This self-paced course was created by the Microsoft World Wide Learning team for data scientists, with the aim of supporting the exam DP-100: Designing and Implementing a Data Science Solution on Azure.

You’ll learn about the data science process and how solutions within Microsoft Azure can best support it. You’ll then delve into machine learning model training and deployment, allowing you to use a greater degree of automation within your data science projects.

Use the Azure machine learning to improve your data modelling

Machine learning is revolutionising a huge range of industries, including data science. Guided by industry experts, you’ll review the machine learning capabilities available in Microsoft Azure, and learn how to use it for local model training, model selection automation, model management, and monitoring.

Consolidate your knowledge by building a chatbot

Once you have built your understanding of Microsoft Azure and its functionality, you’ll put that knowledge into practice by building a simple chatbot. This offers you the opportunity to customise and troubleshoot within the platform, as well as a chance to build hands-on experience.

By the end of the course you’ll have the confidence and knowledge to design and implement a data science solution using Microsoft Azure’s AI stack.

Syllabus

Week 1
Course Introduction
- About this Course
  This activity introduces you to the course outline and the learning outcomes. It also explains how to navigate the course on the FutureLearn platform and access the CloudSwyft Hands-On Labs.
- Data Science
  In this activity, you will learn about data science and the different roles that comprise the data science team.
- The Data Science Process
  In this activity, you will explore each of the predefined steps involved in the data science process.
- Azure and Data Science
  In the activity, we will provide a broad overview of Azure services and products used for data science and machine learning.
- Cloud-based and On-premises Machine Learning
  In this activity, you will examine the functionality and key features of Azure's cloud-based and on-premises machine learning products.
- Wrapping up the Week
  This is the closing activity for Week 1, where you will have the opportunity to reflect on what you have learned during the week.
Week 2
Data Science on Azure
- Development platforms and tools
  In this activity, we will review the development platforms and tools that Microsoft provides for machine learning.
- Introduction to Data Science with Azure Notebooks
  In this activity, you will learn the functionality of Azure Notebooks through a number of exercises.
- CloudSwyft Hands-On Lab 1
  In this activity, you will put the concepts from the previous activities into action by completing your first CloudSwyft Hands-On Lab.
- Introducing the Azure Machine Learning (AML) Service
  This activity explains how the Azure Machine Learning service augments and automates the data science process through the use of containerisation and special machine learning services.
- Training Machine Learning Models
  This activity demonstrates how to use Azure Machine Learning service to select a model, tune hyperparameters, train a model, deploy a model and monitor the deployed model.
- Wrapping Up the Week
  This is the closing activity for Week 2, where you'll have the opportunity to reflect on what you've learned during the week.
Week 3
Azure Machine Learning Service
- Registering and Deploying Models
  In this activity, you will create a deployment workspace, learn about registering a model to the container registry, and deploy a model as a web service.
- CloudSwyft Hands-On Lab 2
  In this activity, you will complete your CloudSwyft Hands-On Lab to put the concepts from the previous activities into action.
- Automating Model Selection with AML Service
  In this activity, you will learn about the machine learning pipeline and how AML service's AutoML can automate parts of it. You'll also learn about the Machine Learning Operations (MLOps) capabilities that AM Learning provides.
- CloudSwyft Hands-On Lab 3
  In this activity, you will complete your CloudSwyft Hands-On Lab to put the concepts from the previous activities into action.
- Managing and Monitoring Models with AML Service
  In this activity, you will learn about Azure Machine Learning service model management, and tracking features that can be integrated into pipelines.
- Wrapping Up the Course
  This is the closing activity for Week 3 and the course where you'll have the opportunity to reflect on what you have learned!

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Who is this accredited by?

Microsoft:

This course is accredited by Microsoft

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

High level overview of Azure data science related services
Deep dive on the premier data science service, Azure Machine Learning service, which supports automation of machine learning model training and deployment.
Navigate the Azure portal and to create services there.
Familiarity with the Azure data storage technologies

Who is the course for?

This course is best suited to aspiring data scientists and those whose job responsibilities primarily fall into the area of data science.

Who developed the course?

CloudSwyft Global Systems, Inc.

CloudSwyft has partnered with the top global technology companies to deliver cutting edge digital skills learning across the modern workplace.

About this ExpertTrack

Investigate Data Science Fundamentals using Microsoft Azure

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Earn digital course certificates and a final award that you can share online, with potential employers, and your professional network
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Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
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Experience the power of social learning, and get inspired by an international network of learners
Share ideas with your peers and course educators on every step of the course
Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

As you work through the course, use notifications and the Progress page to guide your learning
Whenever you’re ready, mark each step as complete, you’re in control
Complete 90% of course steps and all of the assessments to earn your certificate

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