BigQuery For Data Analysis
Fundamental 12 Steps 1 day 37 Credits
Want to learn the core SQL and visualization skills of a Data Analyst? Interested on how to write queries that scale to petabyte-size datasets? Take the BigQuery for Analyst Quest and learn how to query, ingest, optimize, visualize, and even build machine learning models in SQL inside of BigQuery.
PrerequisitesThis quest assumes basic knowledge of SQL (Structured Query Language) but does provide an optional first lab to review the basic query syntax. No other labs or quests are required as a prerequisite.
In this lab you will learn fundamental SQL clauses and will get hands on practice running structured queries on BigQuery and Cloud SQL.
In this lab, you learn to use BigQuery to find data, query the data-to-insights public dataset, and write and execute queries.
In this lab, you use BigQuery to troubleshoot common SQL errors, query the data-to-insights public dataset, use the Query Validator, and troubleshoot syntax and logical SQL errors.
In this lab, you learn how to connect Google Data Studio to Google BigQuery data tables, create charts, and explore the relationships between dimensions and measures.
This lab focuses on how to create new permanent reporting tables and logical reviews from an existing ecommerce dataset.
This lab focuses on how to ingest new datasets into tables inside of BigQuery.
This lab focuses on how to reverse-engineer the relationships between data tables and the pitfalls to avoid when joining them together.
This lab focuses on how to create new reporting tables using SQL JOINS and UNIONs.
This lab focuses on how to query partitioned datasets and how to create your own dataset partitions to improve query performance, which reduces cost.
In this lab you will work with semi-structured data (ingesting JSON, Array data types) inside of BigQuery. You will practice loading, querying, troubleshooting, and unnesting various semi-structured datasets.
In this lab you will use a newly available ecommerce dataset to run some typical queries that businesses would want to know about their customers’ purchasing habits.
In this lab you will explore millions of New York City yellow taxi cab trips available in a BigQuery Public Dataset, create a ML model inside of BigQuery to predict the fare, and evaluate the performance of your model to make predictions.