I Preface

Topics Covered: Why This Book?, Who Should Read This Book?, Scope of This Book, Outline of This Book

Most probably you might have already heard the quotes like ‘Data is the new oil’, ‘AI is the new electricity’. There is no doubt that data and AI have become the most valuable assets of the digital ecosystem. Different applications of data and AI are helping businesses, governments, and society in general. Due to the unprecedented adoption of data and AI techniques, the demand for data professionals has also skyrocketed.

Why This Book?

I have been thinking to cover data and AI concepts in a form of a book for quite some time. But as Simon Sinek suggests, ‘Start with Why?’, it gives you purpose. The very first question that I asked myself was why I need to write a book on ‘Data and AI’? I pondered over this question for several weeks and then came up with several reasons:

First, data and AI is a vast field, in fact, it’s an amalgamation of multiple fields. There is already so much great literature that has been written on various aspects and those books cover the specific areas in great length and depth. But there is no literature in my knowledge that covers the overall landscape holistically.

Second, data and AI projects can’t be delivered by a single person, these projects are a team effort. Members with different niche skill-set collaborate to deliver the business value of data. It has been identified that data professionals can be effective with a T-shaped skill-set, which means they have their niche but they also have enough knowledge and exposure of the horizontal layer. With this book, I try to cover the horizontal layer of data and AI space in just enough depth end-to-end.

Third, after working in Data and AI field for more than a decade, I have developed my own perspective around it, which I would like to share with the other learners and practitioners. I truly believe that if I really understand my stuff, I should be able to teach it to a duck. Moreover, this exercise will immensely help to structure my knowledge as well.

Fourth, my focus in this book would be on the concepts. Why? Because concepts are the abstraction of the real-world phenomenon, if you know the concept, you can explore it and build on it as you desire. I intend to cover the data and AI concepts in an intuitive way.

Finally, I plan to cover all the aspects of the Data and AI field holistically, which will help you to connect the dots and build your own perspective. I am confident that you will be able to contribute to your data and AI projects more effectively.

Who Should Read This Book?

This book is for anyone who calls himself a data professional or wants to become one.

I call a data professional as anyone who is a stakeholder in data and AI projects. Be it a technical or business person, there is a minimum level of understanding that is required to be effective in data and AI teams. I have used parts of the material with people from business and technology alike.

A data professional can be a data analyst, data scientist, data engineer, machine learning (ML) engineer, business intelligence (BI) engineer, cloud engineer, DevOps/MLOps engineer, data architect, and head of analytics.

Don’t worry if you are completely new to the data and AI field, you have even more reason to be excited. There are no prerequisites to read and grasp the concepts mentioned in the book. This is a promise that learning data and AI concepts will change the way you think about the problems you want to solve and show you how to tackle them by unlocking the power of data.

Scope of This Book

In this section I am going to mention what and what not I am going to cover in this book.

As you can imagine, the data and AI being a vast and complex field, it’s nearly impossible to cover every topic from concept to implementation in a single book. Hence as I am covering the breadth of topics, I will be limiting the scope of this book to the concepts only. I have already mentioned in the previous section that concepts are the starting point and important in a view that it abstracts the real-world phenomenon, it exposes you to the topic well enough so that you can explore it further on need basis.

Covered: breadth of the Data and AI field to just enough depth, sticking to the comcepts
Not covered: specialization in each of the sub-fields, not the implementation details

Outline of This Book

Before I mention the outline, have a look at the above figure, what do you understand from above? Does it look too complicated? Are you aware of the layers and terms mentioned?

Now lets have a look at this figure, does this fugure look simpler? What do you understand from this?

As it turns out, before building or working on any data and AI platform, we need to understand the underlying different layers and what they are made of. Even before that, we we need to make sense of the building blocks.

The book is divided in three parts, first one builds the foundation, second one covers the components and third part talks about various data and AI platforms.

Data and AI Foundation: This part covers the basic concepts like Data, , Mathematics, IT/Programming, Business Domain, AI
Data and AI Components: This part exposes you to different layers of a data and AI platform and its components like Data Ingestion, Data Storage, Data Engineering, Data Science, Data Visualization, Data Operationalization, Data Architecture, Data Governance, Data Management
Data and AI Platforms: This part builds your understanding around various platforms like Open Source, AWS, Azure, GCP, Databricks, Snowflake