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Artikelnr.:
210326851
SQL for Data Analytics: Analyze data effectively, uncover insights and master advanced SQL for real-world applications
Artikelnr.:
210326851
ANG 94
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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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Wat opvalt
Comprehensive Coverage
This edition offers extensive coverage of SQL techniques tailored for data analytics, ensuring users can handle real-world datasets efficiently and effectively.
Hands-On Approach
Engage with practical exercises and real-world scenarios that enhance learning and application of advanced SQL skills for data-driven decision-making.
Expert Insights
Gain perspectives from industry experts that provide invaluable tips and strategies, making complex SQL concepts accessible and applicable to various analytical challenges.
Productdetails
- Level up from basic SQL to advanced, analytics-grade data analysis and use real PostgreSQL datasets, modern features, and practical business scenarios to turn raw data into clear, actionable insights. Key FeaturesSolve real business problems with advanced SQL techniquesWork with time-series, geospatial, and text data using PostgreSQLBuild job-ready data analysis skills with hands-on SQL projectsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionSQL remains one of the most essential tools for modern data analysis and mastering it can set you apart in a competitive data landscape. This book helps you go beyond basic query writing to develop a deep, practical understanding of how SQL powers real-world decision-making. SQL for Data Analytics, Fourth Edition, is for anyone who wants to go beyond basic SQL syntax and confidently analyze real-world data. Whether you're trying to make sense of production data for the first time or upgrading your analytics toolkit, this book gives you the skills to turn data into actionable outcomes. You'll start by creating and managing structured databases before advancing to data retrieval, transformation, and summarization. From there, you’ll take on more complex tasks such as window functions, statistical operations, and analyzing geospatial, time-series, and text data. With hands-on exercises, case studies, and detailed guidance throughout, this book prepares you to apply SQL in everyday business contexts, whether you're cleaning data, building dashboards, or presenting findings to stakeholders. By the end, you'll have a powerful SQL toolkit that translates directly to the work analysts do every day. What you will learnWrite SQL Queries to explore and analyze structured data.Use JOINs, subqueries, views, and CTEs to build analytics-ready datasetsApply window functions to identify trends, patterns, and cohort behaviorPerform statistical analysis and hypothesis testing directly in SQLAnalyze JSON, arrays, text, geospatial, and time-series dataImprove SQL performance with indexing strategies and query plan optimizationLoad data with Python and automate analytics workflowsComplete a full case study simulating a real-world data analysis projectWho this book is forThis book is for aspiring and early-career data analysts, data engineers, backend developers, business analysts, and students who want to apply SQL to real-world data analytics. You should have basic SQL familiarity and college-level math knowledge, along with the desire to advance toward analytics-grade SQL, data transformation, pattern discovery, and business insight generation. Table of ContentsIntroduction to Data Management SystemsCreating Tables with Solid StructuresExchanging Data Using COPYManipulating Data with PythonPresenting Data with SELECTTransforming and Updating DataDefining Datasets from Existing DatasetsAggregating Data with GROUP BYInter-Row Operation with Window FunctionsPerformant SQLProcessing JSON and ArraysAdvanced Data Types: Date, Text, and GeospatialInferential Statistics Using SQLA Case Study for Analytics Using SQL
| 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) |
Voor wie is dit geschikt?
-
Data Analysts
Ideal for analysts seeking to improve their SQL skills for deeper data insights and reporting.
-
Business Intelligence Experts
Perfect for BI professionals aiming to leverage SQL for effective data analysis in decision-making.
-
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.
PRODUCTBESCHRIJVING
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Vragen en antwoorden van klanten
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vraag:
Who is this book intended for?
antwoord: This book is for aspiring data analysts, data engineers, backend developers, business analysts, and students with basic SQL familiarity. -
vraag:
What will I learn from this book?
antwoord: You will learn advanced SQL techniques, data manipulation, statistical analysis, and how to create actionable insights from raw data. -
vraag:
Do I need prior SQL experience?
antwoord: Yes, basic SQL familiarity is recommended to get the most out of this book.
Data Mining Editorial Review
**** SQL for Data Analytics (Fourth Edition) is a highly-regarded resource for those looking to deepen their understanding of SQL and its role in data analysis. This book is lauded for its structured and coherent approach, leading readers from foundational concepts to advanced applications in a logical progression that mirrors professional usage. Beginning with basic SQL techniques such as data manipulation, filtering, and joins, the authors gradually introduce more complex topics, including performance tuning and data types like JSON, ensuring that readers not only learn the syntax but also how to apply it in real-world scenarios. One of the standout features of this edition is its practicality. It places Considerable emphasis on how SQL contributes to decision-making processes—helping analysts recognize patterns, diagnose issues, and extract valuable insights from complex datasets. The case studies presented at the end of the book effectively encapsulate these principles, demonstrating SQL's practical applications in real business environments. While the book is particularly strong for beginners, equipping them with a robust foundation, it also serves as an excellent resource for seasoned professionals. Its focus on practical exercises that follow each concept aids in reinforcing learning and supports self-study. Importantly, the inclusion of integration with Python tools like SQLAlchemy and pandas broadens the book's applicability in modern data workflows where SQL is often just one part of the larger analytical toolkit. However, the book does have its limitations. It leans heavily on PostgreSQL syntax, which can pose challenges for readers working in other database environments like Snowflake or SQL Server. Suggestions for comparison or translations between different SQL dialects could enhance the book’s relevance in diverse setups. Additionally, modern tools and concepts such as dbt or advanced data modeling could be integrated to prepare readers for future developments in the field. Overall, SQL for Data Analytics is a valuable addition to the library of both budding data analysts and experienced practitioners seeking to refine their skills. Its accessible style, combined with practical applications and a focus on the conceptual underpinnings of SQL, makes it a must-read for anyone serious about working with data. **
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- Clear explanations of SQL fundamentals and advanced concepts.
- Emphasizes SQL's role in decision-making and data insights.
- Practical exercises following each concept reinforce understanding.
- Strong coverage of performance tuning and data processing techniques.
- Integrates concepts with Python for modern data workflows.
Nadelen
- Heavily focused on PostgreSQL; less useful for users of other SQL dialects.
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Kenmerken en voordelen
- 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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