This is the third of five courses in the Google Cloud Data Analytics Certificate. In this course, you’ll begin by getting an overview of the data journey, from collection to insights. You’ll then learn how to use SQL to transform raw data into a usable format. Next, you’ll learn how to transform high volumes of data with a data pipeline. Finally, you’ll gain experience applying transformation strategies to real data sets to solve business needs.
Overview
Syllabus
- Introduction to data transformation in the cloud
- Introduction to course 3
- Course 3 overview
- Alyx: Data analysts help others learn from data
- Helpful resources and tips
- Lab technical tips
- Explore your Course 3 scenario: TheLook eCommerce
- Welcome to module 1
- Stages of the data journey
- The data journey is a flexible path
- Impacts of the data journey on decision making
- Steps for effective data collection
- Test your knowledge: Introduction to the data journey and collecting data
- The process stage ensures clean and consistent data
- [Supplemental] Process and summarize data
- Techniques used to transform data, part 1
- Techniques used to transform data, part 2
- Manual and automated transformation
- Storage options for transformed data
- Practice transformation methods
- Test your knowledge: The processing and storage stages of the data journey
- Wrap-up
- Glossary terms from module 1
- Module 1 challenge
- Handle raw data with data pipelines
- Welcome to module 2
- Data pipelines in the cloud
- Alyssa: Improve systems by collecting data and asking questions
- Overview of data pipeline stages and benefits
- The difference between ELT and ETL
- Test your knowledge: Introduction to data pipelines
- Data ingestion methods that suit your needs
- Strategies and tools for efficient data ingestion
- Data mapping helps ensure consistent data
- Test your knowledge: The extract stage of data pipelines
- Data transformation steps to ensure reliable data
- Introduction to profiling and cleaning data
- Common ways to manipulate data
- Data transformation techniques that achieve different business needs
- Transformation approaches for securing data
- Data anonymization with generative models
- Test your knowledge: The transformation stage of data pipelines
- Different approaches to loading data
- Overview of data validation strategies and rules
- Data dictionaries and monitoring help maintain data integrity
- Analysis and visualization after the data pipeline
- Creating and managing SQL pipelines
- Test your knowledge: The load stage of data pipelines
- Wrap-up
- Glossary terms from module 2
- Module 2 challenge
- Cloud data optimization strategies
- Welcome to module 3
- Benefits of data transformation plans
- Challenges of data transformation
- Steps for developing a data transformation plan
- Activity: Analyze a data transformation plan
- Activity Quiz: Analyze a data transformation plan
- Test your knowledge: Data transformation plans
- Data aggregation can make it easier to extract meaningful insights
- Nulls and aggregate data
- Consequences of duplicate data and how to eliminate it
- Test your knowledge: Data aggregation and deduplication
- Overview of how joins combine data from different tables
- SQL strategies to join data effectively
- Data derivation to combine data and obtain new insights
- Apply RFM method to segment customer data
- Test your knowledge: Joins and data derivation
- Wrap-up
- Lauren: What makes candidates stand out
- Lauren and Ela: Interview role play
- Interview tip: Explain impact
- Glossary terms from module 3
- Module 3 challenge
- Course wrap-up
- Course 3 resources and citations
- Glossary terms from Course 3
- Your Next Steps
- Course Badge
