Author : Rui Sarmento
Publisher : Information Science Reference
Page : 0 pages
File Size : 14,84 MB
Release : 2017
Category : Mathematical statistics
ISBN : 9781683180166
Offers 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 designed for professionals, researchers, academics, graduate students, and practitioners.
Author : Rui|Vera
Publisher :
Page : pages
File Size : 11,89 MB
Release : 2017
Category :
ISBN : 9781522519904
Author : Sarmento, Rui
Publisher : IGI Global
Page : 215 pages
File Size : 37,44 MB
Release : 2017-01-06
Category : Business & Economics
ISBN : 1522519890
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.
Author : Ajay Ohri
Publisher : John Wiley & Sons
Page : 369 pages
File Size : 27,57 MB
Release : 2017-11-13
Category : Computers
ISBN : 1119126762
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.
Author : G. Sudhamathy, C. Jothi Venkateswaran
Publisher : MJP Publisher
Page : 383 pages
File Size : 10,60 MB
Release : 2019-06-03
Category : Computers
ISBN :
Chapter 1 - Basics of R, Chapter 2 - Data Types in R , Chapter 3 - Data Preparation. Chapter 4 - Graphics using R, Chapter 5 - Statistical Analysis Using R, Chapter 6 - Data Mining Using R, Chapter 7 - Case Studies. Huge volumes of data are being generated by many sources like commercial enterprises, scientific domains and general public daily. According to a recent research, data production will be 44 times greater in 2020 than it was in 2010. Data being a vital resource for business organizations and other domains like education, health, manufacturing etc., its management and analysis is becoming increasingly important. This data, due to its volume, variety and velocity, often referred to as Big Data, also includes highly unstructured data in the form of textual documents, web pages, graphical information and social media comments. Since Big Data is characterised by massive sample sizes, high dimensionality and intrinsic heterogeneity, traditional approaches to data management, visualisation and analytics are no longer satisfactorily applicable. There is therefore an urgent need for newer tools, better frameworks and workable methodologies for such data to be appropriately categorised, logically segmented, efficiently analysed and securely managed. This requirement has resulted in an emerging new discipline of Data Science that is now gaining much attention with researchers and practitioners in the field of Data Analytics.
Author : Khosrow-Pour D.B.A., Mehdi
Publisher : IGI Global
Page : 1966 pages
File Size : 10,21 MB
Release : 2020-07-24
Category : Computers
ISBN : 1799834808
The rise of intelligence and computation within technology has created an eruption of potential applications in numerous professional industries. Techniques such as data analysis, cloud computing, machine learning, and others have altered the traditional processes of various disciplines including healthcare, economics, transportation, and politics. Information technology in today’s world is beginning to uncover opportunities for experts in these fields that they are not yet aware of. The exposure of specific instances in which these devices are being implemented will assist other specialists in how to successfully utilize these transformative tools with the appropriate amount of discretion, safety, and awareness. Considering the level of diverse uses and practices throughout the globe, the fifth edition of the Encyclopedia of Information Science and Technology series continues the enduring legacy set forth by its predecessors as a premier reference that contributes the most cutting-edge concepts and methodologies to the research community. The Encyclopedia of Information Science and Technology, Fifth Edition is a three-volume set that includes 136 original and previously unpublished research chapters that present multidisciplinary research and expert insights into new methods and processes for understanding modern technological tools and their applications as well as emerging theories and ethical controversies surrounding the field of information science. Highlighting a wide range of topics such as natural language processing, decision support systems, and electronic government, this book offers strategies for implementing smart devices and analytics into various professional disciplines. The techniques discussed in this publication are ideal for IT professionals, developers, computer scientists, practitioners, managers, policymakers, engineers, data analysts, and programmers seeking to understand the latest developments within this field and who are looking to apply new tools and policies in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to software engineering, cybersecurity, information technology, media and communications, urban planning, computer science, healthcare, economics, environmental science, data management, and political science will benefit from the extensive knowledge compiled within this publication.
Author : Gareth James
Publisher : Springer Nature
Page : 617 pages
File Size : 43,4 MB
Release : 2023-08-01
Category : Mathematics
ISBN : 3031387473
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Author : Editor IJSMI
Publisher : International Journal of Statistics and Medical Informatics
Page : 101 pages
File Size : 49,11 MB
Release : 2019-11-15
Category : Computers
ISBN : 1708620281
Python programming language is an open source programming language which can be used under different operating system. Python programming redefined the programming concepts with its important features like flexibility, adaptability and reusability of codes. Python programming language has numerous libraries or modules which helps the programmer to save their time. The book starts with the overview of basic Python topics such as data structures, data types, conditions and controls, functions, lists, file handling and handling external datasets and database connections. The book also covers the topics in data science such as graphical and chart visualization, statistical modeling, text mining and machine learning algorithms. The book uses popular libraries of Python like matplotlib, sciket-learn and numpy, to perform graphical and machine learning related tasks. Users are encouraged to refer to the author’s book on “Machine Learning: An overview with the help of R software package” (ISBN- 978-1790122622) if they are familiar with R software package which is also an open source package The book requires users to download the Python version 3.0 and any of the Integrated Development Environments (IDE) such as Liclipse, Wing,PyCharm and Eric. Editor International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php https://www.amazon.com/dp/1708620281(Paper Back) https://www.amazon.com/DP/B081K1SD4K (e-Book)
Author : José Machado
Publisher : Springer Nature
Page : 327 pages
File Size : 28,24 MB
Release : 2022-06-17
Category : Technology & Engineering
ISBN : 3031093607
This book covers a variety of topics in the field of industrial engineering, with a special focus on research and industrial applications aimed at both improving quality of processes and products and contributing to a sustainable economy. Based on a set of papers presented at the 2nd International Conference “Innovation in Engineering”, ICIE, held in Minho, Portugal, on June 28–30, 2022, it focuses on innovative technologies associated with and strategies for the development of Industry 4.0. The chapters discuss new ways to improve industrial production and supply chain management by applying mathematical and computational methods. They also cover important issues relating to sustainability, education, and collaborations between industry and universities, and national developments. This book, which belongs to a three-volume set, provides engineering researchers and professionals with a timely overview and extensive information on trends and technologies behind the current and future developments of Industry 4.0.
Author : Bilan, Stepan
Publisher : IGI Global
Page : 313 pages
File Size : 29,15 MB
Release : 2017-10-31
Category : Computers
ISBN : 1522527745
Modern computing systems preserve all information in intricate binary codes. The evolution of systems and technologies that aid in this preservation process must be continually assessed to ensure that they are keeping up with the demands of society. Formation Methods, Models, and Hardware Implementation of Pseudorandom Number Generators: Emerging Research and Opportunities is a crucial scholarly resource that examines the current methodologies used in number generator construction, and how they pertain to the overall advancement of contemporary computer systems. Featuring coverage on relevant topics such as cellular automata theory, inhomogeneous cells, and sequence generators, this publication is ideal for software engineers, computer programmers, academicians, students, and researchers that are interested in staying abreast of innovative trends within the computer engineering field.
