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Data Analysis for Self-Publishers

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A Little Bit of Advice for Self-Publishers

For over a decade, publications consultant Vinnie Kinsella has been helping authors navigate the complex self-publishing landscape;

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Self-editing for Self-publishers

, three-stage process for authors looking to effectively self-edit their book, be it for self-publication or if they are looking to polish their;

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Self-editing for Self-publishers: Incorporating

, three-stage process for authors looking to effectively self-edit their book, be it for self-publication or if they are looking to polish their;

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Self-editing for Self-publishers: Incorporating

, three-stage process for authors looking to effectively self-edit their book, be it for self-publication or if they are looking to polish their;

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European Data Protection Regulation, Journalism, and Traditional Publishers

The tension between freedom of expression and European personal data protection regulation is unmistakable. Nowhere is this more apparent;

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Computational Data Analysis Techniques in Economics & Finance

The vast volume of financial data that exists and the globalisation of financial markets create new challenges for researchers and;

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Topological Data Analysis for Scientific Visualization

methods for the analysis and visualization of scientific data. Theoretical concepts are presented in a painstaking but intuitive manner, with;

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Topological Data Analysis for Scientific Visualization

methods for the analysis and visualization of scientific data. Theoretical concepts are presented in a painstaking but intuitive manner, with;

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Self-Esteem

Self-Esteem is een boek van Nova Science Publishers;

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Applied Categorical and Count Data Analysis

that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and;

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Tableau Prep Up and Running Self Service Data Preparation for Better Analysis

For self-service data preparation, Tableau Prep is relatively easy to use-as long as you know how to clean and organize your datasets. Carl;

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Signal Processing and Data Analysis

This book presents digital signal processing theories and methods and their applications in data analysis, error analysis and statistical;

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Analysis of Variance for Functional Data

, Analysis of Variance for Functional Data presents up-to-date hypothesis testing methods for functional data analysis. The book covers the;

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Handbook of Neuroimaging Data Analysis

This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development;

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Data Analysis and Applications 4

Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a;

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Data Analysis and Applications 3

areas. The material is divided into two parts: Computational Data Analysis, and Classification Data Analysis, with methods for both - providing;

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Learn Data Analysis with Python

needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python;

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Spatial Data Analysis in Ecology and Agriculture Using R

> Based on the authors spatial data analysis course at the University of California, Davis, the book is intended for classroom use or self;

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Data Analysis Using Stata 3rd Ed

logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an;

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Handbook of Neuroimaging Data Analysis

This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development;

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Environmental Data Analysis

data analysis procedures and techniques in a clear, concise, and straightforward language and is written in a self-contained way that;

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Hadoop BIG DATA

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Environmental Data Analysis with MatLab or Python

both be used as self-contained resources or as a step-by-step guide for students, and is supplemented with data and scripts to demonstrate;

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Unsupervised Learning

. Inspired by the relative success of existing popular research on self-organizing neural networks for data clustering and feature extraction;

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Multivariate Models and Multivariate Dependence Concepts

for modeling of binary, count, ordinal, and extreme value response data. It is virtually self-contained, and includes many exercises and;

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Data-Driven Computational Methods

leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that;

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