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John D. Kelleher

Brendan Tierney

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

by John D. Kelleher (Author), Brendan Tierney (Author)

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Part of: The MIT Press Essential Knowledge (94 books)

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A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges.

The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges.

It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

ISBN-10

0262535432
ISBN-13

978-0262535434
Edition

Illustrated
Publisher

The MIT Press
Publication date

April 13, 2018
Language

English
Dimensions

5 x 0.57 x 6.9 inches
Print length

280 pages
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Editorial Reviews

About the Author

John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at the Technological University Dublin. He has authored a number of books, including: Deep Learning, MIT Press, 2019, Data Science, MIT Press, 2018, and Fundamentals of Machine Learning for Predictive Data Analytics, MIT Press, 2015.

Product details

Publisher ‏ : ‎ The MIT Press; Illustrated edition (April 13, 2018)
Language ‏ : ‎ English
Paperback ‏ : ‎ 280 pages
ISBN-10 ‏ : ‎ 0262535432
ISBN-13 ‏ : ‎ 978-0262535434
Item Weight ‏ : ‎ 8.8 ounces
Dimensions ‏ : ‎ 5 x 0.57 x 6.9 inches

Best Sellers Rank: #99,443 in Books (See Top 100 in Books)
- #58 in Data Processing
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- #1,214 in Engineering (Books)

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John D. Kelleher
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John Kelleher is the Academic Leader of the Information, Communication and Entertainment research institute at the Technological University Dublin. His areas of expertise include artificial intelligence, data analytics and machine learning, natural language processing, spatial cognition, and text analytics. John has worked in a number of different academic and research focused institutes, including Dublin City University, Media Lab Europe, and DFKI (the German Centre for Artificial Intelligence Research).
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Brendan Tierney
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Brendan Tierney, Oracle ACE Director, is an independent consultant and lectures on Data Mining and Advanced Databases in the Dublin Institute of Technology in Ireland. He has 23+ years of extensive experience working in the areas of Data Mining, Data Warehousing, Data Architecture and Database Design. Brendan has worked on projects in Ireland, UK, across Europe, Canada and USA. Brendan is the editor of the UKOUG Oracle Scene magazine and deputy chair of the OUG Ireland BI SIG. Brendan is a regular speaker at conferences across Europe and the USA and has written technical articles for OTN, Oracle Scene, IOUG SELECT Journal and ODTUG Technical Journal.
twitter : @brendantierney
blog : www.oralytics.com
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Michael George

an excellent non-technical overview of data science

Reviewed in the United States 🇺🇸 on February 29, 2020

Verified Purchase

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.

12 people found this helpful

Helpful

Eric

An excellent intro into Data Science

Reviewed in the United States 🇺🇸 on August 21, 2022

Verified Purchase

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).

3 people found this helpful

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Brandon Fosdick

Good but very high-level and vague

Reviewed in the United States 🇺🇸 on October 15, 2021

Verified Purchase

For a basic introduction this book does fairly well, but it’s seriously lacking in details or specifics. If you’re completely new to data science then this might be for you. If you already have even the most basic understanding of data science then you can easily skip this book.

One person found this helpful

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Zach

Great intro

Reviewed in the United States 🇺🇸 on June 8, 2022

Verified Purchase

Great intro to the field. Not jargonny. High level clear explanations of some of the more complex topics in the field.

Reviewed in the United States 🇺🇸 on November 9, 2022

Verified Purchase

I had to get it for school. It is good if you are NEW, like literally day one in data science. Otherwise, it is just too basic/introductory.

One person found this helpful

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Nylon0Smile

Excellent book to get an overall picture

Reviewed in the United States 🇺🇸 on December 4, 2019

Verified Purchase

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.

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Cai

Must-read for anyone who wishes to enter data science

Reviewed in the United States 🇺🇸 on January 26, 2019

Verified Purchase

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.

2 people found this helpful

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Yogesh Kumar

good data science primer

Reviewed in the United States 🇺🇸 on May 6, 2019

Verified Purchase

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.

5 people found this helpful

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Top reviews from other countries

Shuaib

Concise overview of Data Science and its applications

Reviewed in the United Kingdom 🇬🇧 on January 27, 2020

Verified Purchase

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.

2 people found this helpful

AntMarakis

Interesting read, but offers nothing new

Reviewed in the United Kingdom 🇬🇧 on March 4, 2019

Verified Purchase

I am torn about this book. On one hand, it offers a nice overview of DS, both some technical aspects and the more "bigger picture"/social impact stuff. On the other hand, it rehashes a lot of stuff that shouldn't have to be covered in here. Namely, the machine learning chapter. A lot of effort has been put into this part, but unfortunately I don't think the section should be part of the book (at least it shouldn't be covered as extensively). I get that ML is a huge part of DS, but DS is more than that. This book fails to showcase this, and this mars the overall nice experience of reading this.

5 people found this helpful

Amazon Customer

Great overview of Data Science

Reviewed in the United Kingdom 🇬🇧 on November 4, 2019

Verified Purchase

I'm a PhD student without a background in computer science and i'm looking to go into Data Science. This book gave a pretty great overview of the area and includes info regarding ethics, most of which I had never thought to consider. Would defo recommend to the initial layman.

Carlos Gómez

Excellent intro to data science

Reviewed in the United Kingdom 🇬🇧 on November 14, 2018

Verified Purchase

Excellent book of data science, that not only covers algorithms, but also important topics as ethics. Great value for the price

Ryan Gadsby

Great insight

Reviewed in the United Kingdom 🇬🇧 on November 11, 2022

Verified Purchase

Good knowledge base and great for referencing