Data science with SQL server quick start guide : integrate SQL server with data science

Data science with SQL server quick start guide integrate SQL server with data science

Get unique insights from your data by combining the power of SQL Server, R and Python Key Features Use the features of SQL Server 2017 to implement the data science project life cycle Leverage the power of R and Python to design and develop efficient data models find unique insights from your data w...

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Bibliographic Details
Other Authors: Sarka, Dejan, author (author)
Format: eBook
Language:Inglés
Published: Birmingham ; Mumbai : Packt 2018.
Edition:1st edition
Subjects:
SQL server.
SQL (Computer program language)
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630676006719
Table of Contents:
  • 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
  • Using R data structures
  • Summary
  • 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
  • Chapter 5: Data Preparation
  • Handling 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.
  • Discovering associations between continuous and discrete variables
  • Expressing 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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