9781788996341-1788996348-Hands-On Data Science with SQL Server 2017: Perform end-to-end data analysis to gain efficient data insight

Hands-On Data Science with SQL Server 2017: Perform end-to-end data analysis to gain efficient data insight

ISBN-13: 9781788996341
ISBN-10: 1788996348
Author: Chmel, Marek, Muzny, Vladimir
Publication date: 2018
Publisher: Packt Publishing
Format: Paperback 506 pages
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Book details

ISBN-13: 9781788996341
ISBN-10: 1788996348
Author: Chmel, Marek, Muzny, Vladimir
Publication date: 2018
Publisher: Packt Publishing
Format: Paperback 506 pages

Summary

Acknowledged authors Chmel, Marek, Muzny, Vladimir wrote Hands-On Data Science with SQL Server 2017: Perform end-to-end data analysis to gain efficient data insight comprising 506 pages back in 2018. Textbook and eTextbook are published under ISBN 1788996348 and 9781788996341. Since then Hands-On Data Science with SQL Server 2017: Perform end-to-end data analysis to gain efficient data insight textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

Find, explore, and extract big data to transform into actionable insights

Key Features
  • Perform end-to-end data analysis―from exploration to visualization
  • Real-world examples, tasks, and interview queries to be a proficient data scientist
  • Understand how SQL is used for big data processing using HiveQL and SparkSQL
Book Description

SQL Server is a relational database management system that enables you to cover end-to-end data science processes using various inbuilt services and features.

Hands-On Data Science with SQL Server 2017 starts with an overview of data science with SQL to understand the core tasks in data science. You will learn intermediate-to-advanced level concepts to perform analytical tasks on data using SQL Server. The book has a unique approach, covering best practices, tasks, and challenges to test your abilities at the end of each chapter. You will explore the ins and outs of performing various key tasks such as data collection, cleaning, manipulation, aggregations, and filtering techniques. As you make your way through the chapters, you will turn raw data into actionable insights by wrangling and extracting data from databases using T-SQL. You will get to grips with preparing and presenting data in a meaningful way, using Power BI to reveal hidden patterns. In the concluding chapters, you will work with SQL Server integration services to transform data into a useful format and delve into advanced examples covering machine learning concepts such as predictive analytics using real-world examples.

By the end of this book, you will be in a position to handle the growing amounts of data and perform everyday activities that a data science professional performs.

What you will learn
  • Understand what data science is and how SQL Server is used for big data processing
  • Analyze incoming data with SQL queries and visualizations
  • Create, train, and evaluate predictive models
  • Make predictions using trained models and establish regular retraining courses
  • Incorporate data source querying into SQL Server
  • Enhance built-in T-SQL capabilities using SQLCLR
  • Visualize data with Reporting Services, Power View, and Power BI
  • Transform data with R, Python, and Azure
Who this book is for

Hands-On Data Science with SQL Server 2017 is intended for data scientists, data analysts, and big data professionals who want to master their skills learning SQL and its applications. This book will be helpful even for beginners who want to build their career as data science professionals using the power of SQL Server 2017. Basic familiarity with SQL language will aid with understanding the concepts covered in this book.

Table of Contents
  1. Data Science Overview
  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
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