SQL Server 2017 Machine Learning Services with R :: Data exploration, modeling, and advanced analytics.
With integrated R Services within SQL Server 2017, developers and data scientists can now benefit from the integrated, effective, efficient and more streamlined analytics environment. In this book, you will understand how to leverage the capabilities of R Services in SQL Server 2017. This short yet...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | With integrated R Services within SQL Server 2017, developers and data scientists can now benefit from the integrated, effective, efficient and more streamlined analytics environment. In this book, you will understand how to leverage the capabilities of R Services in SQL Server 2017. This short yet effective guide will help you get familiar ... |
| Beschreibung: | 1 online resource (331 pages) |
| ISBN: | 9781787280922 1787280926 1787283577 9781787283572 |
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| 505 | 0 | |a Cover; Copyright and Credits; www.PacktPub.com; Contributors; Table of Contents; Preface; Chapter 1: Introduction to R and SQL Server; Using R prior to SQL Server 2016; Microsoft's commitment to the open source R language; Boosting analytics with SQL Server R integration; Summary ; Chapter 2: Overview of Microsoft Machine Learning Server and SQL Server; Analytical barriers; The Microsoft Machine learning R Server platform; Microsoft R Open (MRO); Microsoft Machine Learning R Server; Microsoft SQL Server Machine Learning R Services; R Tools for Visual Studio (RTVS). | |
| 505 | 8 | |a The Microsoft Machine Learning R Services architectureR Limitations; Performance issues; Memory limitations; Security aspects; Language syntax; Summary; Chapter 3: Managing Machine Learning Services for SQL Server 2017 and R; Minimum requirements; Choosing the edition; Configuring the database; Configuring the environment and installing R Tools for Visual Studio (RTVS); Security; Resource Governor; Installing new R packages; Package information; Using R Tools for Visual Studio (RTVS) 2015 or higher; Using R.exe in CMD; Using XP_CMDSHELL; Copying files; Using the rxInstallPackages function. | |
| 505 | 8 | |a Managing SQL Server R Services with PowerShellGetting to know the sp_execute_external_script external procedure; Arguments; Summary; Chapter 4: Data Exploration and Data Visualization; Understanding SQL and R data types; Data frames in R; Data exploration and data munging; Importing SQL Server data into R; Exploring data in R; Data munging in R; Adding/removing rows/columns in data frames; More data munging with dplyr; Finding missing values; Transpose data; Pivot / Unpivot data; Example -- data exploration and munging using R in T-SQL; Data visualization in R; Plot; Histogram; Boxplot. | |
| 505 | 8 | |a Scatter plotTree diagram; Example â#x80;#x93; R data visualization in T-SQL; Integrating R code in reports and visualizations; Integrating R in SSRS reports; Integrating R in Power BI; Summary; Chapter 5: RevoScaleR Package; Overcomming R language limitations; Scalable and distributive computational environments; Functions for data preparation; Data import from SAS, SPSS, and ODBC; Importing SAS data; Importing SPSS data; Importing data using ODBC; Variable creation and data transformation; Variable creation and recoding; Dataset subsetting; Dataset merging; Functions for descriptive statistics. | |
| 505 | 8 | |a Functions for statistical tests and samplingSummary; Chapter 6: Predictive Modeling; Data modeling; Advanced predictive algorithms and analytics; Deploying and using predictive solutions; Performing predictions with R Services in the SQL Server database; Summary; Chapter 7: Operationalizing R Code; Integrating an existing R model; Prerequisite â#x80;#x93; prepare the data; Step 1 â#x80;#x93; Train and save a model using T-SQL; Step 2 â#x80;#x93; Operationalize the model; Fast batch prediction; Prerequisites; Real-time scoring; Native scoring; Integrating the R model for fast batch prediction. | |
| 505 | 8 | |a Step 1Â â#x80;#x93; Train and save a real-time scoring model using T-SQL. | |
| 520 | |a With integrated R Services within SQL Server 2017, developers and data scientists can now benefit from the integrated, effective, efficient and more streamlined analytics environment. In this book, you will understand how to leverage the capabilities of R Services in SQL Server 2017. This short yet effective guide will help you get familiar ... | ||
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| 776 | 0 | 8 | |i Print version: |a Koesmarno, Julie. |t SQL Server 2017 Machine Learning Services with R : Data exploration, modeling, and advanced analytics. |d Birmingham : Packt Publishing, ©2018 |
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| contents | Cover; Copyright and Credits; www.PacktPub.com; Contributors; Table of Contents; Preface; Chapter 1: Introduction to R and SQL Server; Using R prior to SQL Server 2016; Microsoft's commitment to the open source R language; Boosting analytics with SQL Server R integration; Summary ; Chapter 2: Overview of Microsoft Machine Learning Server and SQL Server; Analytical barriers; The Microsoft Machine learning R Server platform; Microsoft R Open (MRO); Microsoft Machine Learning R Server; Microsoft SQL Server Machine Learning R Services; R Tools for Visual Studio (RTVS). The Microsoft Machine Learning R Services architectureR Limitations; Performance issues; Memory limitations; Security aspects; Language syntax; Summary; Chapter 3: Managing Machine Learning Services for SQL Server 2017 and R; Minimum requirements; Choosing the edition; Configuring the database; Configuring the environment and installing R Tools for Visual Studio (RTVS); Security; Resource Governor; Installing new R packages; Package information; Using R Tools for Visual Studio (RTVS) 2015 or higher; Using R.exe in CMD; Using XP_CMDSHELL; Copying files; Using the rxInstallPackages function. Managing SQL Server R Services with PowerShellGetting to know the sp_execute_external_script external procedure; Arguments; Summary; Chapter 4: Data Exploration and Data Visualization; Understanding SQL and R data types; Data frames in R; Data exploration and data munging; Importing SQL Server data into R; Exploring data in R; Data munging in R; Adding/removing rows/columns in data frames; More data munging with dplyr; Finding missing values; Transpose data; Pivot / Unpivot data; Example -- data exploration and munging using R in T-SQL; Data visualization in R; Plot; Histogram; Boxplot. Scatter plotTree diagram; Example â#x80;#x93; R data visualization in T-SQL; Integrating R code in reports and visualizations; Integrating R in SSRS reports; Integrating R in Power BI; Summary; Chapter 5: RevoScaleR Package; Overcomming R language limitations; Scalable and distributive computational environments; Functions for data preparation; Data import from SAS, SPSS, and ODBC; Importing SAS data; Importing SPSS data; Importing data using ODBC; Variable creation and data transformation; Variable creation and recoding; Dataset subsetting; Dataset merging; Functions for descriptive statistics. Functions for statistical tests and samplingSummary; Chapter 6: Predictive Modeling; Data modeling; Advanced predictive algorithms and analytics; Deploying and using predictive solutions; Performing predictions with R Services in the SQL Server database; Summary; Chapter 7: Operationalizing R Code; Integrating an existing R model; Prerequisite â#x80;#x93; prepare the data; Step 1 â#x80;#x93; Train and save a model using T-SQL; Step 2 â#x80;#x93; Operationalize the model; Fast batch prediction; Prerequisites; Real-time scoring; Native scoring; Integrating the R model for fast batch prediction. Step 1 â#x80;#x93; Train and save a real-time scoring model using T-SQL. |
| ctrlnum | (OCoLC)1028224836 |
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| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-on1028224836 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:28:15Z |
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| isbn | 9781787280922 1787280926 1787283577 9781787283572 |
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| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Koesmarno, Julie. SQL Server 2017 Machine Learning Services with R : Data exploration, modeling, and advanced analytics. Birmingham : Packt Publishing, 2018. 1 online resource (331 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Copyright and Credits; www.PacktPub.com; Contributors; Table of Contents; Preface; Chapter 1: Introduction to R and SQL Server; Using R prior to SQL Server 2016; Microsoft's commitment to the open source R language; Boosting analytics with SQL Server R integration; Summary ; Chapter 2: Overview of Microsoft Machine Learning Server and SQL Server; Analytical barriers; The Microsoft Machine learning R Server platform; Microsoft R Open (MRO); Microsoft Machine Learning R Server; Microsoft SQL Server Machine Learning R Services; R Tools for Visual Studio (RTVS). The Microsoft Machine Learning R Services architectureR Limitations; Performance issues; Memory limitations; Security aspects; Language syntax; Summary; Chapter 3: Managing Machine Learning Services for SQL Server 2017 and R; Minimum requirements; Choosing the edition; Configuring the database; Configuring the environment and installing R Tools for Visual Studio (RTVS); Security; Resource Governor; Installing new R packages; Package information; Using R Tools for Visual Studio (RTVS) 2015 or higher; Using R.exe in CMD; Using XP_CMDSHELL; Copying files; Using the rxInstallPackages function. Managing SQL Server R Services with PowerShellGetting to know the sp_execute_external_script external procedure; Arguments; Summary; Chapter 4: Data Exploration and Data Visualization; Understanding SQL and R data types; Data frames in R; Data exploration and data munging; Importing SQL Server data into R; Exploring data in R; Data munging in R; Adding/removing rows/columns in data frames; More data munging with dplyr; Finding missing values; Transpose data; Pivot / Unpivot data; Example -- data exploration and munging using R in T-SQL; Data visualization in R; Plot; Histogram; Boxplot. Scatter plotTree diagram; Example â#x80;#x93; R data visualization in T-SQL; Integrating R code in reports and visualizations; Integrating R in SSRS reports; Integrating R in Power BI; Summary; Chapter 5: RevoScaleR Package; Overcomming R language limitations; Scalable and distributive computational environments; Functions for data preparation; Data import from SAS, SPSS, and ODBC; Importing SAS data; Importing SPSS data; Importing data using ODBC; Variable creation and data transformation; Variable creation and recoding; Dataset subsetting; Dataset merging; Functions for descriptive statistics. Functions for statistical tests and samplingSummary; Chapter 6: Predictive Modeling; Data modeling; Advanced predictive algorithms and analytics; Deploying and using predictive solutions; Performing predictions with R Services in the SQL Server database; Summary; Chapter 7: Operationalizing R Code; Integrating an existing R model; Prerequisite â#x80;#x93; prepare the data; Step 1 â#x80;#x93; Train and save a model using T-SQL; Step 2 â#x80;#x93; Operationalize the model; Fast batch prediction; Prerequisites; Real-time scoring; Native scoring; Integrating the R model for fast batch prediction. Step 1 â#x80;#x93; Train and save a real-time scoring model using T-SQL. With integrated R Services within SQL Server 2017, developers and data scientists can now benefit from the integrated, effective, efficient and more streamlined analytics environment. In this book, you will understand how to leverage the capabilities of R Services in SQL Server 2017. This short yet effective guide will help you get familiar ... SQL. Relational databases. http://id.loc.gov/authorities/subjects/sh86007768 Bases de données relationnelles. Servers. bicssc Databases. bicssc Database design & theory. bicssc Information architecture. bicssc Data capture & analysis. bicssc COMPUTERS Databases General. bisacsh Computers Data Modeling & Design. bisacsh Computers Data Processing. bisacsh Relational databases fast Kastrun, Tomaz. has work: SQL Server 2017 Machine Learning Services with R (Text) https://id.oclc.org/worldcat/entity/E39PCXHtT3tPp7YJQW6bQHBTf3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Koesmarno, Julie. SQL Server 2017 Machine Learning Services with R : Data exploration, modeling, and advanced analytics. Birmingham : Packt Publishing, ©2018 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1728028 Volltext |
| spellingShingle | Koesmarno, Julie SQL Server 2017 Machine Learning Services with R : Data exploration, modeling, and advanced analytics. Cover; Copyright and Credits; www.PacktPub.com; Contributors; Table of Contents; Preface; Chapter 1: Introduction to R and SQL Server; Using R prior to SQL Server 2016; Microsoft's commitment to the open source R language; Boosting analytics with SQL Server R integration; Summary ; Chapter 2: Overview of Microsoft Machine Learning Server and SQL Server; Analytical barriers; The Microsoft Machine learning R Server platform; Microsoft R Open (MRO); Microsoft Machine Learning R Server; Microsoft SQL Server Machine Learning R Services; R Tools for Visual Studio (RTVS). The Microsoft Machine Learning R Services architectureR Limitations; Performance issues; Memory limitations; Security aspects; Language syntax; Summary; Chapter 3: Managing Machine Learning Services for SQL Server 2017 and R; Minimum requirements; Choosing the edition; Configuring the database; Configuring the environment and installing R Tools for Visual Studio (RTVS); Security; Resource Governor; Installing new R packages; Package information; Using R Tools for Visual Studio (RTVS) 2015 or higher; Using R.exe in CMD; Using XP_CMDSHELL; Copying files; Using the rxInstallPackages function. Managing SQL Server R Services with PowerShellGetting to know the sp_execute_external_script external procedure; Arguments; Summary; Chapter 4: Data Exploration and Data Visualization; Understanding SQL and R data types; Data frames in R; Data exploration and data munging; Importing SQL Server data into R; Exploring data in R; Data munging in R; Adding/removing rows/columns in data frames; More data munging with dplyr; Finding missing values; Transpose data; Pivot / Unpivot data; Example -- data exploration and munging using R in T-SQL; Data visualization in R; Plot; Histogram; Boxplot. Scatter plotTree diagram; Example â#x80;#x93; R data visualization in T-SQL; Integrating R code in reports and visualizations; Integrating R in SSRS reports; Integrating R in Power BI; Summary; Chapter 5: RevoScaleR Package; Overcomming R language limitations; Scalable and distributive computational environments; Functions for data preparation; Data import from SAS, SPSS, and ODBC; Importing SAS data; Importing SPSS data; Importing data using ODBC; Variable creation and data transformation; Variable creation and recoding; Dataset subsetting; Dataset merging; Functions for descriptive statistics. Functions for statistical tests and samplingSummary; Chapter 6: Predictive Modeling; Data modeling; Advanced predictive algorithms and analytics; Deploying and using predictive solutions; Performing predictions with R Services in the SQL Server database; Summary; Chapter 7: Operationalizing R Code; Integrating an existing R model; Prerequisite â#x80;#x93; prepare the data; Step 1 â#x80;#x93; Train and save a model using T-SQL; Step 2 â#x80;#x93; Operationalize the model; Fast batch prediction; Prerequisites; Real-time scoring; Native scoring; Integrating the R model for fast batch prediction. Step 1 â#x80;#x93; Train and save a real-time scoring model using T-SQL. SQL. Relational databases. http://id.loc.gov/authorities/subjects/sh86007768 Bases de données relationnelles. Servers. bicssc Databases. bicssc Database design & theory. bicssc Information architecture. bicssc Data capture & analysis. bicssc COMPUTERS Databases General. bisacsh Computers Data Modeling & Design. bisacsh Computers Data Processing. bisacsh Relational databases fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh86007768 |
| title | SQL Server 2017 Machine Learning Services with R : Data exploration, modeling, and advanced analytics. |
| title_auth | SQL Server 2017 Machine Learning Services with R : Data exploration, modeling, and advanced analytics. |
| title_exact_search | SQL Server 2017 Machine Learning Services with R : Data exploration, modeling, and advanced analytics. |
| title_full | SQL Server 2017 Machine Learning Services with R : Data exploration, modeling, and advanced analytics. |
| title_fullStr | SQL Server 2017 Machine Learning Services with R : Data exploration, modeling, and advanced analytics. |
| title_full_unstemmed | SQL Server 2017 Machine Learning Services with R : Data exploration, modeling, and advanced analytics. |
| title_short | SQL Server 2017 Machine Learning Services with R : |
| title_sort | sql server 2017 machine learning services with r data exploration modeling and advanced analytics |
| title_sub | Data exploration, modeling, and advanced analytics. |
| topic | SQL. Relational databases. http://id.loc.gov/authorities/subjects/sh86007768 Bases de données relationnelles. Servers. bicssc Databases. bicssc Database design & theory. bicssc Information architecture. bicssc Data capture & analysis. bicssc COMPUTERS Databases General. bisacsh Computers Data Modeling & Design. bisacsh Computers Data Processing. bisacsh Relational databases fast |
| topic_facet | SQL. Relational databases. Bases de données relationnelles. Servers. Databases. Database design & theory. Information architecture. Data capture & analysis. COMPUTERS Databases General. Computers Data Modeling & Design. Computers Data Processing. Relational databases |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1728028 |
| work_keys_str_mv | AT koesmarnojulie sqlserver2017machinelearningserviceswithrdataexplorationmodelingandadvancedanalytics AT kastruntomaz sqlserver2017machinelearningserviceswithrdataexplorationmodelingandadvancedanalytics |