Learn BigQuery
16 expert-rated courses covering BigQuery. Compared by rating, price, difficulty, and job relevance so you can pick the right one.
The SkillsetCourse catalog indicates a comprehensive approach to learning BigQuery, with all 16 courses available on platforms like Coursera and Udemy. Each course awards a certificate upon completion, enhancing professional credentials. Related skills such as SQL and data analysis complement BigQuery training, ensuring learners gain a well-rounded understanding of data management and analytics.
Catalog analysis updated . Ratings are independent editorial scores. Read the rating methodology.
Key Facts About BigQuery
- 1BigQuery allows for real-time analytics on massive datasets, making it ideal for data-driven decision-making.
- 2Courses on BigQuery cover essential skills in data analysis, machine learning, and data pipelines.
- 3All courses in the SkillsetCourse catalog award certificates, enhancing career prospects.
- 4BigQuery integrates seamlessly with other Google Cloud services, providing a robust analytics ecosystem.
- 5Learning BigQuery can significantly improve data processing efficiency and analytical capabilities.
Top BigQuery Courses
Data Analytics and Visualization
This learning path provides a comprehensive introduction to the data lifecycle, focusing on how to derive actionable insights using Google Cloud’s powerful analytics tools. Learners will progress from foundational cloud concepts to advanced data transformation with BigQuery and professional dashboarding in Looker Studio. By the end of this path, you will be able to ingest, clean, and visualize complex datasets to support data-driven decision-making.
Gemini in BigQuery
This learning path provides a journey into leveraging Gemini within BigQuery for advanced data and AI workflows. Starting with foundational productivity enhancements, it progresses to building generative AI applications and culminates in mastering Retrieval Augmented Generation to mitigate AI inaccuracies. By completing this path, learners will gain practical skills in utilizing Gemini to streamline data processes, create innovative AI solutions, and ensure reliable AI outputs within the BigQuery environment.
Hands-on Foundations for Data Science and Machine Learning with Google Cloud Labs
In this Google Cloud Labs Specialization, you'll receive hands-on experience building and practicing skills in BigQuery and Cloud Data Fusion. You will start learning the basics of BigQuery, building and optimizing warehouses, and then get hands-on practice on the more advanced data integration features available in Cloud Data Fusion. Learning will take place leveraging Google Cloud's Qwiklab platform where you will have the virtual environment and resources need to complete each lab. This specialization is broken up into 4 courses comprised of a series of courses: BigQuery Basics for Data Analysts Build and Optimize Data Warehouses with BigQuery Building Advanced Codeless Pipelines on Cloud Data Fusion Data Science on Google Cloud: Machine Learning You will even be able to earn a Skills Badge in one of these lab-based courses.
BigQuery Fundamentals for Redshift Professionals
This course covers BigQuery fundamentals for professionals who are familiar with SQL-based cloud data warehouses in Redshift and want to begin working in BigQuery. Through interactive lecture content and hands-on labs, you learn how to provision resources, create and share data assets, ingest data, and optimize query performance in BigQuery. Drawing upon your knowledge of Redshift, you also learn about similarities and differences between Redshift and BigQuery to help you get started with data warehouses in BigQuery.
Consuming Customer Specific Datasets from Data Sharing Partners using BigQuery
This is a self-paced lab that takes place in the Google Cloud console. In this lab you will learn how to create Data Twins for customers on Google Cloud or a different cloud service provider.
Create Embeddings, Vector Search, and RAG with BigQuery
This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.
Develop Code with Gemini in BigQuery
This is a self-paced lab that takes place in the Google Cloud console. In this lab you learn how to use Gemini code generation, explanation and suggestions in BigQuery.
Analyze BigQuery Usage with Log Analytics
This is a self-paced lab that takes place in the Google Cloud console. Use Log Analytics for in-depth usage logs analysis through SQL querires from the Cloud Logging page.
Analyze Customer Reviews with Gemini Using Python Notebooks
This is a self-paced lab that takes place in the Google Cloud console. Learn how to use BigQuery Machine Learning with remote models (Gemini AI) to analyze customer reviews using Python Notebooks.
Analyzing Billing Data with BigQuery
This is a self-paced lab that takes place in the Google Cloud console. In this lab you will create datasets and tables, import data from billing reports, and conduct a variety of queries on the data using BigQuery.
BigQuery Soccer Data Analysis
This is a self-paced lab that takes place in the Google Cloud console. Learn the fundamentals of writing and executing queries to query soccer data stored in BigQuery tables. In this lab you will learn more fundamentals of sports data science by writing and executing queries to query data stored in BigQuery tables. The emphasis of the lab is to illustrate how the database works and answer some interesting questions related to the following topics in soccer.
BigQuery Soccer Data Analytical Insight
This is a self-paced lab that takes place in the Google Cloud console. Learn how to create deeper analytical insights from soccer event data using BigQuery. BigQuery can be used to perform more sophisticated data analysis. In this lab, you will analyze soccer event data to achieve real insight from the dataset.
Analyzing and Visualizing Data the Google Way
This learning experience guides you through the process of utilizing various data sources and multiple Google Cloud products (including BigQuery and Google Sheets using Connected Sheets) to analyze, visualize, and interpret data to answer specific questions and share insights with key decision makers.
Data Engineering, Big Data, and ML on GCP
Master data engineering on Google Cloud including Spark, Kafka, data pipelines, data warehousing, and machine learning deployment.
Master Data Engineering using GCP Data Analytics
Learn GCS for Data Lake, BigQuery for Data Warehouse, GCP Dataproc and Databricks for Big Data Pipelines.
Data Engineering, Big Data, and Machine Learning on GCP
This five-week, accelerated online specialization provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data. This course teaches the following skills: • Design and build data processing systems on Google Cloud Platform • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow • Derive business insights from extremely large datasets using Google BigQuery • Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML • Enable instant insights from streaming data This class is intended for developers who are responsible for: • Extracting, Loading, Transforming, cleaning, and validating data • Designing pipelines and architectures for data processing • Creating and maintaining machine learning and statistical models • Querying datasets, visualizing query results and creating reports >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
Pro Tips for Learning BigQuery
- #1Start with the 'Data Analytics and Visualization' course to build foundational skills in BigQuery.
- #2Practice using BigQuery's SQL capabilities to enhance your data querying skills effectively.
- #3Utilize Google Cloud's free resources to supplement your learning and gain hands-on experience.
- #4Engage with community forums and discussions to deepen your understanding of BigQuery applications.
Why Learn BigQuery?
- Learning BigQuery can enhance your data analysis skills, making you more competitive in the job market.
- BigQuery expertise is valuable for roles in data engineering and machine learning, which are in high demand.
- Proficiency in BigQuery can lead to better insights and data-driven decisions within organizations.
- Understanding BigQuery can facilitate career advancement in tech fields focused on data science and analytics.