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
