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SQL for data analysis : advanced techniques for transforming data into insights /
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 workflo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
2021.
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Tanimura, Cathy, |e author. | |
| 245 | 1 | 0 | |a SQL for data analysis : |b advanced techniques for transforming data into insights / |c Cathy Tanimura. |
| 250 | |a First edition. | ||
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| 500 | |a Includes index. | ||
| 520 | |a 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. | ||
| 505 | 0 | |a 1. Analysis with SQL -- 2. Preparing data for analysis -- 3. Time series analysis -- 4. Cohort analysis -- 5. Text analysis -- 6. Anomaly detection -- 7. Experiment analysis -- 8. Creating complex data sets for analysis -- 9. Conclusion. | |
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