Google BigQuery for Data Analysts

Learn Google BigQuery from the ground up: SQL fundamentals, joins and nested data, window functions, performance and cost optimization, advanced SQL, geospatial analysis, BigQuery ML, and two full portfolio projects with worked solutions.

Rutvik Acharya

Principal Data Scientist

Atlassian

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What You'll Learn

Write efficient, cost-aware SQL in BigQuery's dialect, including joins, window functions, and nested/repeated data

Optimize query performance and cost using partitioning, clustering, and execution-plan analysis

Apply advanced techniques like PIVOT, ROLLUP, recursive queries, geospatial analysis, and BigQuery ML

Build and present a real analytics portfolio using both a business dataset and a public production-scale dataset

Who Should Attend

Data analysts who know SQL and want to work confidently in BigQuery

Excel or spreadsheet-based analysts ready to move to a cloud data warehouse

Aspiring data analysts building a portfolio for job applications

Engineers or PMs who need to query and interpret BigQuery data independently

CERTIFICATION

Certificate of Completion

11 Modules

11 Hour 45 Minutes

49 Lessons

49 Challenges

Language: English

FAQ

FREQUENTLY ASKED QUESTIONS

No prior BigQuery experience is required, but the course does assume you already know SQL. If you're comfortable writing SELECT statements, joins, and basic aggregations in any SQL dialect, you're ready to start from Module 1.

You'll be able to write efficient, cost-aware SQL in BigQuery, work with joins and nested data, use window functions for cohort and retention analysis, optimize query performance and cost, and apply advanced techniques like geospatial analysis and BigQuery ML. You'll also have two complete portfolio projects to show for it.

The core 11 modules take roughly 11 hours to work through, including the practice exercises in every lesson. The two portfolio projects add additional hands-on time depending on how deeply you work through the worked solutions yourself first.

You'll need a Google Cloud account to follow along with the hands-on exercises. Google Cloud offers a free tier and trial credits for new accounts that are sufficient for learning at the scale used in this course, and the course itself includes a full lesson on understanding and controlling BigQuery costs.

Most of the course uses a consistent fictional retail dataset called retail_analytics, covering customers, products, orders, and web events, so techniques build on each other instead of resetting with a new example every lesson. The final portfolio project also uses a real public BigQuery dataset of over 200 million Chicago taxi trips to demonstrate working at production scale.

Yes. Module 9 covers BigQuery ML, including linear regression, logistic regression for churn prediction, model evaluation, and time-series forecasting with ARIMA_PLUS, all written entirely in SQL with no separate ML platform or programming language required.

No. While it's built around the data analyst role, it's equally useful for engineers, product managers, or anyone who needs to query and interpret BigQuery data independently as part of their job, or for spreadsheet-based analysts looking to move to a cloud data warehouse.

The course ends with two full end-to-end projects, each with a business problem, dataset, specific questions, and complete worked SQL solutions. One analyzes retail performance using the course's retail dataset, and the other is a real public-data investigation using Chicago taxi trip data. Both are designed to be strong, concrete portfolio pieces for job applications.

The documentation explains what each feature does; this course teaches how an analyst actually uses those features together to answer real business questions, with consistent example data, worked practice exercises, and two full projects that combine techniques from across the entire course rather than covering each feature in isolation.

The course teaches GoogleSQL, BigQuery's specific SQL dialect, including its particular syntax, functions, and behaviors that differ from other databases like Postgres or MySQL. If you already know general SQL, you'll find the fundamentals familiar, with the course focusing on what's genuinely different and BigQuery-specific.

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