Book Image

Azure Data Scientist Associate Certification Guide

By : Andreas Botsikas, Michael Hlobil
Book Image

Azure Data Scientist Associate Certification Guide

By: Andreas Botsikas, Michael Hlobil

Overview of this book

The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam.
Table of Contents (17 chapters)
1
Section 1: Starting your cloud-based data science journey
6
Section 2: No code data science experimentation
9
Section 3: Advanced data science tooling and capabilities

Chapter 1: An Overview of Modern Data Science

Data science has its roots in the early eighteenth century and has gained tremendous popularity during the last couple of decades.

In this book, you will learn how to run a data science project within Azure, the Microsoft public cloud infrastructure. You will gain all skills needed to become a certified Azure Data Scientist Associate. You will start with this chapter, which gives some foundational terminology used throughout the book. Then, you will deep dive into Azure Machine Learning (AzureML) services. You will start by provisioning a workspace. You will then work on the no-code, low-code experiences build in the AzureML Studio web interface. Then, you will deep dive into the code-first data science experimentation, working with the AzureML Software Development Kit (SDK).

In this chapter, you will learn some fundamental data science-related terms needed for the DP 100 exam. You will start by understanding the typical life cycle of a data science project. You will then read about big data and how Apache Spark technology enables you to train machine learning models against them. Then, you will explore what the DevOps mindset is and how it can help you become a member of a highly efficient, multi-disciplinary, agile team that builds machine learning-enhanced products.

In this chapter, we are going to cover the following main topics:

  • The evolution of data science
  • Working on a data science project
  • Using Spark in data science
  • Adopting the DevOps mindset