How Data Happened: A History from the Age of Reason to the Age of Algorithms

Well researched and packed with useful information on how we got here and where we might be going. I found it thought provoking.

I bought this book on Kindle and then bought the hardcover so I could rabbit-ear it, see the references etc. This book is a wealth of information. Everybody talks about data--data is the new oil, privacy, surveillance, use of data for AI, etc.--but you need to understand the history as well. This book is THE book.

This book was not what, I expected and I enjoyed it greatly partly because of that. After heavy technical reading, this book was informative yet refreshing. It is divided into 3 sections, the first is the history of data science beginning with statistics, the second sections honed in on big data more specifically and is what I consider the “meat of the book” and the third section includes a a data ethics chapter and has actions that can be taken to challenge the status quo. All-in-all a great read and I recommend it to anyone interested in this kind of thing.

I have read this book three times, given how imporrtant "data" and their associated technologies are for our lives today. The authors use accessible language (e.g.,not all jargon) and offer much information and commentary, but I wish they focused more on critical techniques rather than go into painstaking details on what often seem to be so many peripheral issues or concerns.Are we living in an information regime that relies on masses of data - as quantifiable, accessible, manipulable, and fungible - rather than good science? If so, why? And what are its costs. They may agree, and believe that in these 300 pages they have made the case. I cannot be sure, and feel lost among the aforementiond "trees." For example, I want to know more about the role(s) and importance of statistics today in data-based decision-making - probabilities, etc.

This book was definitely illuminating for some historical synthesis on the evolution of statistical science. Going back a few hundred years and working forward with how statistics, pure mathematics, science, engineering, and "data science" evolved is probably a complex task beyond anyone today, but some insightful discussions are at play in this book. Ethical dilemmas are, of course, paramount toward the present headaches of juggling personal, corporate and government interests worldwide. It has led to a complex, and often peculiarly opaque state for understanding data science. I like the way the book was written to address important statistical and social aspects of data without burrowing into deep mathematics. This makes the book attractive to a broad audience. I recommend this book for getting a first rough perspective on a vastly important field: Statistical science (call it "data science" if you wish.) We all need to educate ourselves rapidly as this emerging field is probably only going to become more important in the near future and likely to be one of the "disruptive technologies" challenging many traditional organizations. A good multidisciplinary perspective as given in this book, in context of the extreme importance now of statistical science, is very welcome. Highly recommended to get you going on the "journey of 1000 miles."

I have just finished reading "How Data Happened" by Chris Wiggins and Matthew L. Jones. Highly recommend this book as it provides an insightful look into the history and implications of data and data-driven decision making in our data-centric world. One aspect I hadn't fully appreciated is the pivot in AI and machine learning from an initial focus on algorithms—which often led to AI winters due to limited data and computational resources—to the later utilization of "big data" and the subsequent surge in computational capacities that fuel today's AI advancements. The authors also portray the interplay of academia and corporations and the tensions and symmetries that have emerged. The most engaging discussions delve into the historical evolution of data's complex relationship with societal issues, focusing on bias, power, and equity.

As the saying goes, "data is the new oil”. It is one of the most important - and lucrative - commodities of the 21st century. So it is good to learn more about it - how it came to be, and the challenges it poses. This book, written by established data science experts, provides all the answers. It can be a bit dry, but it contains a lot of essential knowledge.Thanks to the publisher, W. W. Norton & Company, and NetGalley for an advanced copy of this book.

Escrita confusa, enrolada, com uso exagerado de citações literais e floreios estranhos. Argumentação circular, a tese não evolui. Transparece demais o fato de ser escrito por dois autores - o estilo de escrita vai e volta e, pra mim, isso atrapalha a fluidez da leitura. Enfim, sinto que faltou um bom editor.

I bought this book after hearing the author on a fascinating podcast, talking about the material presented in the book. Much to my surprise, the book is a turgid read. It is semi-academic in that it misses no opportunity to name sources, and one loses sight of the wood for the trees. Disappointing, since it is a fascinating subject, and I wished the authors had told the story in their own voice.