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Shaikh Salamatullah

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Regression Analysis with R

Dive into the world of regression analysis with this hands-on guide that covers everything you need to know about building effective regression models in R. You'll learn both the theoretical foundations and how to apply them using practical examples and R code. By the end, you'll be equipped to interpret regression results and use them to make meaningful predictions. What this Book will help me do Master the fundamentals of regression analysis, from simple linear to logistic regression. Gain expertise in R programming for implementing regression models and analyzing results. Develop skills in handling missing data, feature engineering, and exploratory data analysis. Understand how to identify, prevent, and address overfitting and underfitting issues in modeling. Apply regression techniques in real-world applications, including classification problems and advanced methods like Bagging and Boosting. Author(s) Giuseppe Ciaburro is an experienced data scientist and author with a passion for making complex technical topics accessible. With expertise in R programming and regression analysis, he has worked extensively in statistical modeling and data exploration. Giuseppe's writing combines clear explanations of theory with hands-on examples, ideal for learners and practitioners alike. Who is it for? This book is perfect for aspiring data scientists and analysts eager to understand and apply regression analysis using R. It's suited for readers with a foundational knowledge of statistics and basic R programming experience. Whether you're delving into data science or aiming to strengthen existing skills, this book offers practical insights to reach your goals.

Statistics for Data Science

Dive into the world of statistics specifically tailored for the needs of data science with 'Statistics for Data Science'. This book guides you from the fundamentals of statistical concepts to their practical application in data analysis, machine learning, and neural networks. Learn with clear explanations and practical R examples to fully grasp statistical methods for data-driven challenges. What this Book will help me do Understand foundational statistical concepts such as variance, standard deviation, and probability. Gain proficiency in using R for programmatically performing statistical computations for data science. Learn techniques for applying statistics in data cleaning, mining, and analysis tasks. Master methods for executing linear regression, regularization, and model assessment. Explore advanced techniques like boosting, SVMs, and neural network applications. Author(s) James D. Miller brings years of experience as a data scientist and educator. He has a deep understanding of how statistics foundationally supports data science and has worked across multiple industries applying these principles. Dedicated to teaching, James simplifies complex statistical concepts into approachable and actionable knowledge for developers aspiring to master data science applications. Who is it for? This book is intended for developers aiming to transition into the field of data science. If you have some basic programming knowledge and a desire to understand statistics essentials for data science applications, this book is designed for you. It's perfect for those who wish to apply statistical methods to practical tasks like data mining and analysis. A prior hands-on experience with R is helpful but not mandatory, as the book explains R methodologies comprehensively.