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In my opinion, The Elements of Data Analytic Style is one of the most under-appreciated books on data science and analysis. It is a very easily understandable book on the dos and don'ts of data analysis.
This book is for: People who are starting out on their journey in data science, and perfect if you are looking for an overview of data science, and especially interested in telling stories through data.
This book is not for: Experts in data science, since some of the points mentioned might appear obvious. If you are looking for a deep discussion on data analysis, then you might be slightly disappointed.
Either way, if you are looking for a succinct read on the entire data pipeline, then this book fits the bill nicely. And the best part is, you can get the book for free on Leanpub.
A Machine Learning expert has theoretical knowledge on his fingertips, but far more valuable is his 'practical' knowledge gained over years of experience. Newbies falter on the road to ML because a lot of this 'practical knowledge' that is needed to successfully develop machine learning applications is not readily available in them. As a result, many machine learning projects take much longer than necessary or wind up producing less-than-ideal results.
This article was written by one of the pioneers in AI, Pedro Domingos, in order to democratize some of the practical aspects of ML and communicate it to a general audience. If you are a traveller on the data science journey, this is definitely something you must not miss reading. And a lot of the points made are not present in any textbooks.