Data Science By Author’s Name

Os autores se propuseram a apresentar e discutir os fundamentos da ciência de dados.
Para tanto, ao longo do texto aprsentam as principais definições da área, além de discutirem a questão da privacidade dos dados e ética na aquisição/uso dos dados.

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.

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

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.

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.

I found this book to be an excellent way of familiarising myself with Data Science, coming from a non-Computer Science (Economics) background.

It covers the recent literature on such computational methods from, the current applications and the challenges behind Data Science. The book also talks about the various types of data along with the use cases like nominal/ordinal (categorical) and numeric data. Eventually, getting to what I think is the best chapter in the book is ‘Machine Learning 101’, which easily explains the types of what’s the difference between supervised learning (classification/regression problems) and unsupervised learning (clustering, segmentation etc.). Only Maths (Algebra/statistics) up to high school/college level is needed to understand the principles of how most of the algorithms are set-up.
The only thing I think this book was disappointing at was the explanation of Deep Learning, which I feel was slightly brushed over compared to Machine Learning, when in some way, Deep Learning may have deserved its own chapter.
Finally, the book ended on the legislation side of Data Ethics, such as GDPR and the trade-off between accurate analysis and privacy among users of the internet/digital applications, again illustrating the future path for Data Science.

I would recommend this book as a handy Data Science reference.