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Data Science (The MIT Press Essential Knowledge series) Kindle Edition
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.
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About the Author
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.
Chris Sorensen is a veteran audiobook narrator with over 160 titles to his name. He has received three AudioFile Earphones Awards, and his recording of Sent by Margaret Peterson Haddix was selected as one of the Best Audiobooks of 2010 by AudioFile magazine. He is a member of SAG-AFTRA and the APA. --This text refers to an out of print or unavailable edition of this title.
- ASIN : B08BT6M8JR
- Publisher : The MIT Press (April 13, 2018)
- Publication date : April 13, 2018
- Language : English
- File size : 1420 KB
- Text-to-Speech : Enabled
- Screen Reader : Supported
- Enhanced typesetting : Enabled
- X-Ray : Enabled
- Word Wise : Not Enabled
- Print length : 282 pages
- Lending : Not Enabled
- Best Sellers Rank: #172,564 in Kindle Store (See Top 100 in Kindle Store)
- Customer Reviews:
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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.