehouse Analytics
Description:In this module, you’ll fast-track your data analytics learning and gain hands-on data analytics experience using IBM Cognos Analytics. After registering with Cognos Analytics, you’ll explore the platform’s capabilities by creating visualizations, building a simple dashboard, and trying out its advanced features.
Name:Final Assignment and Final Quiz
Description:In this module, you’ll complete your final course project, which brings together concepts and practices you previously learned in the first three modules. In this final project, you will design and load data into a data warehouse using facts and dimension tables. Then you’ll write aggregation queries using CUBE and ROLLUP functions and create materialized query tables, known as a materialized view. You will complete your project by using IBM Cognos to create an analytics dashboard.
Data is one of an organization’s most valuable commodities. But how can organizations best use their data? And how does the organization determine which data is the most recent, accurate, and useful for business decision making at the highest level? After taking this course, you will be able to describe different kinds of repositories including data marts, data lakes, and data reservoirs, and explain their functions and uses. A data warehouse is a large repository of data that has been cleaned to a consistent quality. Not all data repositories are used in the same way or require the same rigor when choosing what data to store. Data warehouses are designed to enable rapid business decision making through accurate and flexible reporting and data analysis. A data warehouse is one of the most fundamental business intelligence tools in use today, and one that successful Data Engineers must understand. You will also be able to describe how data warehouses serve a single source of data truth for organization’s current and historical data. Organizations create data value using analytics and business intelligence applications. Now that you have experienced the ELT process, gain hands-on analytics and business intelligence experience using IBM Cognos and its reporting, dashboard features including visualization capabilities. Finally, you will complete a shareable final project that enables you to demonstrate the skills you acquired in each module.
