Non-Technical Data Science Books You MUST Read
There’s nothing better than a good book, is there? Here are our top 5 recommended non-technical data science books. Perfect for data scientists wanting to expand their knowledge, and take a read of a great book!
This book is a New York Times bestseller and the Economist book of the year for a reason. Seth Stephens-Davidowitz takes a deep look into how the data from our internet searches reveal insights so personal about the human psyche as a whole that they’ve not been uncovered before. Not just a book about big data, but a study on the human mind that uncovers interesting, funny and at times uncomfortable truths.
Sharon McGrayne takes a look at the history of Bayes’ rule and how it went from being a controversial mathematical theorem considered taboo for over 100 years, to being one of the most used today. She delves into the many ways in which Bayes’ theorem contributed to historical events, such as famously Alan Turing’s work in cracking the codes that helped shorten WW2.
Through case studies, Virginia Eubanks takes us on a journey on the way in which automated decision making is impacting the lives of the most vulnerable in our society. She looks at homelessness, child protection and welfare in America in an engaging but disturbing read. For those interested in algorithmic bias, whilst it does lack some insight into the more positive changes we can make from a technological perspective.
An examination of the way algorithms influences our behaviours and decisions, shaping our lives and what this means as a whole. An interesting look at the problems and pitfalls of algorithmic bias, how they work and are implemented through the pairing of real-life stories to explain it accessibly for people at all levels of knowledge.
This international bestseller offers a comprehensive look at human rationale – how we make choices through the faster (more intuitive) way of thinking, and slower (more rational) thinking. Kahneman’s book will certainly get you thinking and re-evaluating how you make decisions in the future. A great read for anyone working in data science. Tuning in to smarter thinking and decision making can make a huge impact in and out of work.
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