Introduction to Datafication

This book presents the process and framework you need to transform aspects of our world into data that can be collected, analyzed, and used to make decisions. You will understand the technologies used to gather and process data from many sources, including humans, machines, and social media, and you will learn how to analyze data with AI and ML models.Datafication is becoming increasingly prevalent in areas of our lives, including business, healthcare, education, and finance. it has the potential to improve decision-making by providing insights into patterns, trends, and correlation. This book explains the evolution, principles, and patterns of datafication used in our day-to-day activities. It covers how to get the data from a variety of sources, using technologies such as edge, streaming techniques, REST, and frameworks, and gives you details about data cleansing and data lineage. A data analysis framework guides you in designing and developing AI and ML projects, including the details of sentiment and behavioral analytics.The book teaches you how to engineer AI and ML projects by using various methodologies, covers the security mechanisms to be applied for datafication, and shows you how to govern the datafication process with a well-defined governance framework.What You Will LearnWhat are the principles and patterns to be adopted for datafication?What data analytics algorithms are available to use for datafication?How are techniques for sourcing and mining data used and for sharing data with the data pipeline?What AI and ML algorithms are suitable for datafication?What is the detailed data analysis framework used in every AI and ML project?What are the details of sentiment and behavioral analytics (with code and models)?What are the development methodologies to use for datafication engineering? And for the related security and governance framework?Who This Book Is ForStudents, data scientists, data analysts, and AI and ML engineers