With revolutionary changes empowering the field of artificial intelligence (AI) and data science, you may find it hard to cope up with the rapid advancements in technology, not to mention the vast knowledge growing at large, and on a daily basis. To ease your curiosity, and keep you up-to-date with the ideas, concepts and practicality of these subjects, we bring to you the 11 best technical reads in AI as well as data science that will help you stay ahead in these technology areas. These books are a work of nonfiction. The list is presented in no particular order.
1. Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark
This is one of the most recent additions to the must read books in AI. Max Tegmark is an AI aficionado. He begins with a bit of fiction in this work, which is, by the way looks so real and believable. It’s like someone else’s dream stuck on your mind as you read it. Our life is pictured into three transitions: biological, cultural and technological phase with each transition redesigned for betterment. The author captures how technological disruption affects our lifestyle. You can buy the book here.
2 . Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo
Looking to make it big in data science? Does the data science paparazzi make you baffled? Well, don’t look back. This book is presented in a no-nonsensical style for anyone interested to get acquainted with data jargon with no mathematical complexity. It covers important topics such as Regression analysis, Neural networks, Decision trees, A/B testing and so on. What makes it simple and easy to read is the illustrations that match the real world scenario. A must-read for amateurs to get a really good grip on data science. You can buy the book here.
3. Microsoft Excel Data Analysis and Business Modeling (5th Edition) by Wayne Winston
When it comes to data analysis, you just cannot ignore the popular spreadsheet software Microsoft Excel. This book explains right from Excel basics to complex business analytics problems. Most of the problems or case studies presented in the book are focussed more on the financial side of business. This makes the reader understand the practicality of finance along with data analytics. The topics include a variety of features in Excel 2016 such as Pivot Tables, Descriptive Statistics, advanced functions such as OFFSET and INDIRECT, Excel Solver to address optimisation problems and even using Excel Macros to automate recurring tasks in data analysis. The author has made sure the reader gets more insights through the real-life examples present in the book. You can buy the book here.
4. Machine Learning by Tom Mitchell
First published in 1986, this book is the best introductory material for anyone, be it an academic student or a machine learning researcher, to learn the elementary aspects of ML. The author assumes the reader has no knowledge of artificial intelligence or statistics and provides an easy approach to understand both of these subjects. Although, there are a lot of mathematics and statistics’ concepts to master, this book is by far the most comprehensive material when it comes to basics of ML. Popular algorithms such as Neural Networks, Bayesian Learning with latest editions covering Reinforcement Learning as well.In addition, analysing datasets for ML is also illustrated with examples. You can buy the book here.
5. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund
This book opens up with the basics of the most popular statistical programming language R. In addition, the authors begin by explaining data visualisation and data transformation using functions in R. Tidyverse, which is a collection of R packages focussed on data science is also explained along with the use of an integrated development environment (IDE) called RStudio, for software development. The authors also ensure that the reader gets the gist of R by explaining the programming style in a simple way. Every section of the book is presented with an exercise, so that the reader can work his way to ace R programming. You can buy the book here.
6. A Student’s Guide to Python for Physical Modeling by Jesse Kinder and Philip Nelson
Python is slowly gaining traction as a popular programming language in data sciences. This book introduces the learner to start with Python right from scratch. From setting up the open-source Python programming environment to performing computing tasks such as simulation, this book covers it all. The authors have presented the concepts in an easy-to-understand manner. In addition, code samples, data sets and exercises are also provided so that the learner understands the jargon at his own pace. A must read for anyone interested in learning Python the classic textbook style way. You can buy the book here.
7. Head First Learn to Code: A Learner’s Guide to Coding and Computational Thinking by Eric Freeman
This book is a slight deviation from the above topics. It purely focuses on getting the leaner acquainted with the art of programming. Since data science requires programming to a larger extent, the book introduces the etiquette of programming helping them come up with writing better code. It uses Python as a base programming language to explain the concepts. The highlighting feature of this book is that it provides more visual depictions rather than relying on massive text information. It’s a definite-read for aspiring programmers for any IT domain. You can buy the book here.
8. AI and Analytics: Accelerating Business Decisions by Sameer Dhanrajani
An essential book aimed at corporate executives and aspiring entrepreneurs in the field of AI and data analytics, it provides business insights which help foster a radical change in an organisation by utilising growing technologies such as Chatbots, Blockchain and Cryptocurrency. The focus of the book is comprehensive strategies and methodologies in analytics which help organisations take decisions at an executive level. The author covers most of the popular business sectors such as Banking, Healthcare, Insurance, Retail and Life Sciences. You can buy the book here.
9. Generation Robot: A Century of Science Fiction, Fact, and Speculation by Terri Favro
This book is a novel which covers fiction as well as facts when it comes to AI. It focuses on the implications that robots could have on us in the future. Terri Favro discusses right from the work of popular science fiction writer Isaac Asimov to the comic books and movies on science fiction. She goes on to say that robotics and technology has permeated in our culture. Leaning more on the pop-culture content, this book is for anyone who is interested in robotics seeking a dose of science fiction. You can buy the book here.
10. The Industries of the Future by Alec Ross
In this book written by Alec Ross, an American technology policy expert, he provides a vivid picture of the next ten years in this digital era. He has mainly focussed on innovation happening in technology in countries he has visited across the globe. Economic insights in the digital technology coupled with captivating storytelling is what makes this book go into your to-read list. Technologies such as AI, cybersecurity and genomics forms the theme of the book. You can buy the book here.
11. Blockchain Basics: A Non-Technical Introduction in 25 Steps by Daniel Drescher
Blockchain technology is the latest buzzword when it comes to innovations in data science. With cryptocurrencies such as Bitcoin, Ethereum and Litecoin catching up lately, this book gives a straightforward and direct approach in providing information without troubling the reader with any mathematical complexity or programming jargon. The concepts are clearly explained with pictures and analogies. The interesting feature of this book is, it has neither adopted a technical or business-like approach which makes it more of a pleasure/aesthetic read. You can buy the book here.
Conclusion :
The ten books presented above will definitely help the reader achieve a firm foothold in the field of data science and AI. The foremost important thing is the reader should know when to apply data science concepts for a particular problem along with just learning concepts in order to excel in the field.
