Key Features
<ul>
<li>Explore R’s key features and discover why it was built for data science</li>
<li>From data mining to analysis and visualization - learn each step in the data workflow</li>
<li>Get started with predictive analytics with R's powerful machine learning packages</li>
</ul>Book DescriptionMake sure you’re ready to tackle Big Data with R for Data Science. Created to help you harness R and its extensive range of scientific tools for a wide range of data analysis tasks, this book covers the fundamental elements of data science from data mining to data analysis to visualization. If you want to learn the latest cutting-edge techniques being used by data scientists, and to experience the extensive features and power of R, follow each step in this vital collection of data science tutorials.
The book is broken down into four sections – data mining, data analysis and data visualization and machine learning, ensuring that you gain insights into the core components of data science. Learn different data mining patterns and sequences; get to grips with the latest in text mining, and then explore a range of approaches to data analysis including clustering, regression analysis and correlation. Find out how to draw insights from data with data visualization techniques and tools, to bring meaning to your data, and learn how to use R for machine learning and predictive analytics.
Better data science can transform the world – start learning how R can help you make data more meaningful with R for Data Science.What you will learn<ul>
<li>Develop, execute, and modify R scripts</li>
<li>Learn how to use different data mining sequences</li>
<li>Find out how to organize your data effectively</li>
<li>Produce high-quality data visualizations</li>
<li>Get to grips with a number of approaches to the statistical analysis of data</li>
<li>Learn how to cultivate a strategic approach to your data to use the right tools, models and visualizations to get the job done</li>
</ul>About the AuthorDan Toomey has been developing applications for over 20 years. He has worked in a variety of industries and companies in different roles, from a sole contributor to VP and CTO. For the last 10 years or so, he has been working with companies in the eastern Massachusetts area.Table of Contents<ol>
<li>Data Mining Patterns</li>
<li>Data Mining Sequences</li>
<li>Text Mining</li>
<li>Data Analysis - Regression Analysis</li>
<li>Data Analysis - Correlation</li>
<li>Data Analysis - Clustering</li>
<li>Data Visualization - R Graphics</li>
<li>Data Visualization - Plotting</li>
<li>Data Visualization - 3D</li>
<li>Machine Learning in Action</li>
<li>Predicting Events with Machine Learning</li>
<li>Supervised and Unsupervised Learning</li>
</ol>

R for Data Science

Computers & Technology

Toomey, Dan

Programming

Packt Pub Ltd

Key FeaturesLearn to write, execute, and comment your live code and formulae all under one roof using this unique guideThis one-stop solution on Project Jupyter will teach you everything you need to know to perform scientific computation with easeThis easy-to-follow, highly practical guide lets you forget your worries in scientific application development by leveraging big data tools such as Apache Spark, Python, R etcBook DescriptionJupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more.This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we’ll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode.Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.What you will learnInstall and run the Jupyter Notebook system on your machineImplement programming languages such as R, Python, Julia, and JavaScript with Jupyter NotebookUse interactive widgets to manipulate and visualize data in real timeStart sharing your Notebook with colleaguesInvite your colleagues to work with you in the same NotebookOrganize your Notebook using Jupyter namespacesAccess big data in JupyterAbout the AuthorDan Toomey has been developing applications for over 20 years. He has worked in a variety of industries and size companies in roles from sole contributor to VP/CTO level. For the last 10 years or so, he has been contracting to companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Again, as a contractor developer in the area. Dan has also written R for Data Sciences with Packt Publishing.Table of ContentsIntroduction to JupyterJupyter Python ScriptingJupyter R ScriptingJupyter Julia ScriptingJupyter JavaScript CodingInteractive WidgetsSharing and Converting Jupyter NotebooksMultiuser Jupyter NotebooksJupyter ScalaJupyter and Big Data

Learning Jupyter

Networking & Cloud Computing

Packt Publishing

Create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with JupyterKey FeaturesLearn how to use Jupyter 5.x features such as cell tagging and attractive table stylesLeverage big data tools and datasets with different Python packagesExplore multiple-user Jupyter Notebook serversBook DescriptionThe Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples.The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode.By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization.What you will learnInstall and run the Jupyter Notebook system on your machineImplement programming languages such as R, Python, Julia, and JavaScript with the Jupyter NotebookUse interactive widgets to manipulate and visualize data in real timeStart sharing your Notebook with colleaguesInvite your colleagues to work with you on the same NotebookOrganize your Notebook using Jupyter namespacesAccess big data in Jupyter for dealing with large datasets using SparkWho this book is forLearning Jupyter 5 is for developers, data scientists, machine learning users, and anyone working on data analysis or data science projects across different teams. Data science professionals will also find this book useful for performing technical and scientific computing collaboratively.Table of ContentsIntroduction to JupyterJupyter Python ScriptingJupyter R ScriptingJupyter Julia Scripting Jupyter Java Coding Jupyter JavaScript Coding Jupyter Scala Jupyter and Big Data Interactive Widgets Sharing and Coverting Jupyter NotebookMultiuser Jupyter Notebook What’s Next?

Learning Jupyter 5, Second Edition

Get to grips with the basics of JupyterLab and its web interface with the help of this quick start guide Key Features <ul>
<li>Manage JupyterLab kernels, code consoles, and terminals, and share your work over the cloud </li> <li>Organize your data solutions within JupyterLab </li> <li>Install and configure extensions on JupyterLab using easy-to-follow examples</li>
</ul> Book Description JupyterLab is a web-based interface and the natural evolution of Jupyter Notebook. This guide will take you through the core commands and functionalities of JupyterLab and help you enhance your JupyterLab productivity. Starting with the installation of JupyterLab, this book will give you an overview of its features and the variety of problems it solves. You'll see how you can work with external files inside the platform, and understand how to use the code console and terminal. This will help you have distinct control over the scripts you work with. As you progress, you'll get to grips with the extensions available in JupyterLab, and gain insights into adding extensions to introduce new functionality in the interface. This book also covers widget operations within your document, different design patterns in JupyterLab, and the various methods for exchanging Notebooks. Additionally, you'll explore how to host JupyterLab Notebooks on GitHub. By the end of this Jupyter book, you'll have become well-versed with all the components of JupyterLab and be able to use it collaboratively within your data science teams. What you will learn <ul>
<li>Install JupyterLab and work with Jupyter Notebooks </li> <li>Host JupyterLab Notebooks on GitHub and access GitHub resources in your Notebooks </li> <li>Explore different methods for exchanging Notebooks </li> <li>Discover ways in which multiple users can access the same Notebook </li> <li>Publish your Notebooks with nbviewer and convert them into different formats </li> <li>Attach and operate widgets within your Notebooks using a JupyterLab document </li> <li>Use JupyterLab to work collaboratively with multiple data scientists in your teams</li>
</ul> Who this book is for This book is for data scientists and data analysts who are new to JupyterLab as well as for existing Jupyter users who want to get acquainted with its impressive features. Although not necessary, basic knowledge of Python will be helpful.Table of Contents <ol>
<li>Introducing JupyterLab</li> <li>Exploring the Jupyterlab Interface</li> <li>Managing and Building Extensions</li> <li>Data Exploration within JupyterLab</li> <li>Sharing and Presenting your work</li> <li>Using Jupyterlab with Teams</li>
</ol>

JupyterLab Quick Start Guide

Richman, Lindsay

Ferrari, Melissa

Oladokun, Joseph

Banfield, Wesley

Databases & Big Data

Ave Verum Corpus

Arts & Photography

Mozart, W. A.

Music

Cherry Classics Music

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