Python for R Users: A Data Science Approach by Ajay Ohri
Requirements: PDF Reader, 12.6 MB
Overview: 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.
Genre: Computers & Technology > Programming> Python
Download Instructions:
https://douploads.net/8x7e1fsa2tk0
(Closed Filehost) https://cloudyfiles.org/86mmgwlcrjqz
Requirements: PDF Reader, 12.6 MB
Overview: 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.
Genre: Computers & Technology > Programming> Python
Download Instructions:
https://douploads.net/8x7e1fsa2tk0
(Closed Filehost) https://cloudyfiles.org/86mmgwlcrjqz