SQL remains one of the most powerful tools in modern data analytics—and this book helps you use it not just to write queries, but to deliver insights.
SQL for Data Analytics, Fourth Edition takes you beyond basic SQL syntax and teaches you how to analyze real-world data with confidence. Whether you're a beginner aiming to understand production data or a professional seeking to upgrade your analytics toolkit, this book gives you the skills to turn data into actionable outcomes.

You'll begin by learning how to create and manage structured databases, before diving into data retrieval, transformation, and summarization. From there, you’ll tackle more complex tasks: window functions, statistical operations, and analysis of geospatial, time-series, and text data.
With hands-on exercises, case studies, and detailed guidance, this book prepares you to apply SQL in everyday business contexts—whether you're cleaning data, building dashboards, or presenting insights to stakeholders.

Zobacz także:

• Data Science w Pythonie. Kurs video. Przetwarzanie i analiza danych 149,00 zł, (67,05 zł -55%)
• Excel 2013. Kurs video. Poziom drugi. Przetwarzanie i analiza danych 79,00 zł, (35,55 zł -55%)
• Data science od podstaw. Analiza danych w Pythonie. Wydanie II 67,00 zł, (33,50 zł -50%)
• Analiza danych w środowisku R. Kurs video. Poziom pierwszy. Manipulacja, modelowanie i wizualizacja danych w praktyce 59,00 zł, (29,50 zł -50%)
• Zrozumieć BPMN. Modelowanie procesów biznesowych. Wydanie 2 rozszerzone 39,90 zł, (19,95 zł -50%)

Spis treści

SQL for Data Analytics. Analyze data effectively, uncover insights and master advanced SQL for real-world applications - Fourth Edition eBook -- spis treści

• 1. Introduction to Data Management Systems
• 2. Creating Table with Solid Structure
• 3. Exchange Data using COPY
• 4. Manipulating Data with Python
• 5. Presenting Data with SELECT
• 6. Transforming and Updating Data
• 7. Defining Datasets from Existing Datasets
• 8. Aggregating Data with GROUP BY
• 9. Inter-row Operation with Window Functions
• 10. Performant SQL
• 11. Processing JSON and Array
• 12. Advanced Data Types: Date, Geospatial, and Text
• 13. Inferential Statistics using SQL
• 14. A Case Study for Analytics using SQL