Introduction to Datafication: Implement Datafication Using AI and ML Algorithms

ISBN-13: 9781484294956 / Angielski

Shivakumar R. Goniwada

Opis

Beginning-Intermediate user level

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 Learn

• What 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 For

Students, data scientists, data analysts, and AI and ML engineers

Spis treści

Chapter 1: Introduction to Datafication

This chapter Provides introduction of datafication and data opportunity and data problems and also provide insight into digitization vs datafication and also provide story of past to preset of datafication

Chapter 2: Elements of Datafication

This chapter provides the list of elements of datafication such as process value generation, monetization etc.

Chapter 3: Principles, Patterns and Theories of Datafication

Provides the list of available patterns and principles to adopt to design a datafication

Chapter 4: Datafication Algorithms

This chapter provides the details of algorithms that are required for datafication

Chapter 5: Datafication Pipeline

This chapter provides the details of how you capture the data from source and process it and ready for use with quality and rules

Chapter 6: Data Sharing

This chapter provides the details of how and what are the methods are available for data sharing

Chapter 7: Behavioral and Sentimental Analytics

This chapter provides the details and code analysis for behavioral and sentimental analytics

Chapter 8: Security in Datafication

This chapter provides the details of security requirements and implementation details of datafication

Chapter 9: Data Governance

Without a proper governance, the datafication implementation is waste of time, so, this chapter provides details of the governance strategy and implementation datafication governance

Chapter 10: Datafication Use Case and Implementation 

This chapter provides the detail implementation of datafication with the use cases from insurance or banking industry etc. 

O autorze

Shivakumar R. Goniwada is an author, inventor, chief enterprise architect, and technology leader with more than 23 years of experience in architecting cloud-native, data analytics, and event-driven systems. He works for Accenture and leads a highly experienced technology enterprise and cloud architect team. In his 23 years of experience, Shivakumar has led many complex projects across industries and the globe. He has 10 software patents in cloud computing, polyglot architecture, software engineering, and IoT. Shivakumar is a speaker at multiple global and in-house conferences. He holds multiple data science certifications: Accenture Master Technology Architecture (MTA), Google Professional, AWS. He completed his Executive MBA at the MIT Sloan School of Management. And he authored the Apress book, Cloud Native Architecture and Design.

Blurb

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.

You will:

• 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