I have written a list of books that have helped me in the process of growing from entry-level to advanced reading on my career journey as a Data Scientist. These books are yearly guides I go back to read at the beginning of a new career year.
Getting Real about Bias: Thinking, Fast and Slow- Daniel Kahneman
I always recommend this book because it is not only useful for Data Science. Daniel, a psychologist, focused more on decision-making, explaining explicitly why and how we choose the things we do. On a daily basis, we make decisions, and the act of making the right decisions is a skill that is learned over time. He also gave an in-depth view of biases, both consciously and unconsciously.
Telling Stories with Data: Info We Trust – RJ Andrews
The whole essence of gathering Data is to disseminate the results of a particular project, and this is what this book extensively talks about. Andrews shows the whole Analysis cycle right through to the output. He built a long-lasting effect story based on Data.
Get Inspired about Deep Learning: Genuis Makers – Cade Metz
The Use of Deep Learning is taught in this book. Documenting the history rise, fall, and rising again of deep learning from the very beginning. There are so many deep-learning techniques listed in the book.
Building a Statistics Foundation: The Art of Statistics – David Spiegelhalter
Statistics cut across every industry and are very crucial in the world of programming because, without statistical Data, programming cannot be embarked on. Whilst, this book is not just an introduction, David went through everything statistics from the basics. The use of statistics in compiling and analyzing Data is extensively treated here.
The Future of Data Science: The Big Nine – Amy Webb
The future of a thing is important. A better understanding of the future of data science has awakened me to position myself for relevance in that future. This led me to seek the future of Data Science. This book reveals the future of most cutting-edge technologies. The Big Nine gives an in-depth future of Artificial Intelligence.
Understanding Causality: The Book of Why- Judea Pearl
This book breaks down a new effective model for how things affect one another. Pearl emphasizes Causality vs. Correlation. This book helped me to learn why certain things cannot be possible due to the relationship involved.
Building Ethical Models: The Cyber Effect – Mary Aiken
App, models, and pipelines are built to solve a problem. The problems are encountered by people, and Aiken is bringing Data Scientists to take note of how the problem they solve affect people’s lives. For instance, smartphones solved long-distance communication problems, and now the lives of people using them are affected by a lack of physical communication. People communicate more online than offline.
This book emphasizes how dangerous online content can be. This book helped me put paramount thought into researching from an ethical perspective before building any tool or model. I hope you forward this information to upcoming Data Scientists.
