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, and you will learn how to analyze data with AI and ML models.
Datafication is becoming increasingly prevalent in many areas of our lives, from business to education and healthcare. It has the potential to improve decision-making by providing insights into patterns, trends, and correlation between seemingly unconnected pieces of data. This book explains the evolution, principles, and patterns of datafication used in our day-to-day activities. It covers how to collect data from a variety of sources, using technologies such as edge, streaming techniques, REST, and frameworks, as well as data cleansing and data lineage. A data analysis framework is provided to guide you in designing and developing AI and ML projects, including the details of sentiment and behavioral analytics.
Introduction to Datafication 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 Learn
- Understand the principles and patterns to be adopted for datafication
- Gain techniques for sourcing and mining data, and for sharing data with a data pipeline
- Leverage the AI and ML algorithms most suitable for datafication
- Understand the data analysis framework used in every AI and ML project
- Master the details of sentiment and behavioral analytics through practical examples
- Utilize development methodologies for datafication engineering and the related security and governance framework
Who This Book Is For
Students, data scientists, data analysts, and AI and ML engineers
