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Hands-On Data Science with SQL Server 2017

You're reading from   Hands-On Data Science with SQL Server 2017 Perform end-to-end data analysis to gain efficient data insight

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788996341
Length 506 pages
Edition 1st Edition
Languages
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Authors (2):
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Vladimír Mužný Vladimír Mužný
Author Profile Icon Vladimír Mužný
Vladimír Mužný
Marek Chmel Marek Chmel
Author Profile Icon Marek Chmel
Marek Chmel
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Toc

Table of Contents (14) Chapters Close

Preface 1. Data Science Overview FREE CHAPTER 2. SQL Server 2017 as a Data Science Platform 3. Data Sources for Analytics 4. Data Transforming and Cleaning with T-SQL 5. Data Exploration and Statistics with T-SQL 6. Custom Aggregations on SQL Server 7. Data Visualization 8. Data Transformations with Other Tools 9. Predictive Model Training and Evaluation 10. Making Predictions 11. Getting It All Together - A Real-World Example 12. Next Steps with Data Science and SQL 13. Other Books You May Enjoy

Questions

  1. Why do we need to transform data?
    We need to make data consistent and prepared for our analytical purposes. Data can come from a range of data sources with very volatile quality.
  2. Is it always good approach to consume source data directly?
    Sometimes, yes, but not always. One of the reasons why we should stage data aside is to avoid conflicts with common data contention coming to a source data.
  3. Why is the OPENQUERY function preferred when writing distributed queries?
    The first reason is that security when using the OPENROWSET function is compromised. The second reason is to do with performance. Distributed queries written using the OPENQUERY function often perform better than those written using an ad hoc approach.
  4. Can we modify data using views?
    No. The view definition is defined by the SELECT statement only. The modular object intended mainly for data manipulation...
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