Python for R Users

A Data Science Approach
 
 
John Wiley & Sons Inc (Verlag)
  • 1. Auflage
  • |
  • erschienen am 3. November 2017
  • |
  • 368 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-119-12677-5 (ISBN)
 

The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python

The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations-complete with sample code-of R to Python and Python to R.

Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining-including supervised and unsupervised data mining methods-are treated in detail, as are time series forecasting, text mining, and natural language processing.

• Features a quick-learning format with concise tutorials and actionable analytics

• Provides command-by-command translations of R to Python and vice versa

• Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages

• Offers numerous comparative examples and applications in both programming languages

• Designed for use for practitioners and students that know one language and want to learn the other

• Supplies slides useful for teaching and learning either software on a companion website

Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics.

A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.



A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing.

  • Englisch
  • Somerset
  • |
  • USA
  • Für Beruf und Forschung
  • 13,69 MB
978-1-119-12677-5 (9781119126775)
1119126770 (1119126770)
weitere Ausgaben werden ermittelt
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Acknowledgments
  • Scope
  • Purpose
  • Plan
  • The Zen of Python
  • Chapter 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
  • 1.6.1 Defining Analytics
  • 1.7 Tools Available to Data Scientists
  • 1.7.1 Guide to Data Science Cheat Sheets
  • 1.8 Packages in Python for Data Science
  • 1.9 Similarities and Differences between Python and R
  • 1.9.1 Why Should R Users Learn More about Python?
  • 1.9.2 Why Should Python Users Learn More about R?
  • 1.10 Tutorials
  • 1.11 Using R and Python Together
  • 1.11.1 Using R Code for Regression and Passing to Python
  • 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
  • 1.17.1 Commonly Used Linux Commands for Data Scientists
  • 1.17.2 Learning Git
  • 1.18 Data-Driven Decision Making: A Note
  • 1.18.1 Strategy Frameworks in Business Management: A Refresher for Non-MBAs and MBAs Who Have to Make Data-Driven Decisions
  • 1.18.2 Additional Frameworks for Business Analysis
  • Bibliography
  • Chapter 2 Data Input
  • 2.1 Data Input in Pandas
  • 2.2 Web Scraping Data Input
  • 2.2.1 Request Data from URL
  • 2.3 Data Input from RDBMS
  • 2.3.1 Windows Tutorial
  • 2.3.2 137 Mb Installer
  • 2.3.3 Configuring ODBC
  • Chapter 3 Data Inspection and Data Quality
  • 3.1 Data Formats
  • 3.1.1 Converting Strings to Date Time in Python
  • 3.1.2 Converting Data Frame to NumPy Arrays and Back in Python
  • 3.2 Data Quality
  • 3.3 Data Inspection
  • 3.3.1 Missing Value Treatment
  • 3.4 Data Selection
  • 3.4.1 Random Selection of Data
  • 3.4.2 Conditional Selection
  • 3.5 Data Inspection in R
  • 3.5.1 Diamond Dataset from ggplot2 Package in R
  • 3.5.2 Modifying Date Formats and Strings in R
  • 3.5.3 Managing Strings in R
  • Bibliography
  • Chapter 4 Exploratory Data Analysis
  • 4.1 Group by Analysis
  • 4.2 Numerical Data
  • 4.3 Categorical Data
  • Chapter 5 Statistical Modeling
  • 5.1 Concepts in Regression
  • 5.1.1 OLS
  • 5.1.2 R-Squared
  • 5.1.3 p-Value
  • 5.1.4 Outliers
  • 5.1.5 Multicollinearity and Heteroscedascity
  • 5.2 Correlation Is Not Causation
  • 5.2.1 A Note on Statistics for Data Scientists
  • 5.2.2 Measures of Central Tendency
  • 5.2.3 Measures of Dispersion
  • 5.2.4 Probability Distribution
  • 5.3 Linear Regression in R and Python
  • 5.4 Logistic Regression in R and Python
  • 5.4.1 Additional Concepts
  • 5.4.2 ROC Curve and AUC
  • 5.4.3 Bias Versus Variance
  • References
  • Chapter 6 Data Visualization
  • 6.1 Concepts on Data Visualization
  • 6.1.1 History of Data Visualization
  • 6.1.2 Anscombe Case Study
  • 6.1.3 Importing Packages
  • 6.1.4 Taking Means and Standard Deviations
  • 6.1.5 Conclusion
  • 6.1.6 Data Visualization
  • 6.1.7 Conclusion
  • 6.2 Tufteâ??s Work on Data Visualization
  • 6.3 Stephen Few on Dashboard Design
  • 6.3.1 Maeda on Design
  • 6.4 Basic Plots
  • 6.5 Advanced Plots
  • 6.6 Interactive Plots
  • 6.7 Spatial Analytics
  • 6.8 Data Visualization in R
  • 6.8.1 A Note of Sharing Your R Code by RStudio IDE
  • 6.8.2 A Note on Sharing Your Jupyter Notebook
  • Bibliography
  • Chapter 7 Machine Learning Made Easier
  • 7.1 Deleting Columns We Dont Need in the Final Decision Tree Model
  • 7.1.1 Decision Trees in R
  • 7.2 Time Series
  • 7.3 Association Analysis
  • 7.4 Cleaning Corpus and Making Bag of Words
  • 7.4.1 Cluster Analysis
  • 7.4.2 Cluster Analysis in Python
  • Chapter 8 Conclusion and Summary
  • Index

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