Practical Pandas With SQL: From Database to DataFrame

Partner: Udemy
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Description: This hands-on course bridges the critical gap between SQL, Pandas and python—the three pillars of modern data work.The course is designed for data analysts, developers, and aspiring data scientists who want to develop confident fluency across the data analytics stack.By the end, you’ll walk away with the skills to:Set up and seed databases from scratchConnect Python to SQL with safe, reusable practicesUnderstand the power differences between SQL and Pandas—and when to use whichWrite advanced queries with CTEs, aggregations, and window functionsMaster performance tuning with indexes, query pushdown, and chunkingBuild secure, parameterized queries that protect against SQL injectionThis course is designed not just to show you the “how,” but also to explain the “why”—so every tool and technique you learn becomes part of a bigger framework for solving real-world data challenges.We start with the foundations and build layer by layer, until you can confidently handle tough data problems end-to-end.Virtual Environment & DependenciesBefore writing a single query, you’ll learn how to set up a clean virtual environment. This ensures your projects are portable, reproducible, and reliable—no more “it works on my machine” headaches. You’ll see how to manage dependencies properly, so that the same codebase can run smoothly on any system.Setting Up the DatabaseEvery serious data project needs a robust backend. You’ll provision a cloud-based Postgres instance in a few clicks, and then seed your database with data. Whether you’re on Mac (with libpq) or Windows (with the Postgres installer</st
Category: IT & Software > Other IT & Software > Pandas
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Price: 19.99
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Source: Impact
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