وب سایت تخصصی شرکت فرین
دسته بندی دوره ها

Combining and Shaping Data

سرفصل های دوره

This course covers both conceptual and practical aspects of pulling together from different data sources, with different schemas and orientations, into a cohesive whole using Excel, Python, and various tools available on the Azure cloud platform.


1. Course Overview
  • 1. Course Overview

  • 2. Exploring Techniques to Combine and Shape Data
  • 1. Version Check
  • 2. Module Overview
  • 3. Prerequisites and Course Outline
  • 4. Connecting the Dots- Combining and Shaping Data
  • 5. Wide Form and Long Form Data
  • 6. Joins
  • 7. Aggregations
  • 8. Summary

  • 3. Combining and Shaping Data Using Spreadsheets
  • 01. Module Overview
  • 02. Load Data and Join Tables
  • 03. Aggregation Operations
  • 04. Join Tables Using Power Query
  • 05. Convert CSV Data to Table Format
  • 06. Convert Wide Form Data to Long Form
  • 07. Pivot Tables for Summary Statistics
  • 08. Unpivot Tables
  • 09. Calculating Cumulative Sum and Ranks on Partitioned Data
  • 10. Summary

  • 4. Combining and Shaping Data Using SQL
  • 1. Module Overview
  • 2. Introducing the Azure SQL Database
  • 3. Loading Data to Azure Blob Storage
  • 4. Creating an Azure SQL Database and Tables
  • 5. Loading Data Using Azure Data Factory Pipelines
  • 6. Querying Data in SQL Using Aggregations
  • 7. Performing Join Operations in SQL
  • 8. Performing Pivot and Unpivot Operations in SQL
  • 9. Summary

  • 5. Combining and Shaping Data Using Python
  • 01. Module Overview
  • 02. Data Cleaning- Missing Data and Outliers
  • 03. Getting Started with Azure Notebooks
  • 04. Combining and Shaping Data Using Pandas
  • 05. Identifying and Coping with Outliers
  • 06. Detecting Outliers Using Z-scores
  • 07. Handling Missing Values
  • 08. Cleaning Data
  • 09. Working with Imbalanced Data
  • 10. Handling Imbalanced Data with Scikit Learn
  • 11. Summary

  • 6. Integrating Data from Disparate Sources into a Data Warehouse
  • 01. Module Overview
  • 02. Transactional and Analytical Processing on Azure
  • 03. Creating an Azure SQL Data Warehouse and Uploading Files to Azure Blob Storage
  • 04. Loading Data from Blob Store to the Data Warehouse
  • 05. Loading Data from GCP Cloud Storage Buckets to the Azure SQL Data Warehouse
  • 06. Enabling Resource Permissions to an Azure Active Directory App
  • 07. Python Script to Transform Data Using Pyspark
  • 08. Transform Data Using a Spark Activity
  • 09. Copying Data from a Blob Storage Folder
  • 10. Summary

  • 7. Working with Streaming Data Using a Data Warehouse
  • 1. Module Overview
  • 2. Batch and Streaming Data
  • 3. Types of Windows
  • 4. Watermarks and the Notion of Time
  • 5. Creating a Stream Analytics Job and a Power BI Pro Account
  • 6. Configuring Input and Output for the Stream Analytics Job
  • 7. Visualizing Streaming Data Using Power BI
  • 8. Summary and Further Study
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    شناسه: 5133
    حجم: 517 مگابایت
    مدت زمان: 209 دقیقه
    تاریخ انتشار: 12 بهمن 1401
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