Barang #:
142553507
SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights
87% of respondents would recommend this to a friend
Barang #:
142553507
USD 81
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This practical book reveals new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow.
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What Stands Out
Advanced Techniques
Offers in-depth methods for analyzing complex datasets, enabling users to uncover actionable insights that drive informed decision-making.
Practical Applications
Includes real-world examples and use cases, making it easier for readers to apply concepts directly to their data challenges and enhance their analytical skills.
Expert Guidance
Written by industry professionals, this book provides credibility and authoritative advice, ensuring readers can trust the strategies presented for effective data transformation.
Rincian Produk
- With the explosion of data, computing power, and cloud data warehouses, SQL has become an even more indispensable tool for the savvy analyst or data scientist. This practical book reveals new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow. You'll learn how to use both common and exotic SQL functions such as joins, window functions, subqueries, and regular expressions in new, innovative ways--as well as how to combine SQL techniques to accomplish your goals faster, with understandable code. If you work with SQL databases, this is a must-have reference. Learn the key steps for preparing your data for analysis Perform time series analysis using SQL's date and time manipulations Use cohort analysis to investigate how groups change over time Use SQL's powerful functions and operators for text analysis Detect outliers in your data and replace them with alternate values Establish causality using experiment analysis, also known as A/B testing
| Publisher | O'Reilly Media |
| Publication date | 5 Oct. 2021 |
| Edition | 1st |
| Language | English |
| Print length | 357 pages |
| ISBN-10 | 1492088781 |
| ISBN-13 | 978-1492088783 |
| Dimensions | 17.15 x 1.91 x 22.23 cm |
Who Should Buy?
-
Data Analysts
Experienced data analysts will find advanced SQL techniques useful for enhancing data transformation and insights extraction.
-
Business Intelligence
Professionals in BI roles can leverage this book to deepen their understanding of data-driven decision-making through SQL.
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Graduate Students
Graduate students specializing in data science or analytics can benefit from advanced SQL concepts and practical applications.
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Beginners
Total beginners may struggle with complex SQL concepts without foundational knowledge and simpler resources.
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English edition Cathy Tanimura Format: Paperback Editorial Review
SQL For Data Analysis: Advanced Techniques For Transforming Data Into Insights, published by O'Reilly Media, provides a comprehensive guide on transforming complex datasets into actionable insights. This 357-page book, released on October 5, 2021, covers advanced SQL techniques essential for data analysts looking to enhance their querying skills. Readers appreciate the clear explanations and practical examples that help in understanding intricate SQL concepts. The community feedback emphasizes its utility for both novice and experienced analysts aiming for a deeper grasp of data manipulation and analysis, making it a valuable resource in any data professional's library.
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- Comprehensive guide on advanced SQL techniques
- Clear explanations and practical examples
- Useful for both novice and experienced analysts
- Valuable resource for data manipulation
- Well-structured content for easy understanding
Kontra
- Requires some prior knowledge of SQL to maximize benefits
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USD 81
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Fitur & Manfaat
- Learn how to use both common and exotic SQL functions
- Combine SQL techniques to accomplish your goals faster
- Perform time series analysis using SQL's date and time manipulations
- Use cohort analysis to investigate how groups change over time
- Detect outliers in your data and replace them with alternate values
- Establish causality using experiment analysis, also known as A/B testing
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