book

Python for R Users

by Ajay Ohri

November 2017

Beginner to intermediate

368 pages

6h 21m

English

Wiley

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1.1 What Is Python?1.2 What Is R?1.3 What Is Data Science?1.4 The Future for Data Scientists1.5 What Is Big Data?1.6 Business Analytics Versus Data Science1.7 Tools Available to Data Scientists1.8 Packages in Python for Data Science1.9 Similarities and Differences between Python and R1.10 Tutorials1.11 Using R and Python Together1.12 Other Software and Python1.13 Using SAS with Jupyter1.14 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 R1.18 Data‐Driven Decision Making: A NoteBibliography
2.1 Data Input in Pandas2.2 Web Scraping Data Input2.3 Data Input from RDBMS

3.1 Data Formats3.2 Data Quality3.3 Data Inspection3.4 Data Selection3.5 Data Inspection in RBibliography
4.1 Group by Analysis4.2 Numerical Data4.3 Categorical Data
5.1 Concepts in Regression5.2 Correlation Is Not Causation5.3 Linear Regression in R and Python5.4 Logistic Regression in R and PythonReferences
6.1 Concepts on Data Visualization6.2 Tufte’s Work on Data Visualization6.3 Stephen Few on Dashboard Design6.4 Basic Plots6.5 Advanced Plots6.6 Interactive Plots6.7 Spatial Analytics6.8 Data Visualization in RBibliography
7.1 Deleting Columns We Dont Need in the Final Decision Tree Model7.2 Time Series7.3 Association Analysis7.4 Cleaning Corpus and Making Bag of Words

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Purpose

The book has been written from a practical use case perspective for helping people navigate multiple open‐source languages in the pursuit of data science excellence. The author believes that there is no one software or language that can solve all kinds of data problems all the time. An optimized approach to learning is better than an ideological approach to learning statistical software. Past habits of thinking must be confronted to enhance speed of future knowledge enhancement.