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

 

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.

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

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.

Course Format:

Присъствен (Classroom) Курс в Учебната ни зала или В Офис на Клиент

Онлайн (Online/Virtual) Курс във виртуална зала с инструктор

Course Language Option

Български (Bulgarian)

Английски (English)

You can choose the language in which the training will be conducted – Bulgarian or English. All our instructors are fluent in English.

Student Guides:


 

The training materials are available in electronic format. They can be used online / offline on any device. Lifetime access.

Lab Environment:

Each student has their own lab environment where the exercises are conducted, part of the course. You do not need to install software on a computer or special hardware requirements.
Participants in a face-to-face format in our Training Center have an individual computer during the training.

At Course Completion:

Lifetime Access - Video Archive 24/7

Certificate of Course Completion

Lifetime access to a video archive with recording of each individual lecture.
Official internationally recognized certificate for completed training course.

Course Duration:

  • 3 working days (09:00 – 17:00)
    or
  • 24 hours  training (theory and practice) in non-working hours lasting 2 weeks
    Saturday and Sunday 10:00 – 14:00, 14:00 – 18:00, 18:00 – 22:00
    Monday and Wednesday 19:00 – 23:00
    Tuesday and Thursday 19:00 – 23:00

Payments:

 

 

An application for an invoice is accepted at the time of enrollment in the respective course.

An invoice is issued within 7 days of confirming the payment.

Next Class:

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For more information, use the contact format. We will contact you to confirm the data.

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.

This Class will teach you how to pass the following exams: