Big Data In Action From Algorithms To Scalable Product Solutions 2025 Author 1 Dr Mehraj Ali Usman Ali
Download Big Data In Action From Algorithms To Scalable Product Solutions 2025 Author 1 Dr Mehraj Ali Usman Ali PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Big Data In Action From Algorithms To Scalable Product Solutions 2025 Author 1 Dr Mehraj Ali Usman Ali book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Big Data in Action: From Algorithms to Scalable Product Solutions 2025 AUTHOR:1-Dr. Mehraj Ali Usman Ali
Author: AUTHOR:1-Dr. Mehraj Ali Usman Ali, AUTHOR:2 -Dr. Shakeb Khan
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
PREFACE In an era dominated by technological advancements, the ability to extract meaningful insights from the ever-expanding volume of data has become a competitive advantage for organizations worldwide. Big Data, with its vast scope, provides companies with unprecedented opportunities to understand consumer behavior, optimize operations, and forecast future trends. Yet, despite its potential, raw data alone is insufficient; it needs to be processed, analyzed, and interpreted in a way that yields actionable insights. This is where Predictive Analytics comes into play. Predictive analytics is the practice of using historical data, machine learning algorithms, and statistical models to forecast future outcomes and trends. By leveraging Big Data, predictive analytics allows organizations to anticipate future behaviors, market shifts, and operational needs with remarkable accuracy. This predictive power is transforming industries, from retail and healthcare to finance and manufacturing, by providing businesses with tools to make data-driven decisions rather than relying solely on intuition or past experience. The goal of this book is to explore the intersection of Big Data and Predictive Analytics, providing readers with both theoretical insights and practical approaches to harnessing predictive models in Big Data environments. Throughout the chapters, we will cover the various types of predictive models, including regression analysis, time-series forecasting, decision trees, and neural networks, highlighting how these models can be applied to Big Data to solve real-world challenges. These methodologies are essential for applications ranging from demand forecasting and fraud detection to personalized marketing and healthcare diagnostics. Data preparation plays a pivotal role in predictive analytics, and this book will delve into the critical process of cleaning, transforming, and normalizing Big Data to ensure accurate and reliable predictions. Additionally, we will explore the implementation of machine learning algorithms, such as supervised and unsupervised learning, which form the backbone of many predictive models used in modern business applications. One of the core themes of this book is to demonstrate how predictive analytics is not just a tool for data scientists but a crucial component of decision support systems, helping organizations make informed choices across various departments, including marketing, operations, and finance. The book will also address the challenges that come with predictive analytics, such as data quality, overfitting, and model interpretability, providing solutions to these common obstacles. Through detailed case studies, particularly in the financial, retail, and healthcare sectors, this book highlights the transformative impact of predictive analytics in Big Data. By the end of this book, readers will not only gain an understanding of the core principles of predictive analytics but will also be equipped with the knowledge to apply these techniques in their own organizations to drive meaningful business outcomes. We hope this book serves as both an academic resource and a practical guide, empowering professionals, researchers, and students to fully leverage predictive analytics in the context of Big Data. Authors Dr. Mehraj Ali Usman Ali Dr. Shakeb Khan
ArcGIS for Environmental and Water Issues
This textbook is a step-by-step tutorial on the applications of Geographic Information Systems (GIS) in environmental and water resource issues. It provides information about GIS and its applications, specifically using the most advanced ESRI GIS technology and its extensions. Eighteen chapters cover GIS applications in the field of earth sciences and water resources in detail from the ground up. Author William Bajjali explains what a GIS is and what it is used for, the basics of map classification, data acquisition, coordinate systems and projections, vectorization, geodatabase and relational database, data editing, geoprocessing, suitability modeling, working with raster, watershed delineation, mathematical and statistical interpolation, and more advanced techniques, tools and extensions such as ArcScan, Topology, Geocoding, Hydrology, Geostatistical Analyst, Spatial Analyst, Network Analyst, 3-D Analyst. ArcPad, ESRI’s cutting-edge mobile GIS software, is covered in detail as well. Each chapter contains concrete case studies and exercises – many from the author’s own work in the United States and Middle East. This volume is targeted toward advanced undergraduates, but could also be useful for professionals and for anyone who utilizes GIS or practices spatial analysis in relation to geology, hydrology, ecology, and environmental sciences. Exercises and supplementary material can be downloaded by chapter here: https://link.springer.com/book/10.1007%2F978-3-319-61158-7
Sustainable Procurement in Supply Chain Operations
Sustainable Procurement is an emerging concept in supply chain and operations management. Manufacturing industries have made improvements in moving from cost-based to quality-based, and customer-focused supply chain management strategies. This is becoming an integrated component in the supply chain system, with players becoming aware of the regulations and needs of the customer. It is imperative for production firms to look at the procurement activity as one of the strategic enablers for sustaining the business in the competitive global environment. This book will provide industries with an understanding of the concepts related to sustainable procurement policies and its implementation. Provides decision and theory development models in sustainable procurement supply chains Includes contributions in all three major analytics: descriptive, predictive, and perspectives in the context of sustainable procurement supply chain Discusses new business models with suppliers and opportunities for co-branding Covers how to develop new tools to measure and allocate the gains from sustainable practices among stakeholders Analyses the science of translating data into meaningful and actionable insights