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)
Section 1: Starting your cloud-based data science journey
Section 2: No code data science experimentation
Section 3: Advanced data science tooling and capabilities

The evolution of data science

If you try to find the roots of the data science practices, you will probably end up discovering evidence at the beginning of civilization. In the eighteenth century, governments were gathering demographic and financial data for taxation purposes, a practice called statistics. As years progressed, the use of this term was expanded to include the summarization and analysis of the data collected. In 1805, Adrien-Marie Legendre, a French mathematician, published a paper describing the least squares to fit linear equations, although most people credit Carl Friedrich Gauss for the complete description he published a couple of years later. In 1900, Karl Pearson published in the Philosophical Magazine his observations on the chi-square statistic, a cornerstone in data science for hypothesis testing. In 1962, John Tukey, the scientist famous for the fast Fourier transformation and the box plot, published a paper expressing his passion for data analysis and how statistics needed to evolve into a new science.

On the other hand, with the rise of informatics in the middle of the twentieth century, the field of Artificial Intelligence (AI) was introduced in 1955 by John McCarthy as the official term for thinking machines. AI is a field of computer science that develops systems that can imitate intelligent human behavior. Using programming languages such as Information Processing Language (IPL) and LISt Processor (LISP), developers were writing programs that could manipulate lists and various other data structures to solve complex problems. In 1955, Arthur Samuel's checkers player was the first piece of software that would learn from the games it has already played by storing board states and the chance of winning if ending up in that state in a cache. This checkers program may have been the first example of machine learning, a subfield of AI that utilizes historical data and the patterns encoded in the data to train models and enable systems to mimic human tasks without explicitly coding the entire logic. In fact, you can think of machine learning models as software code that is generated by training an algorithm against a dataset to recognize certain types of patterns.

In 2001, William S. Cleveland published the first article in which the term data science was used in the way we refer to it today, a science at the intersection of statistics, data analysis, and informatics that tries to explain phenomena based on data.

Although most people correlate data science with machine learning, data science has a much broader scope, which includes the analysis and preparation of data before the actual machine learning model training process, as you will see in the next section.