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It is probably not a secret that data and AI has become one of the most exciting fields of this age. Although it may seem the buzzword of our time, it is certainly not the hype. This exciting field opens the way to new possibilities and is becoming indispensable to our daily lives.
Data and AI refers to all the analytical tools and methods business employ to form a better understanding of the information they collect to improve their services or investments. Data and AI focuses on analysis and interaction with information in a meaningful way to promote better decision-making in the future.
Data and AI solutions are not built in silos, data needs to be ingested, processed, analyzed and presented in a specific context to add value to business.
The ‘Data and AI’ term in this book represents all the data related fields like data dcience(DS), machine learning(ML), data engineering (DE), big data, cloud computing for data, DataOps fields collectively.
Organizations, big or small, are heavily investing in data and AI research and applications these days. And hence, it has become the hottest career. If you want to become a data and AI practitioner, there is no better time than this.
Aspirants are taking different approaches to get into the field, some are fortunate enough to be put into projects as freshers, but most aspirants are building their capabilities by learning theory and applying them on public data-sets.
While there is no dearth of free & paid material, too much of information has only confused the current crop of data aspirants.
Based on the questions that I am asked by them on day to day basis, I can see how perplexed they are. Not to say, taking advantage of the situation, most of the training institutes are minting money like anything, bundling even non-relevant courses as well in data and AI ones.
Why this book?
From a time around when data field started picking up, every other day I get many messages from starters & enthusiasts on ‘How can I get into data and AI field?’. Over a while, I have improvised my response based on the follow-up questions they ask like:
- What is the difference between different sub-fields (i.e. data science, data engineering, machine learning etc)?
- What is the end-to-end process and what are the roles in data and AI, who does what?
- What are the main concepts, which tools and technologies they need to learn?
- Which are the quality resources (books, courses, channels, blogs, podcasts etc) they need to refer to?
- How to build a data and AI portfolio based on your role?
- How to write a resume for data role?
- How to build a helpful network with fellow data professionals?
- How to search for the job in data field?
- How to prepare for the interview for a data role?
- How to stay up-to-date in this still-evolving field?
You can notice that these questions are not conceptual ones and there is no dedicated material to address these challenges and roadblocks.
How about a book or course that gives you enough exposure to the data and AI field that you can yourself analyze what is needed for you and build your own roadmap?
My answer to the above questions is this book.
What this book covers?
On an abstract level, it looks fairly simple, overall process can be divided into four logical phases: Explore, Build, Launch & Steer.
Explore is the very first phase of your data and AI journey, where you need to understand the overall landscape before diving deep. This step covers Q1 & Q2.
Build phase covers all the concepts, processes, tools you need to learn and the resources you need to refer to gain required knowledge. This step responses Q3 & Q4.
Launch is the phase where you build your portfolio, network with like-minded professionals and start looking for job. This step elaborates from Q5 to Q9.
Steer phase details out how you can stay up to date in this ever-evolving field. This step answers Q10.
This book is organized into 8 chapters covering above mentioned framework end-to-end:
Chapter 1 covers the high-level approach to how you can build your road-map to learn data and AI, and build a career in it.
Chapters 2–7 cover all the steps mentioned in chapter 1 in greater depth to give you enough knowledge to build your road-map.
Chapter 8 guides on how to continue from here, keep yourself updated and remain ahead of the curve in this ever-evolving field.
Who this book is for?
This book can be useful for a variety of readers, but I wrote it with two main target audiences in mind. One of these target audiences is university students learning about data and AI field, including those who are starting a career in data intelligence. The other target audience is professionals working in other fields, who do not have a data or technology background but want to rapidly acquire one and expand their career.
There is no assumption or prerequisites for a reader to cover before reading this series.
Looks interesting? Stay tuned to read the upcoming chapter as soon as it is published.
Please note that this book does not solely cover typical data and AI concepts.
Consider it as a personal coach to launch your career in data and AI field which gives you enough exposure to the field so that you can yourself prepare a roadmap of your data and AI journey.
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