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Applied Data Science with python and jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data

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2018年

作者： Alex Galea

ISBN: (数字)9781789951929

ISBN: (纸本)9781789958171

Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key Features • Get up and running with the jupyter ecosystem and some example datasets • Learn about key machine learning concepts such as SVM, KNN classifiers, and Random Forests • Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Getting started with data science doesn't have to be an uphill battle. Applied Data Science with python and jupyter is a step-by-step guide ideal for beginners who know a little python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learn • Get up and running with the jupyter ecosystem • Identify potential areas of investigation and perform exploratory data analysis • Plan a machine learning classification strategy and train classification models • Use validation curves and dimensionality reduction to tune and enhance your models • Scrape tabular data from web pages and transform it into Pandas DataFrames • Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for Applied Data Science with python and jupyter is ideal for professionals with a variety of job descriptions ac

关键词： jupyter jupyter Notebook jupyter python Interactive Computing Interactive Computing Web Application jupyter Data Science jupyter configuration jupyter R jupyter visualization

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jupyter Cookbook 1

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丛书名： []

2018年

作者： Dan Toomey

ISBN: (数字)9781788839747

ISBN: (纸本)9781788839440

Leverage the power of the popular jupyter notebooks to simplify your data science tasks without any hassle Key Features Create and share interactive documents with live code, text and visualizations Integrate popular programming languages such as python, R, Julia, Scala with jupyter Develop your widgets and interactive dashboards with these innovative recipes Book Description jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as python, R, Julia, JavaScript, Scala, and Spark on your jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of jupyter to the next level to perform all key tasks associated with it. What you will learn Install jupyter and configure engines for python, R, Scala and more Access and retrieve data on jupyter Notebooks Create interactive visualizations and dashboards for different scenarios Convert and share your dynamic codes using HTML, JavaScript, Docker, and more Create custom user data interactions using various jupyter widgets Manage user authentication and file permissions Interact with Big Data to perform numerical computing and statistical modeling Get familiar with jupyter's next-gen user interface - jupyterLab W

关键词： jupyter jupyter Notebook jupyter python Interactive Computing Interactive Computing Web Application jupyter installation jupyter configuration jupyter R jupyter visualization

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