2020 has been an interesting year. Not for the obvious topic, but for new books on Data Science and Machine Learning. The list below are some of my favorite books from 2020. Making the selection was difficult. Some months had a large number of releases and some were a bit quieter. The books below are listed based on their release date and are not ranked in any way. I’ve included links to these books on Amazon (.com, .uk and .de).
January
Everyone wants to work in Data Science, but where and how do you start. Aimed at beginners with guidance without the technical. High level, not for everyone.
With by Thomas Davenport and others, and is a good addition to his other books. Consisting of interviews, research and analysis on how to win with ML & AI.
ML & AI is not perfect. Lots can go wrong. Not just with the project but also with the implementation of the applications. Lots to thing about and consider.
No one really builds ML algorithms. We build ML solutions and applications. But whats the best way to do this, from technical, organizational and ethical aspects.
And to finish off the list I’m including this additional book. It wasn’t released this year. It was released in April 2018. It was a best seller on Amazon in 2018 and 2019! This was really exciting for us and we still amazed at how it it is still selling in 2020. It is currently, as of December 2020, listed in 8th place on the MIT Press Best Sellers list. It won’t be making any best seller list in 2020, but is still proving popular with many readers. To all of you who have bought this book, I’d like to say Thank You and wishing you all the best with 2021 and beyond.