Название: Principles and Methods for Data Science (Volume 43)
Автор: Arni S.R. Srinivasa Rao, C.R. Rao
Издательство: North-Holland, Elsevier
Серия: Handbook of Statistics
Год: 2020
Страниц: 498
Язык: английский
Формат: pdf (true), epub
Размер: 40.8 MB

Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big Data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more.

Data science is an umbrella term used for referring to concepts and practices of subset of the topics under Artificial Intelligence (AI) methodologies. AI is actually a framework to define notion of intelligence in software systems or devices in terms of knowledge representation and reasoning methodologies. There are two main types of reasoning methods deductive and inductive over data. The major class of machine learning and deep learning methods come under inductive reasoning where essentially, missing pieces of information are interpolated based on existing data through numerical transformations. However, today AI is mostly identified with deduction systems while it is actually a comprehensive school of thought and formal framework. The AI framework offers rigor and robustness to the solutions developed and there is still scope for onboarding today's deep learning solutions and reap benefits of sturdiness. Data science is about end to end development of a smart solution that involves creation of pipelines for activities for data generation, business decision making and solution maintenance with humans in loop. Data generation is a cycle of activities involving collection, refinement, feature transformations, devising more insightful heuristic measures based on domain peculiarities and iterations to enhance quality of data driven decisions.

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