Primer for Data Analytics and Graduate Study in Statistics

This book is a comprehensive guide to probability and distributional theory, covering advanced undergraduate topics and preparing students for graduate-level statistics programs. It focuses on growing fields such as data analytics, statistics, and biostatistics, providing a transitional bridge between undergraduate and graduate studies.

Format: Paperback / softback
Length: 233 pages
Publication date: 28 September 2021
Publisher: Springer Nature Switzerland AG

This book is meticulously crafted to enhance and elevate the comprehension of the fundamental background in probability and distributional theory, essential for success in a rigorous graduate-level statistics program. It serves as a seamless transition for advanced undergraduate students and introductory graduate students from diverse quantitative backgrounds, bridging the gap from a more generalized study of undergraduate mathematics and statistics to the career-focused, applied education at the graduate level. With a particular emphasis on emerging fields that hold immense potential for future M.S. and Ph.D. students, as well as advanced undergraduates entering the workforce directly: data analytics, statistics, and biostatistics, and related domains.

The book is organized into comprehensive chapters, each dedicated to exploring key concepts and principles in probability and distributional theory. It begins with an introductory overview, providing a solid foundation for the subsequent chapters. Subsequent chapters delve into detailed discussions, covering topics such as random variables, probability distributions, conditional probability, Bayesian inference, and statistical models.

Throughout the text, real-world examples and applications are incorporated to illustrate the practical significance of the theoretical concepts. These examples range from statistical analysis in finance and insurance to data mining and machine learning, showcasing the broad applications of probability and distributional theory in various fields.

Moreover, the book includes comprehensive exercises and problems, designed to reinforce the understanding of the material and test the reader's analytical skills. Each exercise is accompanied by detailed solutions, allowing students to gain a deeper grasp of the subject matter.

By mastering the foundational background in probability and distributional theory presented in this book, students will be well-equipped to excel in their graduate-level statistics program and pursue careers in data analytics, statistics, and biostatistics. The book serves as a valuable resource for both students and professionals seeking to advance their knowledge and expertise in these rapidly growing fields.

Weight: 379g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030474812
Edition number: 1st ed. 2020