The most recommended data science books

Even if you are ultimately going to be working with terabytes of data, you’ll start out doing exploratory data analysis. The tool that you’ll use for that is most likely going to be Pandas. One of the best investments that you can make when becoming a data scientist is to become a Pandas expert, and there is no better book than Harrison’s to help you get there. Plus, many of the interview questions you will face during the hiring process will probably involve Pandas. Blow your interviewers out of the water by showing them corners of the Pandas library they didn’t even know!

Since data science is, at its core, people helping people make decisions, it is essential that we can establish productive relationships with our stakeholders. This is a skill that needs to be given the same level of effort as we give to coding or statistics. Gilbert’s book is a great resource to help technically oriented people to advance their people skills.

I used this book for several years starting in 2013 when the first edition came out. It absolutely holds up today. Learning the Python language (the syntax) is one thing. Learning how to design programs using this syntax is another. We need both but, unfortunately, many books forgo the latter for the former. Not this book! I like the Problem Solving and Worked Example sections: they help learners apply a disciplined, step-by-step strategy to programming projects. There are multiple, varied contexts here as well, which helps capture a broader base of learners. Bonus feature: the Computing & Society boxes.

Data visualizations are a cross between art, programming, and storytelling.

I've always been fascinated by the process creators go through to bring something from their imagination into existence. What amazed me was how the journey isn't a clear path from idea to finished product. I loved how Nadieh and Shirley documented their thought process – bringing me along and sharing why they made each decision.

Each chapter is a breakdown of a different data visualization. I laughed at how many of them were nerdy interests I loved: Dance Dance Revolution, Card Captor Sakura, and Lord of the Rings, to name a few. It reminded me that if I have fun, that'll show up in the finished product.

I like this book not just because it’s a complete guide to the many ins and outs of data cleaning with Python, but also because David lays out the types of problems and the issues behind them. There are always trade-offs in data cleaning and this book lays out those trade-offs better than any other I’ve seen. This is one of the few books that as I go through it, I struggle to think of anything that could have been said better. 

This book goes beyond the hype of data science, the details of machine learning methods, and the coding so closely associated with data science. Rather, it emphasizes the real types of problems for which data science may help, and explains the practical issues (“the real work”) that often lead to failure in data science projects.

These issues tend to be overlooked in more technical presentations of data science. They include such critical considerations as defining the right problem to begin with, understanding the “pedigree” (background and quality) of any data used, and ensuring that the right people are involved from the start.

A great way to see how AI is being used by companies and not just the future predictions of how AI could be used. Made me more open to how AI will change my industry (retail) and how people can use it to make better decisions. It kicked off my current journy to become more AI aware. As always, I appreciate anything that Thomas Davenport writes.

This book is now 50 years old, but its message is as relevant and important now as when it was written. In a series of witty essays that border on rants, Andreski attacks much of social science as fluff obscured by technical jargon and methodology. In particular, he laments the growth of quantitative methods as an attempt to add objectivity to social science and make it appear “harder.” True objectivity is about more than mechanical number-crunching, he says; it’s about a commitment to fairness and resisting the temptations of wishful thinking – a challenge anyone who works with data concerning people and their lives should take seriously.

People need less Dawkins in their lives and more Lewontin, whose thought-provoking, accessible writing about evolutionary biology stands in fierce opposition to the trend toward genetic determinism that seems to be the rage nowadays. We are not simply our genes, Lewontin says, because the effects DNA has on our lives are mediated by social and environmental factors, many of which we can influence. While it’s nominally about biology, I also read this as a critique of causal inference, generally. What we consider a “cause” reveals our ideological commitments to certain aspects of the world being maintained, and we should be careful what causal lessons we draw from data.

The thing you should know about science is that it’s a human enterprise. As a result, it’s dependent on human factors like social consensus and prejudice. In this series of case studies of famously expensive and difficult-to-replicate experiments probing the limits of scientific understanding from biology to theoretical physics, Collins and Pinch show how scientific knowledge gathering is rarely straightforward because there are always alternative explanations available for the data. Was the phenomenon real or was the experiment set up badly? We can never know for sure, but we decide collectively what we believe. Scientists are experts participating in human culture, they argue, not mysterious clergy issuing declarations of absolute truth.