Statistical Application Development With R And Python

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Statistical Application Development With R And Python

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Author : Prabhanjan Narayanachar Tattar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-08-31

Statistical Application Development With R And Python written by Prabhanjan Narayanachar Tattar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-31 with Computers categories.

Software Implementation Illustrated with R and Python About This Book Learn the nature of data through software which takes the preliminary concepts right away using R and Python. Understand data modeling and visualization to perform efficient statistical analysis with this guide. Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics. Who This Book Is For If you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versa What You Will Learn Learn the nature of data through software with preliminary concepts right away in R Read data from various sources and export the R output to other software Perform effective data visualization with the nature of variables and rich alternative options Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference Learn statistical inference through simulation combining the classical inference and modern computational power Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics Introduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearity In Detail Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects. Style and approach Developing better and smarter ways to analyze data. Making better decisions/future predictions. Learn how to explore, visualize and perform statistical analysis. Better and efficient statistical and computational methods. Perform practical examples to master your learning

Statistical Application Development With R And Python Second Edition

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Author : Prabhanjan Narayanachar Tattar
language : en
Publisher:
Release Date : 2017-08-30

Statistical Application Development With R And Python Second Edition written by Prabhanjan Narayanachar Tattar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-30 with Computers categories.

Software Implementation Illustrated with R and PythonAbout This Book* Learn the nature of data through software which takes the preliminary concepts right away using R and Python.* Understand data modeling and visualization to perform efficient statistical analysis with this guide.* Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics.Who This Book Is ForIf you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versaWhat You Will Learn* Learn the nature of data through software with preliminary concepts right away in R* Read data from various sources and export the R output to other software* Perform effective data visualization with the nature of variables and rich alternative options* Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference* Learn statistical inference through simulation combining the classical inference and modern computational power* Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics* Introduce yourself to CART - a machine learning tool which is very useful when the data has an intrinsic nonlinearityIn DetailStatistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions.This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world.You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python.The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics.By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.Style and approachDeveloping better and smarter ways to analyze data. Making better decisions/future predictions. Learn how to explore, visualize and perform statistical analysis. Better and efficient statistical and computational methods. Perform practical examples to master your learning

Hands On Ensemble Learning With R

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Author : Prabhanjan Narayanachar Tattar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-07-27

Hands On Ensemble Learning With R written by Prabhanjan Narayanachar Tattar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-27 with Computers categories.

Explore powerful R packages to create predictive models using ensemble methods Key Features Implement machine learning algorithms to build ensemble-efficient models Explore powerful R packages to create predictive models using ensemble methods Learn to build ensemble models on large datasets using a practical approach Book Description Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples. What you will learn Carry out an essential review of re-sampling methods, bootstrap, and jackknife Explore the key ensemble methods: bagging, random forests, and boosting Use multiple algorithms to make strong predictive models Enjoy a comprehensive treatment of boosting methods Supplement methods with statistical tests, such as ROC Walk through data structures in classification, regression, survival, and time series data Use the supplied R code to implement ensemble methods Learn stacking method to combine heterogeneous machine learning models Who this book is for This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.

Mastering Data Analysis With R

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Author : Gergely Daroczi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-09-30

Mastering Data Analysis With R written by Gergely Daroczi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-30 with Computers categories.

Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.

Survival Analysis

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Author : Prabhanjan Narayanachar Tattar
language : en
Publisher: CRC Press
Release Date : 2022-08-26

Survival Analysis written by Prabhanjan Narayanachar Tattar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-26 with Computers categories.

Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way. Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis. Features: Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model, Aalen’s additive hazards model, etc. Information criteria to facilitate model selection including Akaike, Bayes, and Focused Penalized methods Survival trees and ensemble techniques of bagging, boosting, and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book

Machine Learning With R

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Author : Brett Lantz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-07-31

Machine Learning With R written by Brett Lantz and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-31 with Computers categories.

Updated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.

Mastering Scientific Computing With R

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Author : Paul Gerrard
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-01-31

Mastering Scientific Computing With R written by Paul Gerrard and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-31 with Computers categories.

If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.

R Programming For Analytics

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Author : Dr.G.S.Thakur
language : en
Publisher: Sankalp Publication
Release Date :

R Programming For Analytics written by Dr.G.S.Thakur and has been published by Sankalp Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on with Education categories.

Handbook Of Statistical Analysis

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Author : Robert Nisbet
language : en
Publisher: Elsevier
Release Date : 2024-09-16

Handbook Of Statistical Analysis written by Robert Nisbet and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-16 with Mathematics categories.

Handbook of Statistical Analysis: AI and ML Applications, third edition, is a comprehensive introduction to all stages of data analysis, data preparation, model building, and model evaluation. This valuable resource is useful to students and professionals across a variety of fields and settings: business analysts, scientists, engineers, and researchers in academia and industry. General descriptions of algorithms together with case studies help readers understand technical and business problems, weigh the strengths and weaknesses of modern data analysis algorithms, and employ the right analytical methods for practical application. This resource is an ideal guide for users who want to address massive and complex datasets with many standard analytical approaches and be able to evaluate analyses and solutions objectively. It includes clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques; offers accessible tutorials; and discusses their application to real-world problems. - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data analytics to build successful predictive analytic solutions - Provides in-depth descriptions and directions for performing many data preparation operations necessary to generate data sets in the proper form and format for submission to modeling algorithms - Features clear, intuitive explanations of standard analytical tools and techniques and their practical applications - Provides a number of case studies to guide practitioners in the design of analytical applications to solve real-world problems in their data domain - Offers valuable tutorials on the book webpage with step-by-step instructions on how to use suggested tools to build models - Provides predictive insights into the rapidly expanding "Intelligence Age" as it takes over from the "Information Age," enabling readers to easily transition the book's content into the tools of the future

International Encyclopedia Of Statistical Science

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Author : Miodrag Lovric
language : en
Publisher: Springer Nature
Release Date : 2025-06-19

International Encyclopedia Of Statistical Science written by Miodrag Lovric and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-19 with Mathematics categories.

The International Encyclopedia of Statistical Science stands as a monumental effort to enrich statistics education globally, particularly in regions facing educational challenges. By amalgamating the expertise of over 700 authors from 110 countries, including Nobel Laureates and presidents of statistical societies, it offers an unparalleled resource for readers worldwide. This encyclopedia is not just a collection of entries; it is a concerted effort to revive statistics as a vibrant, critical field of study and application. Providing a comprehensive and accessible account of statistical terms, methods, and applications, it enables readers to gain a quick insight into the subject, regardless of their background. This work serves to refresh and expand the knowledge of researchers, managers, and practitioners, highlighting the relevance and applicability of statistics across various fields, from economics and business to healthcare and public policy. Furthermore, it aims to inspire students by demonstrating the significance of statistics in solving real-world problems, thus encouraging a new generation to explore and contribute to the field.