SQL Server

DP-080T00: Querying Data with Microsoft Transact-SQL

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Trukmė Kalba Miestas Kaina Data ir registracija kursui
2 dienos lietuvių k. Nuotolinis 1000 EUR


This course will teach the basics of Microsoft's dialect of the standard SQL language: Transact-SQL. Topics include both querying and modifying data in relational databases that are hosted in Microsoft SQL Server-based database systems, including: Microsoft SQL Server, Azure SQL Database and, Azure Synapse Analytics.

Kursai skirti

This course can be valuable for anyone who needs to write basic SQL or Transact-SQL queries. This includes anyone working with data as a data analyst, a data engineer, a data scientist, a database administrator or a database developer. It can also be useful for others peripherally involved with data, or wanting to learn more about working with data such as solution architects, students and technology managers.

Kurso nauda

In this course students will learn:

  • Use SQL Server query tools
  • Write SELECT statements to retrieve columns from one or more tables
  • Sort and filter selected data
  • Use built-in functions to returned data values
  • Create groups of data and aggregate the results
  • Modify data with Transact-SQL using INSERT, UPDATE, DELETE and MERGE

  1. Getting Started with Transact-SQL
  2. Sorting and Filtering Query Results
  3. Using Joins and Subqueries
  4. Using Built-in Functions
  5. Modifying Data

Module 1: Introduction to Azure Machine Learning
Getting Started with Azure Machine Learning
Azure Machine Learning Tools
Lab : Creating an Azure Machine Learning Workspace
Lab : Working with Azure Machine Learning Tools
Module 2: No-Code Machine Learning with Designer
Training Models with Designer
Publishing Models with Designer
Lab : Creating a Training Pipeline with the Azure ML Designer
Lab : Deploying a Service with the Azure ML Designer
Module 3: Running Experiments and Training Models
Introduction to Experiments
Training and Registering Models
Lab : Running Experiments
Lab : Training and Registering Models
Module 4: Working with Data
Working with Datastores
Working with Datasets
Lab : Working with Datastores
Lab : Working with Datasets
Module 5: Compute Contexts
Working with Environments
Working with Compute Targets
Lab : Working with Environments
Lab : Working with Compute Targets
Module 6: Orchestrating Operations with Pipelines
Introduction to Pipelines
Publishing and Running Pipelines
Lab : Creating a Pipeline
Lab : Publishing a Pipeline
Module 7: Deploying and Consuming Models
Real-time Inferencing
Batch Inferencing
Lab : Creating a Real-time Inferencing Service
Lab : Creating a Batch Inferencing Service
Module 8: Training Optimal Models
Hyperparameter Tuning
Automated Machine Learning
Lab : Tuning Hyperparameters
Lab : Using Automated Machine Learning
Module 9: Interpreting Models
Introduction to Model Interpretation
using Model Explainers
Lab : Reviewing Automated Machine Learning Explanations
Lab : Interpreting Models
Module 10: Monitoring Models
Monitoring Models with Application Insights
Monitoring Data Drift
Lab : Monitoring a Model with Application Insights
Lab : Monitoring Data Drift