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‎_sources/books.rst.txt

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=====
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Books
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=====
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================
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Books And Videos
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================
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Jupyter for Data Science
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------------------------
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.. image:: _static/jupyter-for-ds.png
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:width: 200px
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:alt: Jupyter For dataScience
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:target: _static/jupyter-for-ds.png
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* `Jupyter for Data Science <https://www.packtpub.com/big-data-and-business-intelligence/jupyter-data-science>`_
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* By Dan Toomey
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* 242 pages
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* Packt Publishing
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* October 2017
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Jupyter Notebook is a web-based environment that enables interactive computing
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in notebook documents. It allows you to create documents that contain live code,
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equations, and visualizations. This book is a comprehensive guide to getting
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started with data science using the popular Jupyter notebook.
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If you are familiar with Jupyter notebook and want to learn how to use its
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capabilities to perform various data science tasks, this is the book for you!
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From data exploration to visualization, this book will take you through every
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step of the way in implementing an effective data science pipeline using
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Jupyter. You will also see how you can utilize Jupyter's features to share your
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documents and codes with your colleagues. The book also explains how Python 3,
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R, and Julia can be integrated with Jupyter for various data science tasks.
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By the end of this book, you will comfortably leverage the power of Jupyter to
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perform various tasks in data science successfully.
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Jupyter Notebook for All - Part II [Video]
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------------------------------------------
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.. image:: _static/jupyter-notebook-for-all-II.jpg
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:width: 200px
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:alt: Jupyter Notebook for All - Part II
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:target: _static/jupyter-notebook-for-all-II.jpg
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* `Jupyter Notebook for All - Part II <https://www.packtpub.com/big-data-and-business-intelligence/jupyter-notebook-all-%E2%80%93-part-ii-video>`_
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* By Dan Toomey
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* 1 hour and 14 minutes
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* Packt Publishing
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* March 2017
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Jupyter Notebook is a web-based environment that enables interactive computing
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in notebook documents. It allows you to create and share documents that contain
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live code, equations, visualizations, and explanatory text. The Jupyter Notebook
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system is extensively used in domains such as data cleaning and transformation,
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numerical simulation, statistical modeling, machine learning, and much more.
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This tutorial starts with a detailed overview of the Jupyter Notebook system and
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its installation in different environments. Next you will learn to integrate the
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Jupyter system with different programming languages such as R, Python,
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JavaScript, and Julia; further, you'll explore the various versions and packages
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that are compatible with the Notebook system. Moving ahead, you'll master
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interactive widgets, namespaces, and working with Jupyter in multiuser mode.
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Towards the end, you will use Jupyter with a big dataset and will apply all the
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functionalities learned throughout the video.
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Jupyter Notebook for All - Part I [Video]
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-----------------------------------------
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.. image:: _static/jupyter-notebook-for-all-I.jpg
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:width: 200px
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:alt: Jupyter Notebook for All - Part I
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:target: _static/jupyter-notebook-for-all-I.jpg
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* `Jupyter Notebook for All - Part I <https://www.packtpub.com/big-data-and-business-intelligence/jupyter-notebook-all-%E2%80%93-part-i-video>`_
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* By Dan Toomey
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* 1 hour 23 minutes
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* Packt Publishing
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* March 2017
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Jupyter Notebook is a web-based environment that enables interactive computing
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in notebook documents. It allows you to create and share documents that contain
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live code, equations, visualizations, and explanatory text. The Jupyter Notebook
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system is extensively used in domains such as data cleaning and transformation,
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numerical simulation, statistical modeling, machine learning, and much more.
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This tutorial starts with a detailed overview of the Jupyter Notebook system and
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its installation in different environments. Next you will learn to integrate the
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Jupyter system with different programming languages such as R, Python,
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JavaScript, and Julia; further, you'll explore the various versions and packages
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that are compatible with the Notebook system. Moving ahead, you'll master
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interactive widgets, namespaces, and working with Jupyter in multiuser mode.
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Towards the end, you will use Jupyter with a big dataset and will apply all the
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functionalities learned throughout the video.
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Learning Jupyter
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----------------

‎_static/basic.css

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‎_static/doctools.js

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‎_static/ipython.css

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/* Extra styles for the IPython website
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*/
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float:left;
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}
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#books-and-videos h2 {
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‎books.html

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ga('create', 'UA-38683231-1', 'auto');
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</script>
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<title>Books &#8212; IPython</title>
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<title>Books And Videos &#8212; IPython</title>
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<link rel="stylesheet" href="_static/agogo.css" type="text/css" />
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<link rel="stylesheet" href="_static/ipython.css" type="text/css" />
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"_static/ipython-cookbook.jpg",
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"_static/learning-jupyter-book.png",
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"_static/mastering-ipython-book.png",
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"_static/jupyter-notebook-for-all-I.jpg",
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"_static/jupyter-notebook-for-all-II.jpg",
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"_static/jupyter-for-ds.png",
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function pick(){
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<div class="bodywrapper">
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<div class="body" role="main">
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<div class="section" id="books">
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<h1>Books<a class="headerlink" href="#books" title="Permalink to this headline"></a></h1>
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<div class="section" id="books-and-videos">
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<h1>Books And Videos<a class="headerlink" href="#books-and-videos" title="Permalink to this headline"></a></h1>
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<div class="section" id="jupyter-for-data-science">
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<h2>Jupyter for Data Science<a class="headerlink" href="#jupyter-for-data-science" title="Permalink to this headline"></a></h2>
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<a class="reference external image-reference" href="_static/jupyter-for-ds.png"><img alt="Jupyter For dataScience" src="_static/jupyter-for-ds.png" style="width: 200px;" /></a>
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<ul class="simple">
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<li><a class="reference external" href="https://www.packtpub.com/big-data-and-business-intelligence/jupyter-data-science">Jupyter for Data Science</a></li>
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<li>By Dan Toomey</li>
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<li>242 pages</li>
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<li>Packt Publishing</li>
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<li>October 2017</li>
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</ul>
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<p>Jupyter Notebook is a web-based environment that enables interactive computing
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in notebook documents. It allows you to create documents that contain live code,
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equations, and visualizations. This book is a comprehensive guide to getting
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started with data science using the popular Jupyter notebook.</p>
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<p>If you are familiar with Jupyter notebook and want to learn how to use its
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capabilities to perform various data science tasks, this is the book for you!
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From data exploration to visualization, this book will take you through every
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step of the way in implementing an effective data science pipeline using
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Jupyter. You will also see how you can utilize Jupyter’s features to share your
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documents and codes with your colleagues. The book also explains how Python 3,
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R, and Julia can be integrated with Jupyter for various data science tasks.</p>
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<p>By the end of this book, you will comfortably leverage the power of Jupyter to
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perform various tasks in data science successfully.</p>
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</div>
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<div class="section" id="jupyter-notebook-for-all-part-ii-video">
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<h2>Jupyter Notebook for All - Part II [Video]<a class="headerlink" href="#jupyter-notebook-for-all-part-ii-video" title="Permalink to this headline"></a></h2>
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<a class="reference external image-reference" href="_static/jupyter-notebook-for-all-II.jpg"><img alt="Jupyter Notebook for All - Part II" src="_static/jupyter-notebook-for-all-II.jpg" style="width: 200px;" /></a>
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<ul class="simple">
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<li><a class="reference external" href="https://www.packtpub.com/big-data-and-business-intelligence/jupyter-notebook-all-%E2%80%93-part-ii-video">Jupyter Notebook for All - Part II</a></li>
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<li>By Dan Toomey</li>
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<li>1 hour and 14 minutes</li>
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<li>Packt Publishing</li>
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<li>March 2017</li>
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</ul>
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<p>Jupyter Notebook is a web-based environment that enables interactive computing
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in notebook documents. It allows you to create and share documents that contain
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live code, equations, visualizations, and explanatory text. The Jupyter Notebook
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system is extensively used in domains such as data cleaning and transformation,
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numerical simulation, statistical modeling, machine learning, and much more.
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This tutorial starts with a detailed overview of the Jupyter Notebook system and
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its installation in different environments. Next you will learn to integrate the
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Jupyter system with different programming languages such as R, Python,
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JavaScript, and Julia; further, you’ll explore the various versions and packages
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that are compatible with the Notebook system. Moving ahead, you’ll master
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interactive widgets, namespaces, and working with Jupyter in multiuser mode.
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Towards the end, you will use Jupyter with a big dataset and will apply all the
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functionalities learned throughout the video.</p>
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</div>
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<div class="section" id="jupyter-notebook-for-all-part-i-video">
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<h2>Jupyter Notebook for All - Part I [Video]<a class="headerlink" href="#jupyter-notebook-for-all-part-i-video" title="Permalink to this headline"></a></h2>
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<a class="reference external image-reference" href="_static/jupyter-notebook-for-all-I.jpg"><img alt="Jupyter Notebook for All - Part I" src="_static/jupyter-notebook-for-all-I.jpg" style="width: 200px;" /></a>
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<ul class="simple">
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<li><a class="reference external" href="https://www.packtpub.com/big-data-and-business-intelligence/jupyter-notebook-all-%E2%80%93-part-i-video">Jupyter Notebook for All - Part I</a></li>
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<li>By Dan Toomey</li>
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<li>1 hour 23 minutes</li>
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<li>Packt Publishing</li>
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<li>March 2017</li>
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</ul>
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<p>Jupyter Notebook is a web-based environment that enables interactive computing
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in notebook documents. It allows you to create and share documents that contain
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live code, equations, visualizations, and explanatory text. The Jupyter Notebook
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system is extensively used in domains such as data cleaning and transformation,
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numerical simulation, statistical modeling, machine learning, and much more.
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This tutorial starts with a detailed overview of the Jupyter Notebook system and
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its installation in different environments. Next you will learn to integrate the
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Jupyter system with different programming languages such as R, Python,
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JavaScript, and Julia; further, you’ll explore the various versions and packages
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that are compatible with the Notebook system. Moving ahead, you’ll master
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interactive widgets, namespaces, and working with Jupyter in multiuser mode.
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Towards the end, you will use Jupyter with a big dataset and will apply all the
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functionalities learned throughout the video.</p>
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</div>
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<div class="section" id="learning-jupyter">
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<h2>Learning Jupyter<a class="headerlink" href="#learning-jupyter" title="Permalink to this headline"></a></h2>
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&#169; Copyright the IPython development team.
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‎books_policy.html

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&#169; Copyright the IPython development team.
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.6.5.
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