Programming Skills For Data Science 2020 Edition at Meripustak

Programming Skills For Data Science 2020 Edition by Michael Freeman, PEARSON INDIA

Books from same Author: Michael Freeman

Books from same Publisher: PEARSON INDIA

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  • General Information  
    Author(s)Michael Freeman
    PublisherPEARSON INDIA
    Edition1st Edition
    Publish YearJanuary 2020


    PEARSON INDIA Programming Skills For Data Science 2020 Edition by Michael Freeman

    Programming Skills for Data Science brings together all the foundation skills needed to transform raw data into actionable insights for domains ranging from urban planning to precision medicine, even if you have no programming or data science experience. Guided by expert instructors Micheal Freeman and Joel Ross, this book will help learners install the tools required to solve professional-level data science problems, including widely used R language, RStudio integrated development environment, and Git version-control system. It explains how to wrangle data into a form where it can be easily used, analyzed, and visualized so others can see the patterns uncovered. Step by step, students will master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. Table of Content 1 Using the Command Line 2 Version Control with git and GitHub 3 Using Markdown for Documentation 4 Introduction to R 5 Functions in R 6 Vectors and Lists 7 Data and Data Frames 8 Manipulating Data with dplyr 9 Reshaping Data with tidyr 10 Accessing Databases and Web APIs 11 Designing Data Visualizations 12 Creating Visualizations with ggplot2 13 Interactive Visualization in R 14 Dynamic Reports with R Markdown 15 Building Interactive Web Applications with Shiny 16 Working Collaboratively" Salient Features 1. Guides students through setting up their computer for data science, understanding how the pieces fit together, and successfully using them to solve real problems 2. Introduces R, RStudio, git, GitHub, Markdown, Shiny, and other leading tools 3. Covers everything from preparing raw data to creating beautiful, sharable visualizations 4. Anticipates questions and demystifies complex ideas, reflecting the authors' experience introducing data science to thousands of students"