There are numerous services that make up the whole SQL Server environment, which you can run either on-premise or in the cloud. The core of the SQL Server is a database management system, which stores the data and provides us with the data operation engine, but the data also has to come in the SQL Server and it needs to be analyzed and represented. Therefore, we have companion services, such as Integration Services, which provide us the ETL solution for importing and cleaning the data before storing it on the SQL Server, Analysis Services, which is used for multidimensional modeling and data mining, and Reporting Services, which is a visualization part of the whole stack. No matter whether you choose the on-premise approach or the cloud services, there are always a number of offerings from the SQL Server that you can utilize for data science.
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Book Overview & Buying
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Table Of Contents
Hands-On Data Science with SQL Server 2017
By :
Hands-On Data Science with SQL Server 2017
By:
Overview of this book
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.
Table of Contents (14 chapters)
Preface
Free Chapter
Data Science Overview
SQL Server 2017 as a Data Science Platform
Data Sources for Analytics
Data Transforming and Cleaning with T-SQL
Data Exploration and Statistics with T-SQL
Custom Aggregations on SQL Server
Data Visualization
Data Transformations with Other Tools
Predictive Model Training and Evaluation
Making Predictions
Getting It All Together - A Real-World Example
Next Steps with Data Science and SQL
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