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1.
Подробнее
R 88
Rühlemann, Christoph,.
Visual linguistics with R : : a practical introduction to quantitative Interactional Linguistics / / Christoph Rühlemann. - Amsterdam ; ; Philadelphia : : John Benjamins Publishing Company,, [2020]. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/A70032AD-791B-4726-8722-8A392DDA0977. - ISBN 9027260982. - ISBN 9789027260987 (electronic bk.)
Description based on print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Rühlemann, Christoph. Visual linguistics with R. - Amsterdam ; Philadelphia : John Benjamins Publishing Company, [2020]. - ISBN 9789027207098
Содержание:
Data management in R -- Graphical parameters -- Location plots -- Barplots -- Dotcharts -- Heatmaps and dendrograms -- Strip charts and violin plots -- Scatter plots -- Association plots and mosaic plots -- Box plots -- Histograms and density plots.
~РУБ DDC 410.2855133
Рубрики: Linguistics--Statistical methods.
Information visualization.
R (Computer program language)
Computational linguistics.
Grammar, Comparative and general--Syntax--Data processing.
Discourse analysis--Data processing.
Conversation analysis--Data processing.
Аннотация: "This book is a textbook on R, a programming language and environment for statistical analysis and visualization. Its primary aim is to introduce R as a research instrument in quantitative Interactional Linguistics. Focusing on visualization in R, the book presents original case studies on conversational talk-in-interaction based on corpus data and explains in good detail how key graphs in the case studies were programmed in R. It also includes task sections to enable readers to conduct their own research and compute their own visualizations in R. Both the code underlying the key graphs in the case studies and the datasets used in the case studies as well as in the task sections are made available on the book's companion website"--
R 88
Rühlemann, Christoph,.
Visual linguistics with R : : a practical introduction to quantitative Interactional Linguistics / / Christoph Rühlemann. - Amsterdam ; ; Philadelphia : : John Benjamins Publishing Company,, [2020]. - 1 online resource. - Includes bibliographical references and index. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/A70032AD-791B-4726-8722-8A392DDA0977. - ISBN 9027260982. - ISBN 9789027260987 (electronic bk.)
Description based on print version record and CIP data provided by publisher; resource not viewed.
Параллельные издания: Print version: : Rühlemann, Christoph. Visual linguistics with R. - Amsterdam ; Philadelphia : John Benjamins Publishing Company, [2020]. - ISBN 9789027207098
Содержание:
Data management in R -- Graphical parameters -- Location plots -- Barplots -- Dotcharts -- Heatmaps and dendrograms -- Strip charts and violin plots -- Scatter plots -- Association plots and mosaic plots -- Box plots -- Histograms and density plots.
Рубрики: Linguistics--Statistical methods.
Information visualization.
R (Computer program language)
Computational linguistics.
Grammar, Comparative and general--Syntax--Data processing.
Discourse analysis--Data processing.
Conversation analysis--Data processing.
Аннотация: "This book is a textbook on R, a programming language and environment for statistical analysis and visualization. Its primary aim is to introduce R as a research instrument in quantitative Interactional Linguistics. Focusing on visualization in R, the book presents original case studies on conversational talk-in-interaction based on corpus data and explains in good detail how key graphs in the case studies were programmed in R. It also includes task sections to enable readers to conduct their own research and compute their own visualizations in R. Both the code underlying the key graphs in the case studies and the datasets used in the case studies as well as in the task sections are made available on the book's companion website"--
2.
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G 83
Gries, Stefan Thomas, (1970-).
Statistics for linguistics with R : : a practical introduction / / by Stefan Th. Gries. - 3rd edition. - Berlin ; ; Boston : : De Gruyter, Inc.,, [2021]. - 1 online resource (512 p.). - (Mouton Textbook). - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/B1E7F910-5015-4842-9229-A7183D0B8C93. - ISBN 9783110718256 (electronic book). - ISBN 3110718251 (electronic book)
Description based on online resource; title from digital title page (viewed on May 25, 2021).
Параллельные издания: Print version: : Gries, Stefan Th. Statistics for Linguistics with R. - Berlin/Boston : De Gruyter, Inc.,c2009
Содержание:
1 Some fundamentals of empirical research -- 2 Fundamentals of R -- 3 Descriptive statistics -- 4 Monofactorial tests -- 5 Fixed-effects regression modeling -- 6 Mixed-effects regression modeling -- 7 Tree-based approaches
~РУБ DDC 410.285/5362 + DDC 401.2/1
Рубрики: Linguistics--Statistical methods.
R (Computer program language)
Linguistics--Statistical methods.
R (Computer program language)
G 83
Gries, Stefan Thomas, (1970-).
Statistics for linguistics with R : : a practical introduction / / by Stefan Th. Gries. - 3rd edition. - Berlin ; ; Boston : : De Gruyter, Inc.,, [2021]. - 1 online resource (512 p.). - (Mouton Textbook). - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/B1E7F910-5015-4842-9229-A7183D0B8C93. - ISBN 9783110718256 (electronic book). - ISBN 3110718251 (electronic book)
Description based on online resource; title from digital title page (viewed on May 25, 2021).
Параллельные издания: Print version: : Gries, Stefan Th. Statistics for Linguistics with R. - Berlin/Boston : De Gruyter, Inc.,c2009
Содержание:
1 Some fundamentals of empirical research -- 2 Fundamentals of R -- 3 Descriptive statistics -- 4 Monofactorial tests -- 5 Fixed-effects regression modeling -- 6 Mixed-effects regression modeling -- 7 Tree-based approaches
Рубрики: Linguistics--Statistical methods.
R (Computer program language)
Linguistics--Statistical methods.
R (Computer program language)
3.
Подробнее
L 58
Lesmeister, Cory,.
Mastering machine learning with R : : advanced machine learning techniques for building smart applications with R 3.5 / / Cory Lesmeister. - Third edition. - Birmingham, UK : : Packt Publishing,, 2019. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/AA03815B-BB70-4A6E-A3BC-C104CCC84CDE. - ISBN 9781789613568. - ISBN 1789613566
Previous edition published: 2017. Description based on online resource; title from title page (Safari, viewed March 25, 2019).
Параллельные издания: Print version: :
~РУБ DDC 519.502855133
Рубрики: Machine learning.
R (Computer program language)
Machine learning.
R (Computer program language)
MATHEMATICS / Applied
MATHEMATICS / Probability & Statistics / General
L 58
Lesmeister, Cory,.
Mastering machine learning with R : : advanced machine learning techniques for building smart applications with R 3.5 / / Cory Lesmeister. - Third edition. - Birmingham, UK : : Packt Publishing,, 2019. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/AA03815B-BB70-4A6E-A3BC-C104CCC84CDE. - ISBN 9781789613568. - ISBN 1789613566
Previous edition published: 2017. Description based on online resource; title from title page (Safari, viewed March 25, 2019).
Параллельные издания: Print version: :
Рубрики: Machine learning.
R (Computer program language)
Machine learning.
R (Computer program language)
MATHEMATICS / Applied
MATHEMATICS / Probability & Statistics / General
4.
Подробнее
M 14
MacLean, Dan.
R Bioinformatics Cookbook : : Use R and Bioconductor to Perform RNAseq, Genomics, Data Visualization, and Bioinformatic Analysis. / Dan. MacLean. - Birmingham : : Packt Publishing, Limited,, 2019. - 1 online resource (307 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/528E64B5-9F6F-4CA7-93AB-31DE7E0D982F. - ISBN 9781789955590 (electronic bk.). - ISBN 1789955599 (electronic bk.)
Print version record.
Параллельные издания: Print version: : MacLean, Dan. R Bioinformatics Cookbook : Use R and Bioconductor to Perform RNAseq, Genomics, Data Visualization, and Bioinformatic Analysis. - Birmingham : Packt Publishing, Limited, ©2019. - ISBN 9781789950694
Содержание:
R Bioinformatics Cookbook: Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis -- Contributors -- Table of Contents -- Preface -- 1. Performing Quantitative RNAseq -- 2. Finding Genetic Variants with HTS Data -- 3. Searching Genes and Proteins for Domains and Motifs -- 4. Phylogenetic Analysis and Visualization -- 5. Metagenomics -- 6. Proteomics from Spectrum to Annotation -- 7. Producing Publication and Web-Ready Visualizations -- 8. Working with Databases and Remote Data Sources -- 9. Useful Statistical and Machine Learning Methods -- 10. Programming with Tidyverse and Bioconductor -- 11. Building Objects and Packages for Code Reuse -- Other Books You May Enjoy -- Index.
Performing quantitative RNAseq -- Finding genetic variants with HTS data -- Searching genes and proteins for domains and motifs -- Phylogenetic analysis and visualization -- Metagenomics -- Proteomics from spectrum to annotation -- Producing publication and web-ready visualizations -- Working with databases and remote data sources -- Useful statistical and machine learning methods -- Programming with tidyverse and bioconductor -- Building objects and packages for code reuse.
~РУБ DDC 570.2855133
Рубрики: Bioinformatics.
R (Computer program language)
Computational biology.
Bio-informatique.
R (Langage de programmation)
Bioinformatics.
R (Computer program language)
Аннотация: In the R Bioinformatics Cookbook, you encounter common and not-so-common challenges in the bioinformatics domain and solve them using real-world examples. The book guides you through varied bioinformatics analysis, from raw data to clean results. It shows you how to import, explore and evaluate your data and how to report it.
M 14
MacLean, Dan.
R Bioinformatics Cookbook : : Use R and Bioconductor to Perform RNAseq, Genomics, Data Visualization, and Bioinformatic Analysis. / Dan. MacLean. - Birmingham : : Packt Publishing, Limited,, 2019. - 1 online resource (307 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/528E64B5-9F6F-4CA7-93AB-31DE7E0D982F. - ISBN 9781789955590 (electronic bk.). - ISBN 1789955599 (electronic bk.)
Print version record.
Параллельные издания: Print version: : MacLean, Dan. R Bioinformatics Cookbook : Use R and Bioconductor to Perform RNAseq, Genomics, Data Visualization, and Bioinformatic Analysis. - Birmingham : Packt Publishing, Limited, ©2019. - ISBN 9781789950694
Содержание:
R Bioinformatics Cookbook: Use R and Bioconductor to perform RNAseq, genomics, data visualization, and bioinformatic analysis -- Contributors -- Table of Contents -- Preface -- 1. Performing Quantitative RNAseq -- 2. Finding Genetic Variants with HTS Data -- 3. Searching Genes and Proteins for Domains and Motifs -- 4. Phylogenetic Analysis and Visualization -- 5. Metagenomics -- 6. Proteomics from Spectrum to Annotation -- 7. Producing Publication and Web-Ready Visualizations -- 8. Working with Databases and Remote Data Sources -- 9. Useful Statistical and Machine Learning Methods -- 10. Programming with Tidyverse and Bioconductor -- 11. Building Objects and Packages for Code Reuse -- Other Books You May Enjoy -- Index.
Performing quantitative RNAseq -- Finding genetic variants with HTS data -- Searching genes and proteins for domains and motifs -- Phylogenetic analysis and visualization -- Metagenomics -- Proteomics from spectrum to annotation -- Producing publication and web-ready visualizations -- Working with databases and remote data sources -- Useful statistical and machine learning methods -- Programming with tidyverse and bioconductor -- Building objects and packages for code reuse.
Рубрики: Bioinformatics.
R (Computer program language)
Computational biology.
Bio-informatique.
R (Langage de programmation)
Bioinformatics.
R (Computer program language)
Аннотация: In the R Bioinformatics Cookbook, you encounter common and not-so-common challenges in the bioinformatics domain and solve them using real-world examples. The book guides you through varied bioinformatics analysis, from raw data to clean results. It shows you how to import, explore and evaluate your data and how to report it.
5.
Подробнее
H 98
Hwang, Yoon Hyup,.
Hands-on data science for marketing : : improve your marketing strategies with machine learning using Python and R / / Yoon Hyup Hwang. - Birmingham, UK : : Packt Publishing,, 2019. - 1 online resource : : il. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/F02C8E37-4A0A-4335-8AC2-310A4F5C8124. - ISBN 178934882X. - ISBN 9781789348828 (electronic bk.)
Online resource; title from title page (Safari, viewed May 1, 2019).
Параллельные издания: Print version: : Hwang, Yoon Hyup. Hands-On Data Science for Marketing : Improve Your Marketing Strategies with Machine Learning Using Python and R. - Birmingham : Packt Publishing Ltd, ©2019. - ISBN 9781789346343
Содержание:
Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Introduction and Environment Setup; Chapter 1: Data Science and Marketing; Technical requirements; Trends in marketing; Applications of data science in marketing; Descriptive versus explanatory versus predictive analyses; Types of learning algorithms; Data science workflow; Setting up the Python environment; Installing the Anaconda distribution; A simple logistic regression model in Python; Setting up the R environment; Installing R and RStudio; A simple logistic regression model in R
Chapter 3: Drivers behind Marketing EngagementUsing regression analysis for explanatory analysis; Explanatory analysis and regression analysis; Logistic regression; Regression analysis with Python; Data analysis and visualizations; Engagement rate; Sales channels; Total claim amounts; Regression analysis; Continuous variables; Categorical variables; Combining continuous and categorical variables; Regression analysis with R; Data analysis and visualization; Engagement rate; Sales channels; Total claim amounts; Regression analysis; Continuous variables; Categorical variables
Combining continuous and categorical variablesSummary; Chapter 4: From Engagement to Conversion; Decision trees; Logistic regression versus decision trees; Growing decision trees; Decision trees and interpretations with Python; Data analysis and visualization; Conversion rate; Conversion rates by job; Default rates by conversions; Bank balances by conversions; Conversion rates by number of contacts; Encoding categorical variables; Encoding months; Encoding jobs; Encoding marital; Encoding the housing and loan variables; Building decision trees; Interpreting decision trees
Decision trees and interpretations with RData analysis and visualizations; Conversion rate; Conversion rates by job; Default rates by conversions; Bank balance by conversions; Conversion rates by number of contacts; Encoding categorical variables; Encoding the month; Encoding the job, housing, and marital variables; Building decision trees; Interpreting decision trees; Summary; Section 3: Product Visibility and Marketing; Chapter 5: Product Analytics; The importance of product analytics; Product analytics using Python; Time series trends; Repeat customers; Trending items over time
~РУБ DDC 658.834
Рубрики: Marketing--Data processing.
Machine learning.
Marketing research.
Python (Computer program language)
R (Computer program language)
Machine learning.
Marketing--Data processing.
Marketing research.
Python (Computer program language)
R (Computer program language)
Аннотация: Section 2: Descriptive Versus Explanatory Analysis; Chapter 2: Key Performance Indicators and Visualizations; KPIs to measure performances of different marketing efforts; Sales revenue; Cost per acquisition (CPA); Digital marketing KPIs; Computing and visualizing KPIs using Python; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Computing and visualizing KPIs using R; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Summary
This book will be an excellent resource for both Python and R developers and will help them apply data science and machine learning to marketing with real-world data sets. By the end of this book, you will be well equipped with the required knowledge and expertise to draw insights from data and improve your marketing strategies.
H 98
Hwang, Yoon Hyup,.
Hands-on data science for marketing : : improve your marketing strategies with machine learning using Python and R / / Yoon Hyup Hwang. - Birmingham, UK : : Packt Publishing,, 2019. - 1 online resource : : il. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/F02C8E37-4A0A-4335-8AC2-310A4F5C8124. - ISBN 178934882X. - ISBN 9781789348828 (electronic bk.)
Online resource; title from title page (Safari, viewed May 1, 2019).
Параллельные издания: Print version: : Hwang, Yoon Hyup. Hands-On Data Science for Marketing : Improve Your Marketing Strategies with Machine Learning Using Python and R. - Birmingham : Packt Publishing Ltd, ©2019. - ISBN 9781789346343
Содержание:
Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Introduction and Environment Setup; Chapter 1: Data Science and Marketing; Technical requirements; Trends in marketing; Applications of data science in marketing; Descriptive versus explanatory versus predictive analyses; Types of learning algorithms; Data science workflow; Setting up the Python environment; Installing the Anaconda distribution; A simple logistic regression model in Python; Setting up the R environment; Installing R and RStudio; A simple logistic regression model in R
Chapter 3: Drivers behind Marketing EngagementUsing regression analysis for explanatory analysis; Explanatory analysis and regression analysis; Logistic regression; Regression analysis with Python; Data analysis and visualizations; Engagement rate; Sales channels; Total claim amounts; Regression analysis; Continuous variables; Categorical variables; Combining continuous and categorical variables; Regression analysis with R; Data analysis and visualization; Engagement rate; Sales channels; Total claim amounts; Regression analysis; Continuous variables; Categorical variables
Combining continuous and categorical variablesSummary; Chapter 4: From Engagement to Conversion; Decision trees; Logistic regression versus decision trees; Growing decision trees; Decision trees and interpretations with Python; Data analysis and visualization; Conversion rate; Conversion rates by job; Default rates by conversions; Bank balances by conversions; Conversion rates by number of contacts; Encoding categorical variables; Encoding months; Encoding jobs; Encoding marital; Encoding the housing and loan variables; Building decision trees; Interpreting decision trees
Decision trees and interpretations with RData analysis and visualizations; Conversion rate; Conversion rates by job; Default rates by conversions; Bank balance by conversions; Conversion rates by number of contacts; Encoding categorical variables; Encoding the month; Encoding the job, housing, and marital variables; Building decision trees; Interpreting decision trees; Summary; Section 3: Product Visibility and Marketing; Chapter 5: Product Analytics; The importance of product analytics; Product analytics using Python; Time series trends; Repeat customers; Trending items over time
Рубрики: Marketing--Data processing.
Machine learning.
Marketing research.
Python (Computer program language)
R (Computer program language)
Machine learning.
Marketing--Data processing.
Marketing research.
Python (Computer program language)
R (Computer program language)
Аннотация: Section 2: Descriptive Versus Explanatory Analysis; Chapter 2: Key Performance Indicators and Visualizations; KPIs to measure performances of different marketing efforts; Sales revenue; Cost per acquisition (CPA); Digital marketing KPIs; Computing and visualizing KPIs using Python; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Computing and visualizing KPIs using R; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Summary
This book will be an excellent resource for both Python and R developers and will help them apply data science and machine learning to marketing with real-world data sets. By the end of this book, you will be well equipped with the required knowledge and expertise to draw insights from data and improve your marketing strategies.
Страница 1, Результатов: 5