Course
Data Science
Continuing Education

The Path to Insights: Data Models and Pipelines

24 Hours

Estimated learning time

Self-Paced

Progress at your own speed

Popular course

A popular course among students

About the Course

Description

This is the second of three courses in the Google Business Intelligence Certificate. In this course, you'll explore data modeling and how databases are designed. Then you’ll learn about extract, transform, load (ETL) processes that extract data from source systems, transform it into formats that enable analysis, and drive business processes and goals.

Google employees who currently work in BI will guide you through this course by providing hands-on activities that simulate job tasks, sharing examples from their day-to-day work, and helping you build business intelligence skills to prepare for a career in the field.

Learners who complete the three courses in this certificate program will have the skills needed to apply for business intelligence jobs. This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.

By the end of this course, you will:
-Determine which data models are appropriate for different business requirements
-Describe the difference between creating and interacting with a data model
-Create data models to address different types of questions
-Explain the parts of the extract, transform, load (ETL) process and tools used in ETL
-Understand extraction processes and tools for different data storage systems
-Design an ETL process that meets organizational and stakeholder needs
-Design data pipelines to automate BI processes

This Course is part of a program

You can only buy it along with program.

Sections

Schedule

Asynchronous

Delivery method

Online

Deliverables

  • 0 Credits

    Academic Excellence

    Earn necessary number of credit hours for completing this content

  • Hone Important Skills

    Total Upgrade

    Such as Modeling, Extract, Transform, Load, Data Model, Data Management