Data Science (The MIT Press Essential Knowledge series) Illustrated Edition

Data science is put in excellent perspective in this book. I think the book is especially oriented toward giving people interested in "specializing" in this field or utilizing data science some good, basic information. As a multidisciplinary field, and one oriented toward business, government and surveillance interests, generally, it is a field that encompasses and extends into practical areas that its associated traditional area, namely statistics, has not in the past much-addressed. Data science is an extremely interesting, technical field with broad social and ethical implications explored in this book. Statistics is only one tool. The authors lucidly discuss the focus on the huge amounts of valuable, unstructured data. They point out that to make all of this useful for the goals and purposes of business, surveillance, medicine, government, etc. requires an enormous time investment in putting appropriate data together and extracting information in a usable form. The discussion of mathematical modeling, machine learning, and the overall use of algorithms is very insightful. The authors make it clear that data science is not merely "deep learning", despite the fact that the extraordinary advances in using neural nets represented by deep learning is largely responsible for much of the importance of data science today. There are excellent perspectives of data science available on the Internet, but I think the authors of this book have provided a good supplement for this information in a deeper way. One of the real problems in picking information out from the Internet is escaping the "hype" surrounding a subject that is currently "hot" like data science. This book definitely allows the interested person to separate some of the solid pieces of knowledge about what the field involves from the huge amount of "noise" surrounding the entire area of "weak" AI and machine learning. I would recommend this book strongly to anyone seriously considering going into this field. A point the authors stress is that weak AI, namely specialized applications, rather than broadly "intelligent" systems competitive with general human intelligence, has opened up a world of opportunity, promise, progress, as well as ethical dilemmas. I personally think that data science is a great field for an enormous spectrum of technicians at all educational levels. The book opens a window a bit on the enormous implications for our future. It is a good start on the climb to a satisfactory knowledge of this field and its potential. I especially recommend the book to business executives and entrepreneurs as a useful and insightful view, for developing a strategic picture of this field, that does not get into unnecessarily technical details, and is not subject to the "hype" and "noise" from the Internet.

This book covers core concepts in data science in an easy to read manner. Infrastructure for handling big data and the data science ecosystem are introduced along with Machine Learning basics and some useful concepts at a high level(like CRISP-DM, clustering, anomaly detection etc.). A chapter on the privacy and ethics covers GDPR and biases in algorithms. Overall, a good general introduction.

The authors do an excellent job of giving a very high level overview of the following for Data Science:-History-Applications (Prediction, clustering, anomaly detection)-Tools of Data Science (Bayes Rule, Logistic Regression, Neural Networks, Decision Trees)-Ethical concerns (Where do we cross the line between privacy, security and applications of the Data Science?)-Growth of Data Science (I wish the authors would've shared how to get into the career field more. Since applying association rule here, anyone that reads the book is likely to be interested in Data Science).

Well-written and easy-to-understand, this book gives a new-comer like me a conceptual framework to think about problems in data science. It helps me to understand what the field really is and what the workflow of a data science project looks like. Particularly interesting is the chapter on data ethics and regulation. I think it is an area that is often overlooked by technical textbook, but should really be emphasized to readers who might someday become a data practitioner. Overall, it’s a very good book and worths your effort to delve into.

The last chapter was really good. wish authors provided more insights into successful data science projects. The rest of the book was very generic information.

Good introductory book for data science. Use it for a lot of my college courses for the last couple years.

Easy to read with accessible and clear examples throughout. Well organised with an easy to follow structure throughout which is well tied together at the end

I am a relatively experienced programmer and have been involved in all parts of Data Science projects (ranging from leading big data processing parts to being an observant in ML parts also actively working with business to understand problems, justify investments and calculate ROIs for multiple proposed ML solutions) and come from a mostly practical background. This book provided a very good overall picture as well as a lot of good references to dig further into.