BookShared
  • MEMBER AREA    
  • Data Science (The MIT Press Essential Knowledge series)

    (By John D. Kelleher)

    Book Cover Watermark PDF Icon Read Ebook
    ×
    Size 29 MB (29,088 KB)
    Format PDF
    Downloaded 696 times
    Last checked 16 Hour ago!
    Author John D. Kelleher
    “Book Descriptions: 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.”

    Google Drive Logo DRIVE
    Book 1

    Artificial Intelligence: A Guide for Thinking Humans

    ★★★★★

    Melanie Mitchell

    Book 1

    Data Science For Dummies (For Dummies (Computer/Tech))

    ★★★★★

    Lillian Pierson

    Book 1

    Naked Statistics: Stripping the Dread from the Data

    ★★★★★

    Charles Wheelan

    Book 1

    Calling Bullshit: The Art of Skepticism in a Data-Driven World

    ★★★★★

    Carl T. Bergstrom

    Book 1

    Superforecasting: The Art and Science of Prediction

    ★★★★★

    Philip E. Tetlock

    Book 1

    Hands-On Machine Learning with Scikit-Learn and TensorFlow

    ★★★★★

    Aurélien Géron

    Book 1

    Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

    ★★★★★

    Cathy O'Neil

    Book 1

    The Deep Learning Revolution (Mit Press)

    ★★★★★

    Terrence J. Sejnowski

    Book 1

    The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy

    ★★★★★

    Sharon Bertsch McGrayne

    Book 1

    Life 3.0: Being Human in the Age of Artificial Intelligence

    ★★★★★

    Max Tegmark

    Book 1

    The Coming Wave: Technology, Power, and the Twenty-first Century's Greatest Dilemma

    ★★★★★

    Mustafa Suleyman

    Book 1

    Lean Impact: How to Innovate for Radically Greater Social Good

    ★★★★★

    Ann Mei Chang

    Book 1

    Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World

    ★★★★★

    Cade Metz

    Book 1

    The Structure of Scientific Revolutions

    ★★★★★

    Thomas S. Kuhn

    Book 1

    The Alignment Problem: Machine Learning and Human Values

    ★★★★★

    Brian Christian

    Book 1

    The Society of the Spectacle

    ★★★★★

    Guy Debord