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Correspondence Analysis and Data Coding with Java and R

new fields of application emerging, its significance is greater than ever. Correspondence Analysis and Data Coding with Java and R clearly;

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Correspondence Analysis and Data Coding with Java and R

new fields of application emerging, its significance is greater than ever. Correspondence Analysis and Data Coding with Java and R clearly;

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Java Data Analysis

for data analysis with Java and make sense of your data through effective visualizations. * Use various Java APIs and tools such as;

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Java Coding Guidelines

vulnerabilities. This book provides realistic guidance to help Java developers implement desired functionality with security, reliability, and;

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An Introduction to Correspondence Analysis

researchers in improving their familiarity with the concepts, terminology, and application of several variants of correspondence analysis. The;

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Doing Meta-Analysis with R

* Contains two introductory chapters on how to set up an R environment and do basic imports/manipulation of meta-analysis data, including;

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Analyzing Qualitative Data

This book offers students and researchers a hands-on guide to the practicalities of coding, comparing data, and using computer-assisted;

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Java Coding Problems

Develop your coding skills by exploring Java concepts and techniques such as Strings, Objects and Types, Data Structures and Algorithms;

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Textual Data Science with R

package in R. Methods discussed include correspondence analysis, clustering, and multiple factor analysis for contigency tables. Each method;

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Textual Data Science with R

package in R. Methods discussed include correspondence analysis, clustering, and multiple factor analysis for contigency tables. Each method;

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Programming Java: Java Programming, JavaScript, Coding: Programming Guide

master this skill. You'll find out the basics of Java, including Object Oriented Programming, Variable Declaration and Data Types, Control;

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Java

tricks, step by step, programming for beginners, data analysis, beginners guide, crash course, database programming, java for dummies, coding;

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Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment;

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Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment;

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The The Complete Coding Interview Guide in Java

into manageable chunks Key Features Discover over 200 coding interview problems and their solutions to help you secure a job as a Java;

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The The Java Workshop

in databases using JDBC Make HTTP requests from Java applications and parse the response data Secure your data with cryptography and encryption;

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Programming JAVA: JavaScript, Coding: Programming Guide

master this skill. You'll find out the basics of Java, including Object Oriented Programming, Variable Declaration and Data Types, Control;

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Java

just have a bit of experience in coding or you have worked in coding for a long time and want to learn how to work with Java, this guidebook;

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R 4 Data Science Quick Reference

, magnittr, dplyr, purrr, ggplot2, modelr, and more. With R 4 Data Science Quick Reference, you'll have the code, APIs, and insights to write data;

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An Introduction to R for Spatial Analysis and Mapping

the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS;

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Quantitative Methods in Archaeology Using R

for archaeologists. It shows how to use the system to analyze many types of archaeological data. Part I includes tutorials on R, with;

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Cambridge Manuals in Archaeology

for archaeologists. It shows how to use the system to analyze many types of archaeological data. Part I includes tutorials on R, with;

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R 4 Data Science Quick Reference

, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. With R 4 Data Science Quick Reference, you'll have the code, APIs, and insights;

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R Data Science Quick Reference

modelr Who This Book Is For Programmers new to R's data science, data mining, and analytics packages. Some prior coding experience with R;

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Joy of Kotlin, The

* Programming with functions * Dealing with optional data * Safe handling of errors and exceptions Audience Written for;

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Java

available in Java * Work with the Java APIs and techniques used to perform efficient data analysis * Find out how to approach different machine;

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Exploratory Multivariate Analysis by Example Using R

components analysis, correspondence analysis, mixed models and multiple factor analysis. The authors take a practical approach, with examples leading;

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