DP-100 – Designing and Implementing a Data Science Solution on Azure

Microsoft Official Courses (MOC)

Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure

 


За Курса (About this Course):

    • Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.

Цели – Какво ще научите (Course Goals/Skills):

  • Explain the evolving world of data
  • Survey the services in the Azure Data Platform
  • Identify the tasks that are performed by a Data Engineer
  • Describe the use cases for the cloud in a Case Study
  • Choose a data storage approach in Azure
  • Create an Azure Storage Account
  • Explain Azure Data Lake Storage
  • Upload data into Azure Data Lake
  • Explain Azure Databricks
  • Work with Azure Databricks
  • Read data with Azure Databricks
  • Perform transformations with Azure Databricks
  • Create an Azure Cosmos DB database built to scale
  • Insert and query data in your Azure Cosmos DB database
  • Build a .NET Core app for Azure Cosmos DB in Visual Studio Code
  • Distribute data globally with Azure Cosmos DB
  • Use Azure SQL Database
  • Describe Azure Data Warehouse
  • Create and Query an Azure SQL Data Warehouse
  • Use PolyBase to Load Data into Azure SQL Data Warehouse
  • Be able to explain data streams and event processing
  • Understand Data Ingestion with Event Hubs
  • Understand Processing Data with Stream Analytics Jobs
  • Understand Azure Data Factory and Databricks
  • Understand Azure Data Factory Components
  • Be able to explain how Azure Data Factory works
  • Have an introduction to security
  • Understand key security components
  • Understand securing Storage Accounts and Data Lake Storage
  • Understand securing Data Stores
  • Understand securing Streaming Data
  • Explain the monitoring capabilities that are available
  • Troubleshoot common data storage issues
  • Troubleshoot common data processing issues
  • Manage disaster recovery

Курсът е предназначен за (Audience):

  • This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Формат на курса

Присъствен Курс

Онлайн (Live) Отдалечен

 


Език на курса (Course Language Option)

Български (Bulgarian)
Български (Bulgarian)

Английски (English)
Английски (English)

 

Може да изберете Език на който да се проведе обучението – български или английски. Всичките ни инструктори владеят свободно английски език.

Учебни Метериали: в електронен формат (Учебните материали са на английски), включени в цената с неограничен достъп.

Лабораторна среда: всеки курсист разполага със собствена лаб среда, където се провеждат упражненията, част от курса.

Видео Архив (24/7)

 

Сертификат за Завършен Курс


Продължителност

  • 3 работни дни (09:00 – 17:00)

или

  • 24 уч.ч. обучение (теория и практика) в извънработно време с продължителност 2 седмици
  • събота и неделя 10:00 – 14:00, 14:00 – 18:00, 18:00 – 22:00
  • понеделник и сряда 19:00 – 23:00
  • вторник и четвъртък 19:00 – 23:00

Плащане

Заявка за издаване на фактура се приема към момента на записването на съответния курс.

Фактура се издава в рамките на 7 дни от потвърждаване на плащането.


Предстоящи Курсове

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Ще се свържем с Вас за потвърждаване на датите.


Предпоставки (Изисквания) за Участие (Prerequisites):

Before attending this course, students must have:

  • A fundamental knowledge of Microsoft Azure
  • Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
  • Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.

Курсът подготвя за следните сертификационни нива