Covers Data Science concepts, processes, and the real-world hands-on use cases. Key Features ? Covers the journey from a basic programmer to an effective Data Science developer. ? Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP. ? Implementation of MLOps using Microsoft Azure DevOps. Description and “How is the Data Science project to be implemented? and ” has never been more conceptually sounding, thanks to the work presented in this book. This book provides an in-depth look at the current state of the world’s data and how Data Science plays a pivotal role in everything we do.This book explains and implements the entire Data Science lifecycle using well-known data science processes like CRISP-DM and Microsoft TDSP. The book explains the significance of these processes in connection with the high failure rate of Data Science projects.The book helps build a solid foundation in Data Science concepts and related frameworks. It teaches how to implement real-world use cases using data from the HMDA dataset. It explains Azure ML Service architecture, its capabilities, and implementation to the DS team, who will then be prepared to implement MLOps. The book also explains how to use Azure DevOps to make the process repeatable while we’re at it.By the end of this book, you will learn strong Python coding skills, gain a firm grasp of concepts such as feature engineering, create insightful visualizations and become acquainted with techniques for building machine learning models. What you will learn ? Organize Data Science projects using CRISP-DM and Microsoft TDSP. ? Learn to acquire and explore data using Python visualizations. ? Get well versed with the implementation of data pre-processing and Feature Engineering. ? Understand algorithm selection, model development, and model evaluation. ? Hands-on with Azure ML Service, its architecture, and capabilities. ? Learn to use Azure ML SDK and MLOps for implementing real-world use cases. Who this book is for This book is intended for programmers who wish to pursue AI ML development and build a solid conceptual foundation and familiarity with related processes and frameworks. Additionally, this book is an excellent resource for Software Architects and Managers involved in the design and delivery of Data Science-based solutions. Table of Contents 1. Data Science for Business 2. Data Science Project Methodologies and Team Processes 3. Business Understanding and Its Data Landscape 4. Acquire, Explore, and Analyze Data 5. Pre-processing and Preparing Data 6. Developing a Machine Learning Model 7. Lap Around Azure ML Service 8. Deploying and Managing Models

Practitioner’s Guide to Data Science: Streamlining Data Science Solutions using Python, Scikit-Learn, and Azure ML Service Platform PDF

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