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    • Bigquery

    BigQuery Courses Online

    Learn Google BigQuery for scalable data analysis. Understand how to manage and analyze large datasets using SQL in the cloud.

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    Explore the BigQuery Course Catalog

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      G

      Google Cloud

      Feature Engineering - Italiano

      Skills you'll gain: Feature Engineering, Tensorflow, Data Processing, Data Pipelines, Keras (Neural Network Library), Data Transformation, Data Modeling, Deep Learning, Machine Learning

      Intermediate · Course · 1 - 3 Months

    • G

      Google Cloud

      Building Batch Data Pipelines on Google Cloud - 한국어

      Skills you'll gain: Data Pipelines, Data Transformation, Extract, Transform, Load, Data Processing, Google Cloud Platform, Data Migration, Data Integration, Serverless Computing, Big Data, Apache Spark, Apache Hadoop, Cloud Management, Cloud Storage, Performance Tuning

      Intermediate · Course · 1 - 3 Months

    • G

      Google Cloud

      Gemini for Data Scientists and Analysts - 日本語版

      Skills you'll gain: Marketing Analytics, Generative AI, Customer Analysis, Customer Insights, Big Data, Marketing Strategies, Advanced Analytics, Forecasting, Predictive Analytics, Data Analysis, Time Series Analysis and Forecasting, Artificial Intelligence and Machine Learning (AI/ML)

      Beginner · Course · 1 - 4 Weeks

    • G

      Google Cloud

      Smart Analytics, Machine Learning and AI on GC - 한국어

      Skills you'll gain: Google Cloud Platform, Unstructured Data, Tensorflow, MLOps (Machine Learning Operations), Data Pipelines, Big Data, Cloud API, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Jupyter

      Intermediate · Course · 1 - 3 Months

    • G

      Google Cloud

      Building Resilient Streaming Analytics Systems on GC - 한국어

      Intermediate · Course · 1 - 3 Months

    • Status: New
      New
      G

      Google Cloud

      Machine Learning in the Enterprise - Italiano

      Skills you'll gain: MLOps (Machine Learning Operations), Google Cloud Platform, Data Management, Data Governance, Business Workflow Analysis, Machine Learning, Data Pipelines, Predictive Modeling, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Feature Engineering

      Intermediate · Course · 1 - 3 Months

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    In summary, here are 6 of our most popular bigquery courses

    • Feature Engineering - Italiano: Google Cloud
    • Building Batch Data Pipelines on Google Cloud - 한국어: Google Cloud
    • Gemini for Data Scientists and Analysts - 日本語版: Google Cloud
    • Smart Analytics, Machine Learning and AI on GC - 한국어: Google Cloud
    • Building Resilient Streaming Analytics Systems on GC - 한국어: Google Cloud
    • Machine Learning in the Enterprise - Italiano: Google Cloud

    Frequently Asked Questions about Bigquery

    BigQuery is a serverless data warehouse that uses the Google Cloud platform. Data warehouses are critical components of data infrastructure required to collect and store data from a variety of sources for use within an organization, but building and maintaining warehouses at the scale necessary for today’s massive datasets can be expensive and time-consuming. By using cloud computing to enable rapidly-scalable analysis over petabytes of data, BigQuery is an important software as a service (SaaS) solution for companies looking to harness the power of big data flexibly and cost-effectively.

    Like other relational database management systems (RDBMS), BigQuery uses the Structured Query Language or SQL to enable users to quickly store, retrieve, manage, and manipulate data. The Google Cloud platform also provides access to a variety of tools from Google and its partners, including Cloud Dataprep to automate the creation of data cleansing pipelines as well as built-in machine learning capabilities to generate insights from large-scale datasets. There are also a wide range of third-party tools that can be used with BigQuery for data visualization and other tasks.‎

    A background in the use of BigQuery is an increasingly common requirement for a career as a data engineer. These specialized professionals in the creation and maintenance of data infrastructure are critical to the work of data science, which requires the delivery of massive datasets in a form that can be readily analyzed and utilized. And, like other cloud computing solutions, BigQuery is becoming popular with a growing number of tech companies due to its flexibility and cost-effectiveness compared to on premises data warehousing.

    Given the fast-growing interest in using big data across virtually every industry, it’s no surprise that data engineer jobs are in high demand - and highly paid. According to Glassdoor, data engineers in the U.S. earn an average base pay of $102,864 per year.‎

    Yes! In fact, Coursera lets you learn about BigQuery directly from Google itself. Google Cloud offers a variety of learning opportunities through the Coursera Platform, including individual courses in BigQuery specifically as well as Specializations that span multiple courses and topics including BigQuery skills. If you want to get a valuable career credential, you can even complete coursework for a Google Cloud Professional Data Engineer certification.

    Regardless of your needs, the ability to study and complete course materials on your own schedule means that learning on Coursera is just as flexible - and powerful - as cloud computing-based SaaS solutions like BigQuery.‎

    Before learning BigQuery, it's very advantageous to have experience writing queries using SQL. You should also have experience using databases. You may also want to be familiar with the Google Cloud Platform and have experience with cloud computing.‎

    Topics related to BigQuery that you can study include Google Sheets, Google Data Studio, data analytics, and the cloud. You might also be interested in learning about other big data tools, such as Redshift, DigitalOcean, S3, Apache Spark, Hadoop, and Hydra. Other technologies may also be of interest to you, such as TypeScript, auto-scaling clusters, Python, machine learning, and continuous deployment.‎

    Places that hire people with a background in BigQuery include companies that are using BigQuery to manage and analyze their data. According to Google, this includes UPS, Twitter, Major League Baseball (MLB), The Home Depot, Dow Jones, Toyota, American Cancer Society, Target, 20th Century Fox, and many more. Companies in retail, financial services, media and entertainment, health care and life sciences, and government are just a few industries that hire people with a background in BigQuery across the world.‎

    If you're a company, data scientist, data analyst, or individual looking for a platform that can analyze petabytes of data in the convenience of the cloud, learning BigQuery is a good fit for you. If your current data analytics processes run slower than you want or aren't giving you the data insights you need, learning BigQuery is worthwhile. BigQuery can help you manage and analyze marketing and sales data very quickly, for example. If that fits your needs, learning BigQuery may be right for you.‎

    Online BigQuery courses offer a convenient and flexible way to enhance your existing knowledge or learn new BigQuery skills. With a wide range of BigQuery classes, you can conveniently learn at your own pace to advance your BigQuery career skills.‎

    When looking to enhance your workforce's skills in BigQuery, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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