Written by one of the top most statisticians with experience in diverse fields of applications of statistics, the book deals with the philosophical and methodological aspects of information technology, collection and analysis of data to provide insight into a problem, whether it is scientific research, policy making by government or decision making in our daily lives.The author dispels the doubts that chance is an expression of our ignorance which makes accurate prediction impossible and illustrates how our thinking has changed with quantification of uncertainty by showing that chance is no longer the obstructor but a way of expressing our knowledge. Indeed, chance can create and help in the investigation of truth. It is eloquently demonstrated with numerous examples of applications that statistics is the science, technology and art of extracting information from data and is based on a study of the laws of chance. It is highlighted how statistical ideas played a vital role in scientific and other investigations even before statistics was recognized as a separate discipline and how statistics is now evolving as a versatile, powerful and inevitable tool in diverse fields of human endeavor such as literature, legal matters, industry, archaeology and medicine.Use of statistics to the layman in improving the quality of life through wise decision making is emphasized.

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.