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SQL for Data Analytics: Analyze data effectively, uncover insights and master advanced SQL for real-world applications 4th ed. Edition
NIO 1952
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This book prepares you to apply SQL in everyday business contexts, whether you're cleaning data, building dashboards, or presenting findings to stakeholders.
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What Stands Out
Product Details
| Publisher | Packt Publishing |
| Publication date | November 21, 2025 |
| Edition | 4th ed. |
| Language | English |
| Print length | 336 pages |
| ISBN-10 | 1836646259 |
| ISBN-13 | 978-1836646259 |
| Item Weight | 1.27 pounds (580 grams) |
| Dimensions | 7.5 x 0.76 x 9.25 inches (19.1 x 1.9 x 23.5 cm) |
Who Should Buy?
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Data Analysts
Ideal for analysts seeking to improve their SQL skills for deeper data insights and reporting.
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Business Intelligence Experts
Perfect for BI professionals aiming to leverage SQL for effective data analysis in decision-making.
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Technical Students
Beneficial for students pursuing data science or analytics courses needing advanced SQL knowledge.
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Beginners
Not suitable for those with no prior SQL knowledge; the content may be too advanced.
Product Description
SQL for Data Analytics: Analyze data effectively, uncover insights and master advanced SQL for real-world applications 4th ed. Edition
Customer Questions & Answers
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Question:
Who is this book intended for?
Answer: This book is for aspiring data analysts, data engineers, backend developers, business analysts, and students with basic SQL familiarity. -
Question:
What will I learn from this book?
Answer: You will learn advanced SQL techniques, data manipulation, statistical analysis, and how to create actionable insights from raw data. -
Question:
Do I need prior SQL experience?
Answer: Yes, basic SQL familiarity is recommended to get the most out of this book.
Data Mining Editorial Review
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NIO 1952
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Features & Benefits
- Level up from basic SQL to advanced data analysis skills.
- Utilize real PostgreSQL datasets and modern features.
- Hands-on projects to build job-ready data analysis capabilities.
- Learn to analyze structured, geospatial, and time-series data.
- Gain practical experience with case studies and exercises.
- Perfect for aspiring data analysts and early-career professionals.
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