TY  - BOOK
AU  -  Scavetta, Rick J. & Angelov,  Boyan
TI  - Python and R for the modern data scientist : the Best of both worlds
SN  - 9789391043681
U1  - 006.312 
PY  - 2021///
CY  - Mumbai
PB  - Shroof Publishers and Distributors
KW  -  	      Data mining.     Python (Computer program language)     R (Computer program language)
N1  - Book description

Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set.

Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist.

    Learn Python and R from the perspective of your current language
    Understand the strengths and weaknesses of each language
    Identify use cases where one language is better suited than the other
    Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows
    Learn how to integrate R and Python in a single workflow
    Follow a case study that demonstrates ways to use these languages together; 
    Preface
        Why We Wrote This Book
        Technical Interactions
        Who This Book Is For
        Prerequisites
        How This Book Is Organized
        Let’s Talk
        Conventions Used in This Book
        Using Code Examples
        O’Reilly Online Learning
        How to Contact Us
        Acknowledgments
    I. Discovery of a New Language
    1. In the Beginning
        The Origins of R
        The Origins of Python
        The Language War Begins
        The Battle for Data Science Dominance
        A Convergence on Cooperation and Community-Building
        Final Thoughts
    II. Bilingualism I: Learning a New Language
    2. R for Pythonistas
        Up and Running with R
        Projects and Packages
        The Triumph of Tibbles
        A Word About Types and Exploring
        Naming (Internal) Things
        Lists
        The Facts About Factors
        How to Find…Stuff
        Reiterations Redo
        Final Thoughts
    3. Python for UseRs
        Versions and Builds
        Standard Tooling
        Virtual Environments
        Installing Packages
        Notebooks
        How Does Python, the Language, Compare to R?
            Import a Dataset
            Examine the Data
        Data Structures and Descriptive Statistics
            Data Structures: Back to the Basics
            Indexing and Logical Expressions
            Plotting
        Inferential Statistics
        Final Thoughts
    III. Bilingualism II: The Modern Context
    4. Data Format Context
        External Versus Base Packages
        Image Data
        Text Data
        Time Series Data
            Base R
            Prophet
        Spatial Data
        Final Thoughts
    5. Workflow Context
        Defining Workflows
        Exploratory Data Analysis
            Static Visualizations
            Interactive Visualizations
        Machine Learning
        Data Engineering
        Reporting
            Static Reporting
            Interactive Reporting
        Final Thoughts
    IV. Bilingualism III: Becoming Synergistic
    6. Using the Two Languages Synergistically
        Faux Operability
        Interoperability
        Going Deeper
            Pass Objects Between R and Python in an R Markdown Document
            Call Python in an R Markdown Document
            Call Python by Sourcing a Python Script
            Call Python Using the REPL
            Call Python with Dynamic Input in an Interactive Document
        Final Thoughts
    7. A Case Study in Bilingual Data Science
        24 Years and 1.88 Million Wildfires
        Setup and Importing Data
        EDA and Data Visualization
        Machine Learning
            Setting Up Our Python Environment
            Feature Engineering
            Model Training
        Prediction and UI
        Final Thoughts
    A. A Python:R Bilingual Dictionary
        Package Management
        Assign Operators
        Types
        Arithmetic Operators
        Attributes
        Keywords
        Functions and Methods
        Style and Naming Conventions
        Analogous Data Storage Objects
        Data Frames
        Logical Expressions
        Indexing
    Index

N2  - Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. --
ER  -