# Google Cloud Data Analytics Professional Certificate Course Syllabus — Curriculum & Modules | Course

> Detailed syllabus and module breakdown for Google Cloud Data Analytics Professional Certificate Course. See what you'll learn, estimated hours per module, prerequisites, and outcomes.

# Google Cloud Data Analytics Professional Certificate Course Syllabus

Full curriculum breakdown — modules, lessons, estimated time, and outcomes.

Overview: This professional certificate program by Google Cloud is designed to equip beginners with essential cloud-based data analytics skills. The course spans approximately 5-6 months at a pace of 5-7 hours per week, totaling around 150 hours. Learners will progress through hands-on modules covering core tools like BigQuery and Looker, build data pipelines, and complete a capstone project to showcase real-world proficiency in data processing, analysis, and visualization on Google Cloud.

### Module 1: Introduction to Data Analytics on Google Cloud

Estimated time: 20 hours

- Understand the fundamentals of data analytics in the cloud

- Explore the Google Cloud Platform (GCP) ecosystem

- Identify key roles in cloud data analytics

- Learn about data storage, querying, and visualization workflows

### Module 2: BigQuery for Data Analysts

Estimated time: 35 hours

- Write and optimize SQL queries in BigQuery

- Import and export data using Cloud Storage

- Analyze large datasets with real-world case studies

- Apply performance tuning and query best practices

### Module 3: Data Visualization with Looker

Estimated time: 35 hours

- Understand Looker’s interface and LookML basics

- Create interactive dashboards and visual reports

- Transform raw data into compelling visual insights

- Use storytelling techniques to communicate findings effectively

### Module 4: Building Batch Data Pipelines on Google Cloud

Estimated time: 50 hours

- Construct batch data pipelines using Cloud Storage and Dataproc

- Automate data transformations with Dataflow and SQL

- Develop scalable processing workflows using Python

- Explore architecture patterns for reliable data pipelines

### Module 5: Generative AI and Advanced Analytics

Estimated time: 20 hours

- Apply generative AI tools for data insights

- Automate reporting and analysis tasks using AI

- Enhance queries and visualizations with AI assistance

### Module 6: Final Project

Estimated time: 60 hours

- Complete an end-to-end data analytics project on Google Cloud

- Clean, process, and analyze a real-world business dataset

- Present findings using dashboards in Looker and summary reports

## Prerequisites

- Familiarity with basic SQL concepts

- Basic understanding of data handling and spreadsheets

- No prior cloud or programming experience required

## What You'll Be Able to Do After

- Query and analyze large datasets using BigQuery

- Build interactive dashboards with Looker for data storytelling

- Design and manage batch data pipelines on Google Cloud

- Apply generative AI to enhance data analysis workflows

- Demonstrate job-ready skills through a portfolio-ready capstone project

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