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Data Science

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Data Science

De: John D. Kelleher, Brendan Tierney
Narrado por: Chris Sorensen
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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.

©2018 Massachusetts Institute of Technology (P)2018 Gildan Media Ingeniería Aprendizaje automático Ciencia de datos Ciencias de la computación Inteligencia artificial
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This is an excellent primer on the concepts, current state, and implications of data science.

Comprehensive and clear

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not great, not horrible. as much as you would expect from a small book on a broad subject

it was ok

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There's probably two hours of material and the ethics/philosophy parts are too absolute for a book about data science. As for the material, most of the application side is common sense, but it was nice to hear it.

Comforting

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This is a great source for Data Science Professionals. The concept are explained clearly and from the standpoint of early industry entry.

A must have resource

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good overview/introduction; clear and concise- BUT the narration is atrocious. the reader has great diction and speaks with precision, but he clearly doesn't know much about the subject matter so for about the first 80% of the book he tries to make up for it by careening all over the place with his pitch, stress, juncture, and inflection. his stressing of arbitrary and inappropriate words and syllables takes fairly dense content and adds a layer of complexity that's baffling and unnecessary. to be fair, I'm sure he's a good narrator when it comes to content that he understands.
there we're also some problems with the audio editing in the first third of the book- paragraphs and chapters sometimes start in the middle of a word or sentence, so when you add that to his bizarre pitch modulation, it can be pretty disorienting and leave you wondering if there was something important that was left out.

performance is borderline unlistenable

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This book reads like a reference book on data science. Good foundation for understanding data science (i.e., data set, machine learning, clustering, correlation/causation, big data, privacy, and trends).

Reference Book

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This was required reading in my CS degree, so I was thankful to find it on Audible for ease of reading. It's a short introduction to data science and, although a little sandpaper dry, was generally informative.

HOWEVER, the intonation, nasal vocal inflections and accent of the narrator makes me want to scrape my face across a bed of nails! It's even worse than early 1990s Michael Kramer! Its also monotonous, disinterested, and repetitive! If this wasn't required reading, I'd return it. As such, I'll just bear it and take LOTS of breaks.

Publisher, if you're reading this, for the love of God, re-do this narration!

that narrator.....aghhhhh!!

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