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BigQuery For Data Analysis

Fundamental 12 Steps 1 day 35 크레딧

Want to learn the core SQL and visualization skills of a Data Analyst? Interested in 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.

Data

Prerequisites:

This 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.

Quest Outline

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BigQuery 및 Cloud SQL용 SQL 소개

이 실습에서는 기본적인 SQL 절을 학습하고 BigQuery 및 Cloud SQL에서 구조화된 쿼리를 실행하는 연습을 진행합니다.

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Using BigQuery in the GCP Console

This lab shows you how to query public tables and load sample data into BigQuery using the GCP Console. Watch the following short video Get Meaningful Insights with Google BigQuery.

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BigQuery: Qwik Start - Command Line

This hands-on lab shows you how to query public tables and load sample data into BigQuery using the Command Line Interface. Watch the short videos Get Meaningful Insights with Google BigQuery and BigQuery: Qwik Start - Qwiklabs Preview.

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Exploring Your Ecommerce Dataset with SQL in Google BigQuery

In this lab, you learn to use BigQuery to find data, query the data-to-insights public dataset, and write and execute queries.

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Troubleshooting Common SQL Errors with BigQuery

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.

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Explore and Create Reports with Data Studio

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.

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Creating Permanent Tables and Access-Controlled Views in BigQuery

This lab focuses on how to create new permanent reporting tables and logical reviews from an existing ecommerce dataset.

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Ingesting New Datasets into BigQuery

This lab focuses on how to ingest new datasets into tables inside of BigQuery.

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Troubleshooting and Solving Data Join Pitfalls

This lab focuses on how to reverse-engineer the relationships between data tables and the pitfalls to avoid when joining them together.

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Creating a Data Warehouse Through Joins and Unions

This lab focuses on how to create new reporting tables using SQL JOINS and UNIONs.

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Creating Date-Partitioned Tables in BigQuery

This lab focuses on how to query partitioned datasets and how to create your own dataset partitions to improve query performance, which reduces cost.

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Working with JSON, Arrays, and Structs in BigQuery

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.

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Predict Visitor Purchases with a Classification Model in BQML

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.

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BigQuery ML 예측 모델을 사용하여 택시 요금 예측하기

이 실습에서는 BigQuery 공개 데이터세트에서 제공되는 수백만 건의 뉴욕 옐로캡 택시 운행 데이터를 살펴보고, BigQuery 내에서 ML 모델을 생성하여 요금을 예측하며, 이러한 예측을 수행하는 모델의 성능을 평가해봅니다.

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