Название: Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and ApplicationsАвтор: Debabrata Samanta, SK Hafzul Islam, Naveen Chilamkurti
Издательство: CRC Press
Год: 2022
Страниц: 275
Язык: английский
Формат: pdf (true), epub (true)
Размер: 15.8 MB
With the rapidly advancing fields of Data Analytics and Computational Statistics, it’s important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for Machine Learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements.
Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how Machine Learning improves the computational model based on the new information.
Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and Machine Learning.
Big data has revolutionized the decision-making process of various business organizations. It has helped uncover several information that would have been hidden otherwise. Various techniques and tools used in big data support in providing meaningful insights about various patterns and trends for data of any size, structure, and source. The advent of cloud computing has increased the computing power and automation ability. This has led to much efficient operations in a wide range of real-time applications from fraud detection to customer personalization. The fusion of data visualization, Machine Learning (ML) models, and Big Data has made it possible to answer more advanced business intelligence queries instantly as compared to traditional methods. Significant rise in data has only increased the growth of Big Data, and this torrential flood of data ensures that this field will never face extinction.
A scalable system that enables real-time publishing and consumption of a large number of messages upon subscription. It is also an open source which is used for data storing and streaming data analysis. Apache Kafka is log based, and it allows publishing data in any real-time applications.
ML consists of various algorithms that allow application software to become extensively precise in predicting results without being distinctly programmed. The basic proposition of ML is to construct algorithms that can accept input data and use statistical interpretation to forecast a result while generating results as new details become accessible. The vital role of ML comprises self-learning algorithms that develop continuously by enhancing their designated task. When put together correctly and given proper data, these ML algorithms finally produce results in the format of pattern recognition and predictive modeling. In the case of ML algorithms, data are like practice; therefore, more practice makes it better to understand the data. Algorithms optimize themselves with the available data they train with similar to the method by which Olympic athletes sharpen their bodies and skills by preparing daily. Numerous programming languages work along with ML involving Python, R, Java, and jаvascript. Python is the popular choice for many developers because of its Tensor Flow library, which helps to give a comprehensive ecosystem of ML tools.
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