This course helps you build a strong foundation in analytics engineering and gives you the practical skills needed to work with modern data systems. You will begin by learning the core components of the modern data stack and the responsibilities of analytics engineers. From there, you will move into analytical SQL, dimensional modeling concepts, and the structure of ELT pipelines. The course concludes with hands-on development in dbt Core, where you will create, test, and document high-quality data models.
Introduction to Analytics Engineering
只需 199 美元（原价 399 美元）即可通过 Coursera Plus 学习更高水平的技能。立即节省
您将学到什么
Write analytical SQL queries to prepare, explore, and analyze data effectively.
Design facts, dimensions, and star schemas to structure data for accurate and efficient analysis.
Build organized raw, staging, and mart layers to support reliable and scalable data transformations.
Create, test, and document dbt models to automate transformations and ensure data quality and transparency.
您将获得的技能
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
该课程共有3个模块
This module introduces analytics engineering and the modern data stack. It explains ELT vs. ETL, essential analytical SQL skills, and core warehousing concepts. Learners work with PostgreSQL and dbt Docs to understand how modern data pipelines are structured.
涵盖的内容
13个视频6篇阅读材料4个作业3个讨论话题
This module covers dimensional modeling and how ELT pipelines are organized across raw, staging, and mart layers. It introduces dbt Core, its project structure, and how it streamlines SQL transformations in modern analytics environments.
涵盖的内容
15个视频4篇阅读材料4个作业2个讨论话题
This module explores building dbt models using sources, refs, and layered transformations. Learners practice using materializations and seeds, and implement testing and documentation to improve data quality and model transparency.
涵盖的内容
14个视频5篇阅读材料5个作业3个讨论话题
人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
自 2018开始学习的学生
''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'
Jennifer J.
自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
Larry W.
自 2021开始学习的学生
''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'
Chaitanya A.
''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'
常见问题
dbt, or data build tool, is a transformation framework used in analytics that applies software engineering practices such as version control, testing, and modular development. It enables analysts and engineers to use simple SQL SELECT statements to transform raw data inside a data warehouse, helping create faster, more reliable, and well-structured data pipelines.
dbt is primarily focused on the T in ELT, meaning it handles the transformation step inside the data warehouse. It allows data engineers and analysts to define tests and validation rules within dbt models, which helps ensure data quality during transformation. Using dbt, teams can verify completeness, accuracy, and consistency of data, making the overall ELT process more reliable and well-governed.
dbt is primarily SQL based, since its core purpose is to manage and run SQL transformations inside a data warehouse. It does not natively support non-SQL transformations. However, dbt is flexible enough to work alongside external tools, and teams can incorporate custom scripts when more advanced processing is required.
更多问题
提供助学金，
¹ 本课程的部分作业采用 AI 评分。对于这些作业，将根据 Coursera 隐私声明使用您的数据。