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LinkedIn Learning

SQL Server Machine Learning Services: Python

via LinkedIn Learning

Overview

Learn how to analyze SQL Server data with Python. Discover how to perform statistical analyses, generate graphics, and process tabular data—directly in SQL Server.

Learn how to analyze SQL Server data with Python. Database expert Adam Wilbert shows how to use a powerful combination of tools, including high-performance Python libraries and the Machine Learning Services add-on, directly inside SQL Server to streamline analysis. Adam shows how to use Python scripts to perform statistical analysis, generate graphics such as scatterplots and bar charts, and process tabular data. He also explains how to turn a Python script into a stored procedure and set up standalone ML services to execute scripts without impacting SQL Server performance.

Syllabus

Introduction
  • Analyze SQL Server data with Python
  • What you should know
  • Using the exercise files
1. Get Started with MLS
  • What is machine learning services?
  • Install ML services for Python
  • Enable script execution in SQL Server
  • Use variables in Python
  • Create a Python while loop
2. Write Python Scripts for SQL Server
  • Import a dataset from SQL Server
  • Manipulate a data frame
  • Output a result set to SQL Server
  • Python syntax pitfalls
  • Challenge: Import a data frame
  • Solution: Import a data frame
3. Python Package Modules and Libraries
  • The Anaconda open-source packages
  • Functions in the revoscalepy package
  • Model, train, and score with microsoftml
  • Produce graphics with MatPlotLib
  • Get descriptive statistics with pandas
  • Challenge: Sample a data frame
  • Solution: Sample a data frame
4. Processing Tabular Data
  • Return values with indexes and series
  • Convert a series to a data frame
  • Add multiple series to a data frame
  • Include the index in a data frame
  • Slice a data frame to series
  • Challenge: Import and process data
  • Solution: Import and process data
5. Create a SQL Stored Procedure
  • Create a Python stored procedure
  • Parameterize the procedure
  • Challenge: Write a stored procedure
  • Solution: Write a stored procedure
6. Create an External Data Science Client
  • Install MLS on a standalone server
  • Add development tools to the client
  • Work with Jupyter Notebooks
Conclusion
  • Next steps

Taught by

Adam Wilbert

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