Comparative Approaches to Using R and Python for Statistical Data Analysis

Comparative Approaches to Using R and Python for Statistical Data Analysis

Noted as an IGI Global Core Reference Title in Computer Science & IT for 2019.

Rui Sarmento (University of Porto, Portugal) and Vera Costa (University of Porto, Portugal)
Indexed In: SCOPUS
Release Date: January, 2017|Copyright: © 2017 |Pages: 197
ISBN13: 9781683180166|ISBN10: 168318016X|EISBN13: 9781522519898|DOI: 10.4018/978-1-68318-016-6


The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users.

Comparative Approaches to Using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Clusters
  • Datasets
  • Descriptive Statistics
  • Factor Analysis
  • Linear Regression
  • Matrices
  • Programming Installation
  • Statistical Inference

Data Sets

Table of Contents and List of Contributors

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Author(s)/Editor(s) Biography

Rui Sarmento has a degree in Electrical Engineering in the Faculty of Engineering, University of Porto and a MSc in Data Analysis and Decision Support Systems in the Faculty of Economics of the University of Porto. He has worked in several areas from an international technical support center to software development companies focusing on Communications and Intranet solutions with Linux-based Enterprise Operating Systems. Finally, he has also worked for the main public transportation company in his hometown, Porto, as a Project Management engineer in the IT area. He is currently also collaborating with LIAAD (Laboratory of Artificial Intelligence and Decision Support) in INESC TEC researching on Large Social Networks Analysis and Visualization. Rui was previously published as a Contributing Author in the book Integration of Data Mining in Business Intelligence Systems (IGI Global, 2014).
Vera Costa has a degree in Mathematics at the Faculty of Sciences of the University of Porto and a Master Degree in Data Analysis and Decision Support Systems at the Faculty of Economics of the same university. She started to teach mathematics to high school and, after that, information systems to college students. She participated in several research projects, and she contributes with her knowledge of statistical analysis and software programming, in different application fields, such as health, politics, and transportation. Currently, she is a Ph.D. student of Transportation Systems of the MIT Portugal program.