- Which external languages are supported by SQL Server?
Python and R. - Which service must be running for the successful execution of external code?
SQL Server Launchpad. - Can we combine filestreams with temporal tables?
No, temporal tables do not support the versioning of filestream data. - We are calling the following code: exec sp_execute_external_script @language = 'R', @script = '';. The script does not work and returns an error. Why?
Every parameter of the procedure is of the nvarchar data type. All parameter values must start with a leading capital N: N'text in the parameter'. - We are calling the sp_execute_external_script stored procedure. Inside the R script, we have the following line: Outputdataset <- as.data.frame(1+1);. The code does not return a result. Why?
This is because the R language is case-sensitive. The correct casing is...
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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