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Data science with SQL server quick start guide : integrate SQL server with data science /
SQL Server started to fully support data science only with its last two editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning Services for their projects, then this is the ideal book for you.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd.,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Writing Queries with T-SQL; Before starting -- installing SQL Server; SQL Server setup ; Core T-SQL SELECT statement elements; The simplest form of the SELECT statement; Joining multiple tables; Grouping and aggregating data; Advanced SELECT techniques; Introducing subqueries; Window functions; Common table expressions; Finding top n rows and using the APPLY operator; Summary; Chapter 2: Introducing R; Obtaining R; Your first line R of code in R; Learning the basics of the R language | |
| 505 | 8 | |a Using R data structuresSummary; Chapter 3: Getting Familiar with Python; Selecting the Python environment; Writing your first python code; Using functions, branches, and loops; Organizing the data; Integrating SQL Server and ML; Summary; Chapter 4: Data Overview; Getting familiar with a data science project life cycle; Ways to measure data values; Introducing descriptive statistics for continuous variables; Calculating centers of a distribution; Measuring the spread; Higher population moments; Using frequency tables to understand discrete variables; Showing associations graphically; Summary | |
| 505 | 8 | |a Chapter 5: Data PreparationHandling missing values; Creating dummies; Discretizing continuous variables; Equal width discretization; Equal height discretization; Custom discretization; The entropy of a discrete variable; Advanced data preparation topics; Efficient grouping and aggregating in T-SQL; Leveraging Microsoft scalable libraries in Python; Using the dplyr package in R; Summary; Chapter 6: Intermediate Statistics and Graphs; Exploring associations between continuous variables; Measuring dependencies between discrete variables | |
| 505 | 8 | |a Discovering associations between continuous and discrete variablesExpressing dependencies with a linear regression formula; Summary; Chapter 7: Unsupervised Machine Learning; Installing ML services (In-Database) packages ; Performing market-basket analysis; Finding clusters of similar cases; Principal components and factor analyses; Summary; Chapter 8: Supervised Machine Learning; Evaluating predictive models; Using the Naive Bayes algorithm; Predicting with logistic regression; Trees, forests, and more trees; Predicting with T-SQL; Summary; Other Books You May Enjoy; Index | |
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