BOOK SUMMARY The main topics in this book are; • Machine Learning Algorithms for Predictive Analysis • Natural Language Processing in Text Analytics • Graph Analytics and Network Analysis • Time Series Analysis and Forecasting • Deep Learning in Image and Video Analytics • Streaming Data Analytics • Spatial Data Analysis and Geospatial Analytics • Big Data Ethics and Privacy Considerations “Advanced Big Data Analytics” offers a comprehensive exploration of cutting-edge techniques and methodologies in the realm of big data analysis. Through a blend of theoretical insights, practical examples and real-world case studies, the book guides readers in harnessing the power of vast datasets to uncover valuable insights, make informed decisions and address contemporary data-driven challenges.

Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.

BOOK SUMMARY The main topics in this book are; • Introduction to SPSS Basics • Data Entry and Management in SPSS • Descriptive Statistics in SPSS • Data Visualization in SPSS • Hypothesis Testing and Inferential Statistics with SPSS • Correlation and Regression Analysis in SPSS • Categorical Data Analysis in SPSS • Advanced Topics in SPSS Introduction to SPSS is a comprehensive guide that demystifies the complexities of IBM SPSS software, providing readers with practical skills to navigate and utilize its features effectively. Readers will learn how to import data from various sources, calculate descriptive statistics, create charts and graphs, perform hypothesis tests, interpret regression models and even delve into advanced topics like factor and cluster analysis.

Interest in applying analytics, machine learning, and artificial intelligence to sales and marketing has grown dramatically, with no signs of slowing down. This book provides essential guidance to apply advanced analytics and data mining techniques to real-world business applications. The foundation of this text is the author’s 20-plus years of developing and delivering big data and artificial intelligence solutions across multiple industries: financial services, pharmaceuticals, consumer packaged goods, media, and retail. He provides guidelines and summarized cases for those studying or working in the fields of data science, data engineering, and business analytics. The book also offers a distinctive style: a series of essays, each of which summarizes a critical lesson or provides a step-by-step business process, with specific examples of successes and failures. Sales and marketing executives, project managers, business and engineering professionals, and graduate students will find this clear and comprehensive book the ideal companion when navigating the complex world of big data analytics.

Information Technology for Management provides students with a comprehensive understanding of the latest technological developments in IT and the critical drivers of business performance, growth, and sustainability. Integrating feedback from IT managers and practitioners from top-level organizations worldwide, the International Adaptation of this well-regarded textbook features thoroughly revised content throughout to present students with a realistic, up-to-date view of IT management in the current business environment. This text covers the latest developments in the real world of IT management with the addition of new case studies that are contemporary and more relevant to the global scenario. It offers a flexible, student-friendly presentation of the material through a pedagogy that is designed to help students easily comprehend and retain information. There is new and expanded coverage of Artificial Intelligence, Robotics, Quantum Computing, Blockchain Technology, IP Intelligence, Big Data Analytics, IT Service Management, DevOps, etc. It helps readers learn how IT is leveraged to reshape enterprises, engage and retain customers, optimize systems and processes, manage business relationships and projects, and more.

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!