If you were about to learn statistics, what would your data say, and how would you interpret it? This learning and provocative question is at the core of the remarkable and easy learning book, Pocket Statistics. This book emerged from the data scientists working in different fields, designed to help people discover their statistical thinking, and then speak it out loud, developed by renowned scientist Bharat Kwatra.

A single, thorough source of definitions for thousands of statistical terms, illustrated with graphs, charts, tables, and equations. The Pocket Dictionary includes terms used in various fields related to statistics including mathematics, probability, economics, business, decision analysis, demography, epidemiology, bio-statistics, engineering, public health, quality control and many others.

If you were about to learn statistics, what would your data say, and how would you interpret it?This learning and provocative question is at the core of the remarkable and easy learning book, Pocket Statistics. This book emerged from the data scientists working in different fields, designed to help people discover their statistical thinking, and then speak it out loud, developed by renowned scientist Bharat Kwatra.Provide all the data related professionals like Business, Researchers, and all other professionals with a clear and methodical approach to essential statistical procedures. The text clearly explains the basic concepts and procedures of descriptive and inferential statistical analysis. It features an emphasis on expressions involving sums of squares and degrees of freedom as well as a strong stress on the importance of variability.This accessible approach will help business professionals tackle such perennially mystifying topics as the standard deviation, variance interpretation of the correlation coefficient, hypothesis tests, degrees of freedom, p-values, and estimates of effect size.The book focuses equally on the theoretical as well as practical aspects of data science. You will learn how to implement elementary data science tools and algorithms from scratch. The book contains an in-depth theoretical and analytical explanation of all data science concepts.Pocket Statistics Provides an accessible overview of the field of statistical learning, an essential tools for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications.

Discover one of the most complete dictionaries in data science. KEY FEATURES ● Simplified understanding of complex concepts, terms, terminologies, and techniques. ● Combined glossary of machine learning, mathematics, and statistics. ● Chronologically arranged A-Z keywords with brief description. DESCRIPTION This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in the process of data analysis and machine learning. WHAT YOU WILL LEARN ● Get absolute clarity on every concept, process, and algorithm used in the process of data science operations. ● Keep yourself technically strong and sound-minded during data science meetings. ● Strengthen your knowledge in the field of Big data and business intelligence. WHO THIS BOOK IS FOR This book is for data professionals, data scientists, students, or those who are new to the field who wish to stay on top of industry jargon and terminologies used in the field of data science. TABLE OF CONTENTS 1. Chapter one: A 2. Chapter two: B 3. Chapter three: C 4. Chapter four: D 5. Chapter five: E 6. Chapter six: F 7. Chapter seven: G 8. Chapter eight: H 9. Chapter nine: I 10. Chapter ten: J 11. Chapter 11: K 12. Chapter 12: L 13. Chapter 13: M 14. Chapter 14: N 15. Chapter 15: O 16. Chapter 16: P 17. Chapter 17: Q 18. Chapter 18: R 19. Chapter 19 : S 20. Chapter 20 : T 21. Chapter 21 : U 22. Chapter 22 : V 23. Chapter 23: W 24. Chapter 24: X 25. Chapter 25: Y 26. Chapter 26 : Z

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.