Data Science

The book has 7 chapters, five of which provide very basic introduction to Data Science. It discusses the definition of data science, data types, databases, machine learning, and data science tasks. The book has lots of texts and very few illustrative examples. It does not describe data processing, data presentation, analysis, and interpretation. In summary, the book is very, very basic and should not be titled as Data Science.

Buen libro. Directo al tema desde los primeros capítulos. Bien explicados. Intro que te sirve para entender más el tema y entrar a detalle luego de conocer el panorama completo

A small book covering essentials of the subject. Absolutely must for amateurs and beginners of Data Science.

CRISP- DM, Supervised Learning, Data Modelling, Linear Regression etc.., well versed concepts ~

very useful book would recommend

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.