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This module introduces the course agenda.

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In the first module, we look at analytics challenges faced by data analysts and compare big data on-premises versus on the Cloud. We then introduce BigQuery, which is Google Cloud’s enterprise data warehouse, and review its features that make BigQuery a great option for your data analytics needs. And finally, we’ll learn from real-world use cases of companies transformed through analytics on the Cloud.

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This module is all about exploring your data with SQL, or structured query language. We go from very simple select statements to more complex queries that explore various datasets.

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This module is where all that hard work around ingesting, cleaning, preparing, and transforming your data comes to fruition as you get to visualize insights from your data by building insightful dashboards and reports. We start off with a little visualization theory and some best practices, and then look at tools, like Looker Studio and Connected Sheets, that can connect to BigQuery and help create impactful visualizations to capture and convey your insights. Although SQL is a powerful query language, programming languages such as Python, Java, or R provide syntaxes and a large array of built-in statistical functions that data analysts might find more expressive and easier to manipulate for certain types of data analysis. Such tools include open source web-based applications like Jupyter Notebooks, and so we discuss these as well.

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Creating, maintaining, and versioning SQL pipelines is a lot of hard work. And many times, data analysts have to use multiple tools to achieve this. So in this module, we introduce Dataform, a new product that offers a unified end-to-end experience to develop, version control, and orchestrate SQL pipelines in BigQuery.

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This module recaps the key topics covered in the course.

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Google Cloud Skills Boost

BigQuery for Data Analysts