Mohsen Nady – författare

The data science field has evolved so much recently with incredible quantities of generated data. To extract value from those data, one needs to be trained in the proper data science skills. This book covers some introductory steps in using R programming language as a data science tool.

This book covers some introductory steps in biostatistics using R programming language. Biostatistics is the branch of statistics that applies statistical methods to medical and biological problems. Biostatistics has become more important recently for studying the great amount of data that is produced from census data, genome sequencing, gene expression data, medical bioinformatics, and medical imaging data. With the help of R programming, statistical analysis, data cleaning, data visualization, and machine learning has become a relatively easy tasks for these huge datasets. R is now considered the centerpiece language for doing all these data science skills because it has many useful packages that not only can perform all these tasks, but also, has additional packages that were specifically designed for several statistical tasks related to biology and medical data. In addition, many scientific journals require the data analysis R scripts to ensure reproducibility of the submitted results. The first chapter of this book introduces many statistical concepts used in scientific research like study designs, sample, and population, and data types. Chapters 2, 4, and 5 cover the three main data types which are continuous data, categorical data, and time to event data. Chapter 3 discusses the popular continuous distribution that is the normal distribution along with its application to sample data. Chapter 6 is about the sampling distribution of different sample estimates along with a discussion of the famous central limit theorem (CLT). Chapters 7 and 8 are involved in confidence interval (CI) calculations, and Chapters 9–11 discuss several types of statistical tests like t-test, ANOVA, Chi-square, log-rank, etc. Finally, Chapters 12–14 deal with different regression types; linear regression for continuous outcomes, logistic regression for binary outcomes, and Cox regression for time to event outcomes. In all these chapters, many examples from many scientific journal articles or built in data sets along with different codes and outputs are given to help your understanding of these numerous statistical concepts. I hope this book will be a great addition to your future biostatistical projects.

Covers the use of R programming language for data visualization. The large real-world datasets can be quickly visualized to gain several insights from it using R. Whether the data is already clean or needs some preliminary steps before data visualization like data cleaning and wrangling, all these can be done using R.

This book introduces readers to the fundamental principles of statistics while leveraging R, a powerful programming language for data analysis. It covers essential topics such as data visualization, hypothesis testing, regression, and machine learning, equipping readers with the skills to analyze and interpret data effectively. With practical examples and hands-on exercises, the book is suitable for beginners and intermediate learners looking to enhance their statistical analysis capabilities in academic and professional settings.

Understanding data is foundational for effective marketing strategies. This volume introduces descriptive statistics and hypothesis testing using R, with a focus on practical applications in marketing. Topics include data visualization, summary statistics, and common statistical tests. Guide readers in applying statistical tools to marketing problems. It is a must-read for marketing professionals and students beginning their journey in data-driven decision-making.

Linear models and cluster analysis are powerful tools for uncovering relationships and segmenting markets. This volume explores their application in marketing, including regression analysis, customer segmentation, and predictive modeling. Practical examples and R code demonstrate how to use these techniques effectively. It is an essential resource for marketing analysts and professionals seeking to deepen their understanding of statistical methods.

Advanced statistical techniques like hierarchical clustering and Cox regression can reveal hidden insights in marketing data. This volume explores their application in areas such as customer lifetime value analysis and market segmentation. Readers will learn how to implement these methods using R, with step-by-step instructions and practical examples. The volume is a valuable guide for marketing professionals and researchers looking to enhance their analytical capabilities.

Understanding patterns over time and associations between products is crucial for modern marketing. This volume covers repeated measures models for time-series analysis and market basket analysis for cross-selling opportunities. Using R, readers will gain hands-on experience with these advanced techniques through practical examples and case studies. The volume is an indispensable resource for marketers aiming to leverage statistical tools for strategic decision-making.

Data preparation is the foundation of any successful machine learning project. This volume provides a comprehensive guide to cleaning, transforming, and splitting data for machine learning using R, including handling missing values, feature scaling, and stratified sampling. Practical examples and R code demonstrate how to optimize datasets for predictive modeling. The volume is essential for data scientists and machine learning practitioners seeking to build robust models.

Resampling techniques are key to improving model performance and reliability in machine learning. This volume explores advanced resampling methods, including cross-validation, bootstrapping, and hyperparameter tuning, using R. Readers will learn how to apply these techniques to optimize model accuracy and prevent overfitting. Practical examples and case studies illustrate their real-world applications. This volume is an essential resource for data scientists and machine learning enthusiasts aiming to master resampling strategies.

Before building predictive models, raw data must be cleaned, transformed, and prepared—a process known as data preprocessing. Effective preprocessing enhances the accuracy and reliability of machine learning algorithms. Statistics with R for Machine Learning: Data Preprocessing for Machine Learning using R provides an in-depth guide to statistical tools and techniques essential for preparing data. The book explains data normalization, missing value imputation, outlier detection, and feature engineering using R programming. It also introduces visualization tools and statistical validation methods. Practical examples and R scripts make it an ideal reference for students and data professionals.