舉報 
會員
Hands-On Data Science with SQL Server 2017
Marek Chmel Vladimír Mu?n? 著
更新時間：2021-06-10 19:14:46
開會員，本書免費讀 >
SQLServerisarelationaldatabasemanagementsystemthatenablesyoutocoverend-to-enddatascienceprocessesusingvariousinbuiltservicesandfeatures.Hands-OnDataSciencewithSQLServer2017startswithanoverviewofdatasciencewithSQLtounderstandthecoretasksindatascience.Youwilllearnintermediate-to-advancedlevelconceptstoperformanalyticaltasksondatausingSQLServer.Thebookhasauniqueapproach,coveringbestpractices,tasks,andchallengestotestyourabilitiesattheendofeachchapter.Youwillexploretheinsandoutsofperformingvariouskeytaskssuchasdatacollection,cleaning,manipulation,aggregations,andfilteringtechniques.Asyoumakeyourwaythroughthechapters,youwillturnrawdataintoactionableinsightsbywranglingandextractingdatafromdatabasesusingT-SQL.Youwillgettogripswithpreparingandpresentingdatainameaningfulway,usingPowerBItorevealhiddenpatterns.Intheconcludingchapters,youwillworkwithSQLServerintegrationservicestotransformdataintoausefulformatanddelveintoadvancedexamplescoveringmachinelearningconceptssuchaspredictiveanalyticsusingreal-worldexamples.Bytheendofthisbook,youwillbeinapositiontohandlethegrowingamountsofdataandperformeverydayactivitiesthatadatascienceprofessionalperforms.
最新章節
- Leave a review - let other readers know what you think
- Other Books You May Enjoy
- Summary
- Data science in the cloud
- Machine learning high availability
- Machine learning services on Linux
品牌：中圖公司
上架時間：2021-06-10 18:23:59
出版社：Packt Publishing
本書數字版權由中圖公司提供，并由其授權上海閱文信息技術有限公司制作發行
- Leave a review - let other readers know what you think 更新時間：2021-06-10 19:14:46
- Other Books You May Enjoy
- Summary
- Data science in the cloud
- Machine learning high availability
- Machine learning services on Linux
- Machine learning
- Big data clusters
- Next steps with SQL Server
- Data science next steps
- Next Steps with Data Science and SQL
- Questions
- Summary
- Using the model in predictions
- Using the rxLinMod function and finishing the model
- Training the model
- Preparing the schema for the model
- Training and using predictive models for estimations
- Data transformation
- Exploring data using R
- Exploring the SourceData.Actions table
- Exploring data using the SSIS Data Profiling Task
- Exploring data using T-SQL
- Data exploration
- Data description
- SQL Server
- Assignment and preparation
- Technical requirements
- Getting It All Together - A Real-World Example
- Questions
- Summary
- Testing the asynchronous solution
- Consuming requests and sending responses
- Sending a request to train a new model
- Creating queues and services
- Creating a contract
- Creating a message type
- Re-calculating a predictive model asynchronously
- Re-calculating a predictive model regularly
- Making the predictive model self-training
- Using the PREDICT keyword
- Making the prediction
- Deserializing a predictive model
- Submitting values into the external script
- Submitting values to an external script
- Reading the model from a temporal table
- Reading the model from a common table
- Reading models from a database
- Technical requirements
- Making Predictions
- Questions
- Summary
- Saving a machine learning model to temporal tables
- Saving our machine learning model to filestreams
- Deploying training and evaluating a predictive model
- Creating objects using temporal tables
- Creating objects using filestreams
- Creating common objects
- Creating physical data structures
- The concept of machine learning in databases
- Creating data structures
- Preparing to install our own R packages
- Setting up and configuring ML services
- Preparing SQL Server
- Technical requirements
- Predictive Model Training and Evaluation
- Questions
- Summary
- Creating simple copy data with ADF
- Creating Azure Data Factory
- Using Data Factory for data transformation
- Working example of Z-score computed in R
- R Syntax first steps
- Preparing client R environment
- Using R for data transformation
- Categorizing the products
- Setting up a SSIS project
- Using Integration Services for data transformation
- Feature-scaling
- Z-score
- Normalization
- Missing values
- Categorization
- Categorization missing values and normalization
- Technical requirements
- Data Transformations with Other Tools
- Summary
- Using SQL Server Data Tools
- Adding charts to Reports
- SQL Server Reporting Services
- Publishing reports
- Visual interactions
- Adding visualizations to the Report
- Defining the data source
- Starting with Power BI Desktop
- Power BI Report Server
- Data visualization – preparation phase
- Technical requirements
- Data Visualization
- Questions
- Summary
- Performance issues and risks
- Maintenance issues and risks
- Development issues and risks
- Limitations and performance considerations
- Deployment and testing
- Implementing the terminate method
- Implementing the Merge method
- Implementing the Accumulate method
- Implementing the Init method
- Implementing custom serialization
- Implementing methods
- Skeleton of CLR aggregation
- Example goal and assignment
- Creating CLR aggregations
- Instance and database configurations to use with SQLCLR
- How to work with SQLCLR
- Use cases of using SQLCLR
- Overview of SQLCLR
- Technical requirements
- Custom Aggregations on SQL Server
- Questions
- Summary
- The PERCENTILE_CONT and PERCENTILE_DISC functions
- The PERCENT_RANK and CUME_DIST functions
- Calculating with percentiles
- Using the LEAD and LAG functions
- Using aggregate functions
- Using aggregate functions in running aggregates
- Running aggregates
- NTILE
- DENSE_RANK
- RANK
- ROW_NUMBER
- Ranking functions
- Ranking framing and windowing
- Using the HAVING clause
- Using groups
- STDEV and STDEVP
- VAR and VARP
- AVG
- SUM
- MIN and MAX
- COUNT COUNT(*) and COUNT_BIG
- Aggregate functions
- Common properties of aggregate functions
- T-SQL aggregate queries
- Technical requirements
- Data Exploration and Statistics with T-SQL
- Questions
- Summary
- COLUMNSTORE INDEX
- B-tree indexes
- Using indexes
- Writing correct code
- Performance considerations
- Using stored procedures
- Using views
- Database applications
- Using views and stored procedures
- Denormalization using joins
- Computed columns
- Ways of denormalization
- Need for denormalization
- Third normal form
- Second normal form
- First normal form
- Relational normalization
- Denormalizing data
- Temporal tables
- CHECKSUM
- The MERGE statement
- Incremental data load
- Full data load
- Transforming data
- Is there an alternative to SSIS?
- Where should SSIS be used?
- What is needed to develop an SSIS solution?
- Why should we use SSIS?
- SQL Server Integration Services
- Distributed queries
- Tools eligible for data movement
- Landing–staging–target scenario
- Staging–target scenario
- Direct source for data analysis
- Database architectures for data transformations
- The need for data transformation
- Technical requirements
- Data Transforming and Cleaning with T-SQL
- Summary
- Installing and configuring
- External data with PolyBase
- Processing stored JSON data
- Retrieve data as JSON
- Working with JSON
- Working with XML data
- Importing flat files
- Importing data from other database systems
- Importing data from SQL Server
- Getting data from databases
- Technical requirements
- Data Sources for Analytics
- Summary
- Machine Learning Services
- Power BI Report Server
- Development tools for Reporting Services
- Reporting Services
- Querying languages
- PowerPivot Mode
- Multidimensional mode
- Tabular Mode
- SQL Server Analysis Services
- SQL Server Integration Services
- SQL Server Services and their use with data science
- Azure SQL Data Warehouse
- Azure SQL Database
- SQL Server in the cloud
- History of SQL Server
- What's available in the pack?
- SQL Server evolution
- Technical requirements
- SQL Server 2017 as a Data Science Platform
- Summary
- SQL Server and big data
- Big data
- Choosing the right algorithm
- SQL Server and machine learning
- Machine learning
- Variability
- Skewness
- Central tendency
- Statistics 101
- Visualizing the types of data
- Math and statistics
- Data science domains
- Final acceptance
- Deployment and visualization
- Modelling and analysis
- Getting data
- Business understanding
- Data science project life cycle
- Introducing data science
- Data Science Overview
- Reviews
- Get in touch
- Conventions used
- Download the example code files
- To get the most out of this book
- What this book covers
- Who this book is for
- Preface
- Packt is searching for authors like you
- About the reviewers
- About the authors
- Contributors
- Packt.com
- Why subscribe?
- About Packt
- Title Page
- coverpage
- coverpage
- Title Page
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the authors
- About the reviewers
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Conventions used
- Get in touch
- Reviews
- Data Science Overview
- Introducing data science
- Data science project life cycle
- Business understanding
- Getting data
- Modelling and analysis
- Deployment and visualization
- Final acceptance
- Data science domains
- Math and statistics
- Visualizing the types of data
- Statistics 101
- Central tendency
- Skewness
- Variability
- Machine learning
- SQL Server and machine learning
- Choosing the right algorithm
- Big data
- SQL Server and big data
- Summary
- SQL Server 2017 as a Data Science Platform
- Technical requirements
- SQL Server evolution
- What's available in the pack?
- History of SQL Server
- SQL Server in the cloud
- Azure SQL Database
- Azure SQL Data Warehouse
- SQL Server Services and their use with data science
- SQL Server Integration Services
- SQL Server Analysis Services
- Tabular Mode
- Multidimensional mode
- PowerPivot Mode
- Querying languages
- Reporting Services
- Development tools for Reporting Services
- Power BI Report Server
- Machine Learning Services
- Summary
- Data Sources for Analytics
- Technical requirements
- Getting data from databases
- Importing data from SQL Server
- Importing data from other database systems
- Importing flat files
- Working with XML data
- Working with JSON
- Retrieve data as JSON
- Processing stored JSON data
- External data with PolyBase
- Installing and configuring
- Summary
- Data Transforming and Cleaning with T-SQL
- Technical requirements
- The need for data transformation
- Database architectures for data transformations
- Direct source for data analysis
- Staging–target scenario
- Landing–staging–target scenario
- Tools eligible for data movement
- Distributed queries
- SQL Server Integration Services
- Why should we use SSIS?
- What is needed to develop an SSIS solution?
- Where should SSIS be used?
- Is there an alternative to SSIS?
- Transforming data
- Full data load
- Incremental data load
- The MERGE statement
- CHECKSUM
- Temporal tables
- Denormalizing data
- Relational normalization
- First normal form
- Second normal form
- Third normal form
- Need for denormalization
- Ways of denormalization
- Computed columns
- Denormalization using joins
- Using views and stored procedures
- Database applications
- Using views
- Using stored procedures
- Performance considerations
- Writing correct code
- Using indexes
- B-tree indexes
- COLUMNSTORE INDEX
- Summary
- Questions
- Data Exploration and Statistics with T-SQL
- Technical requirements
- T-SQL aggregate queries
- Common properties of aggregate functions
- Aggregate functions
- COUNT COUNT(*) and COUNT_BIG
- MIN and MAX
- SUM
- AVG
- VAR and VARP
- STDEV and STDEVP
- Using groups
- Using the HAVING clause
- Ranking framing and windowing
- Ranking functions
- ROW_NUMBER
- RANK
- DENSE_RANK
- NTILE
- Running aggregates
- Using aggregate functions in running aggregates
- Using aggregate functions
- Using the LEAD and LAG functions
- Calculating with percentiles
- The PERCENT_RANK and CUME_DIST functions
- The PERCENTILE_CONT and PERCENTILE_DISC functions
- Summary
- Questions
- Custom Aggregations on SQL Server
- Technical requirements
- Overview of SQLCLR
- Use cases of using SQLCLR
- How to work with SQLCLR
- Instance and database configurations to use with SQLCLR
- Creating CLR aggregations
- Example goal and assignment
- Skeleton of CLR aggregation
- Implementing methods
- Implementing custom serialization
- Implementing the Init method
- Implementing the Accumulate method
- Implementing the Merge method
- Implementing the terminate method
- Deployment and testing
- Limitations and performance considerations
- Development issues and risks
- Maintenance issues and risks
- Performance issues and risks
- Summary
- Questions
- Data Visualization
- Technical requirements
- Data visualization – preparation phase
- Power BI Report Server
- Starting with Power BI Desktop
- Defining the data source
- Adding visualizations to the Report
- Visual interactions
- Publishing reports
- SQL Server Reporting Services
- Adding charts to Reports
- Using SQL Server Data Tools
- Summary
- Data Transformations with Other Tools
- Technical requirements
- Categorization missing values and normalization
- Categorization
- Missing values
- Normalization
- Z-score
- Feature-scaling
- Using Integration Services for data transformation
- Setting up a SSIS project
- Categorizing the products
- Using R for data transformation
- Preparing client R environment
- R Syntax first steps
- Working example of Z-score computed in R
- Using Data Factory for data transformation
- Creating Azure Data Factory
- Creating simple copy data with ADF
- Summary
- Questions
- Predictive Model Training and Evaluation
- Technical requirements
- Preparing SQL Server
- Setting up and configuring ML services
- Preparing to install our own R packages
- Creating data structures
- The concept of machine learning in databases
- Creating physical data structures
- Creating common objects
- Creating objects using filestreams
- Creating objects using temporal tables
- Deploying training and evaluating a predictive model
- Saving our machine learning model to filestreams
- Saving a machine learning model to temporal tables
- Summary
- Questions
- Making Predictions
- Technical requirements
- Reading models from a database
- Reading the model from a common table
- Reading the model from a temporal table
- Submitting values to an external script
- Submitting values into the external script
- Deserializing a predictive model
- Making the prediction
- Using the PREDICT keyword
- Making the predictive model self-training
- Re-calculating a predictive model regularly
- Re-calculating a predictive model asynchronously
- Creating a message type
- Creating a contract
- Creating queues and services
- Sending a request to train a new model
- Consuming requests and sending responses
- Testing the asynchronous solution
- Summary
- Questions
- Getting It All Together - A Real-World Example
- Technical requirements
- Assignment and preparation
- SQL Server
- Data description
- Data exploration
- Exploring data using T-SQL
- Exploring data using the SSIS Data Profiling Task
- Exploring the SourceData.Actions table
- Exploring data using R
- Data transformation
- Training and using predictive models for estimations
- Preparing the schema for the model
- Training the model
- Using the rxLinMod function and finishing the model
- Using the model in predictions
- Summary
- Questions
- Next Steps with Data Science and SQL
- Data science next steps
- Next steps with SQL Server
- Big data clusters
- Machine learning
- Machine learning services on Linux
- Machine learning high availability
- Data science in the cloud
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-06-10 19:14:46
