Python for R Users: A Data Science Approach at Meripustak

Python for R Users: A Data Science Approach

Books from same Author: A. Ohri

Books from same Publisher: Wiley India

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  • General Information  
    Author(s)A. Ohri
    PublisherWiley India
    Publish YearNovember 2018


    Wiley India Python for R Users: A Data Science Approach by A. Ohri

    This book is the first of its kind to provide a reference that enables students and practitioners to easily learn to code in Python if they are familiar with R and vice versa, even if they are beginners in the second language. It also provides a detailed introduction and overview of each language to the reader who might be unfamiliar with the other. While R has better statistical and graphical tools, Python has good machine learning tools and proves to be more useful software for the analysis of Big Data. A unique feature of this book is how it provides a command-by-command translation between R and Python for many mathematical, visualization and machine learning techniques. The intended audience is statistical practitioners and data scientists trying to learn one of R or Python or both, as well as students that are familiar with one of the languages. About the Author Ajay Ohri is the founder of analytics startup He has pursued graduate courses at the University of Tennessee, Knoxville and completed a Masters from Indian Institute of Management. Ohri also has a mechanical engineering degree from the Delhi College of Engineering. Ohri's current research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces to cloud computing, investigating climate change and knowledge flows. TABLE OF CONTENTS Preface Acknowledgments Scope Purpose Plan The Zen of Python 1 Introduction to Python R and Data Science 1.1 What Is Python? 1.2 What Is R? 1.3 What Is Data Science? 1.4 The Future for Data Scientists 1.5 What Is Big Data? 1.6 Business Analytics Versus Data Science 1.7 Tools Available to Data Scientists 1.8 Packages in Python for Data Science 1.9 Similarities and Differences between Python and R 1.10 Tutorials 1.11 Using R and Python Together 1.12 Other Software and Python 1.13 Using SAS with Jupyter 1.14 How Can You Use Python and R for Big Data Analytics? 1.15 What Is Cloud Computing? 1.16 How Can You Use Python and R on the Cloud? 1.17 Commercial Enterprise and Alternative Versions of Python and R 1.18 Data? Driven Decision Making: A Note 2 Data Input 2.1 Data Input in Pandas 2.2 Web Scraping Data Input 2.3 Data Input from RDBMS 3 Data Inspection and Data Quality 3.1 Data Formats 3.2 Data Quality 3.3 Data Inspection 3.4 Data Selection 3.5 Data Inspection in R 4 Exploratory Data Analysis 4.1 Group by Analysis 4.2 Numerical Data 4.3 Categorical Data 5 Statistical Modeling 5.1 Concepts in Regression 5.2 Correlation Is Not Causation 5.3 Linear Regression in R and Python 5.4 Logistic Regression in R and Python 6 Data Visualization 6.1 Concepts on Data Visualization 6.2 Tufte's Work on Data Visualization 6.3 Stephen Few on Dashboard Design 6.4 Basic Plots 6.5 Advanced Plots 6.6 Interactive Plots 6.7 Spatial Analytics 6.8 Data Visualization in R 7 Machine Learning Made Easier 7.1 Deleting Columns We Dont Need in the Final Decision Tree Model 7.2 Time Series 7.3 Association Analysis 7.4 Cleaning Corpus and Making Bag of Words 8 Conclusion and Summary Index