Data science from scratch: first principles with Python

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Description

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out.

  • Get a crash course in Python
  • Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
  • Collect, explore, clean, munge, and manipulate data
  • Dive into the fundamentals of machine learning
  • Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
  • Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
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    ISBN:
    9781491901427
    9781491904398
    149190142
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    Grouping Information

    Grouped Work ID 9c03fa02-967e-fe58-8b31-0754a87a269a
    Grouping Title data science from scratch first principles with python
    Grouping Author grus joel
    Grouping Category book
    Last Grouping Update 2018-07-20 22:45:09PM
    Last Indexed 2018-07-20 00:17:44AM

    Solr Details

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    author Joel Grus
    author_display Grus, Joel
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    display_description

    Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.

    If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out.

  • Get a crash course in Python
  • Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
  • Collect, explore, clean, munge, and manipulate data
  • Dive into the fundamentals of machine learning
  • Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
  • Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
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    primary_isbn 9781491901427
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    record_details ils:CARL0000481027|Book|Books|First edition.|English|O'Reilly,|[2015]|xvi, 311 pages : illustrations ; 24 cm., overdrive:72fae62a-b876-4be1-8202-446c18ba55fa|eBook|eBook||English|O'Reilly Media|2015|
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    subject_facet Data structures (Computer science), Database management, Python (Computer program language)
    title_display Data science from scratch : first principles with Python
    title_full Data Science from Scratch First Principles with Python, Data science from scratch : first principles with Python / Joel Grus
    title_short Data science from scratch :
    title_sub first principles with Python
    topic_facet Computer Technology, Data structures (Computer science), Database management, Nonfiction, Python (Computer program language)