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Notebook system is extensively used in domains such as data cleaning and Live code, equations, visualizations, and explanatory text. Towards the end, you will use Jupyter with a big dataset and will apply all theįunctionalities learned throughout the video. Interactive widgets, namespaces, and working with Jupyter in multiuser mode. That are compatible with the Notebook system. JavaScript, and Julia further, you’ll explore the various versions and packages Jupyter system with different programming languages such as R, Python, Its installation in different environments. This tutorial starts with a detailed overview of the Jupyter Notebook system and Numerical simulation, statistical modeling, machine learning, and much more. System is extensively used in domains such as data cleaning and transformation, It allows you to create and share documents that contain Perform various tasks in data science successfully. R, and Julia can be integrated with Jupyter for various data science tasks.īy the end of this book, you will comfortably leverage the power of Jupyter to You will also see how you can utilize Jupyter’s features to share yourĭocuments and codes with your colleagues. Step of the way in implementing an effective data science pipeline using
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If you are familiar with Jupyter notebook and want to learn how to use itsĬapabilities to perform various data science tasks, this is the book for you!įrom data exploration to visualization, this book will take you through every Started with data science using the popular Jupyter notebook. This book is a comprehensive guide to getting It allows you to create documents that contain live code,Įquations, and visualizations. Jupyter Notebook is a web-based environment that enables interactive computing The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.
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The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. Python is one of the leading open source platforms for data science and numerical computing.