Data Analytics and Machine Learning: Navigating the Big Data Landscape Edited by PushpaSingh, Asha RaniMishra, and PayalGargSpringer, 2024, 366 pages, $169.99, hardcover ISBN: 978‐9819704477

Readership: Statistics graduate and advanced undergraduate students, as well as practitioners. Data Analytics and Machine Learning: Navigating the Big Data Landscape, edited by Pushpa Singh, Asha Rani Mishra and Payal Garg, presents an in-depth analysis of successful data-driven initiatives, mainly how organizations leverage data to enable decision-making, optimize operations and produce favourable outcomes. This book is not just about big data methods. It delves into how the transformative power of data analytics and big data is used to solve various problems that require data. Data are everywhere, including in organizations that possess hidden data. Consequently, examining the expansion of big data, the advanced data analytic methods and the machine learning (ML) revolution can bring new prospects and opportunities in the ever-changing data landscape and explosive power in computing to facilitate such an analysis. This book, in essence, provides applications that could be explored in the ever-changing world of data and the appropriate methods to analyse such data. Readers are presented with interdisciplinary approaches in data analytics, ensuring no focus is on one discipline covered in this book. I suspect after reading this book, readers will possibly even think of new applications that could solve a particular problem in the organization, feeling included in this diverse field! Chapter 1, authored by Youddha Beer Singh, Aditya Dev Mishra, Mayank Dixit and Atul Srivastava, all renowned experts in the field, provides a comprehensive introduction. This chapter explains the role of data analytics, big data and ML and integrates these concepts into modern innovation and technology. It is a solid foundation that provides a deep understanding of the vast potential of data analytics and big data, and how organizations can effectively use them to solve real problems and optimize their bottom line. Chapter 2, prepared by Ritu Sharma and Payal Garg, provides a brief overview of the fundamentals and lifecycle of data analytics. The authors not only lay the foundation for the present stage of technology and data analytics systems but also detail the practical applications of open-source tools, such as Power BI and Tableau, in the creation of data analytic systems. They emphasize that data analytics is more than just running an OLS regression; it requires various stages to complete the analysis: processing, acquisition, reuse and analysis of the given data. The careful use of various tools, such as SQL and business analytics, is necessary to solve a particular problem the organization confronts. The final section of this chapter outlines the future of data analytics by leveraging its fundamental lifecycle and elucidates various data analytics tools that could be used in real-world scenarios. Ruchi Gupta, Anupama Sharma and Tanweer Alam wrote Chapter 3, which is a practical guide for constructing predictive models using ML. The key themes include selecting an appropriate ML model tailored to a specific problem, mastering the art of refining raw data into informative features to align with chosen algorithms, and the iterative process of model training and hyperparameter fine-tuning for optimal predictive accuracy. In data analytics, hyperparameter fine-tuning is arduous, so the authors have carefully laid out the appropriate methods for fine-tuning, providing the readers with practical guidance that can be directly applied in their work or research. In Chapter 4, Rashmi Sharma, Charu Pawar, Pranjali Sharma and Ashish Malik show how big data and ML can help improve agricultural production. Agricultural production consists of the selection of crops, soil preparation, seed selection, seed sowing, irrigation, fertilizer/manure selection, control of pests/weeds/diseases, crop harvesting and crop distribution for sales. The authors discuss how predictive ML supervised classification algorithms, K-nearest neighbour (KNN), can be beneficial by looking at different parameters, for example, climatic conditions, soil type and previous crops grown. These models make use of new agricultural practices, including digital incorporation of IoT, AI/ML and sensor technologies. Their algorithms were more than 90% effective, depending on some uncertainties in collecting data from different sensors. Shahina Anjum, Sunil Kumar Yadav and Seema Yadav, in their chapter, discussed the Big Data Streams because they have a significant impact owing to the reality that there are many applications from where a large amount of data is continuously and rapidly generated. More importantly, there are limitations to the assessment at a rapid pace via machine learning. Consequently, data streaming would be a viable method. A modern-day data streaming architecture allows taking up, operating and analysing the large mass of high-speed data from a collection of sources in real-time to build more effective data for organizations. This chapter comprises a brief assessment of the stream analysis of big data, which engages a thorough and organized way to look at the inclination of technologies and tools used in big data streaming along with their comparisons. Soumya Ranjan Mishra, Hitesh Mohapatra and Sandeep Saxena wrote Chapter 6. This chapter provides a comprehensive survey of the advancements in image super-resolution (SR). SR is the process of obtaining high-resolution images from at least one or more low-resolution observations of the same image. These advancements would be helpful for researchers, engineers, and practitioners in computer vision, image processing and ML. Image SR continues to evolve, which could ultimately shape the future of high-resolution imaging. Asha Rani Mishra, Rajat Kumar Rathore and Sansar Singh Chauhan delve into finance applications in their chapter such as cryptocurrency, which operates via a Blockchain. These currencies are volatile and using forecasting methods would be challenging because of the number of existing cryptocurrencies. Given such volatility, the authors use the time series-based forecasting model to predict the future price of Ethereum because it handles both logistic growth and piece-wise linearity of data. More importantly, this model is independent as it does not depend on past or historical data that could contain seasonality. This model is suitable for real use cases after seasonal fitting using the naïve model and Facebook Prophet (FBProphet). Ironically, FBProphet performs better than other forecasting models as revealed by the authors. In Chapter 8, Neha Chandela, Kamlesh Kumar Raghuwanshi and Himani Tyag discussed the application of R for machine learning and data analytics in e-commerce. The authors use the R tool and represent it with utility for designing recommendation systems and finding insights from e-commerce data by showing various plots. Their study results showed that the R tool has the potential to be one of the leading tools for research and business analytics. Ajay Kumar Badhan, Abhishek Bhattacherjee and Rita Roy delve into deep learning applications as applied to big data. The authors thoroughly assess deep learning methods and how they can be applied in big data analytics. They show how deep learning models can benefit feature learning, enabling the automatic extraction of valuable information from large data sets. They also describe the emerging trends in deep learning and big data analysis by providing applications and new methods that can be used in the future. Chapter 10 elucidates the significant aspects of data privacy with ethics under data science. There has been an explosion of techniques that can be applied in data analytics, and ethical matters could arise. Rajasegar R. S., Gouthaman P., Vijayakumar Ponnusamy, Arivazhagan N. and Nallarasan V. discuss how important it is to protect an individual's personal information. They also discuss the various legal frameworks as applied to data privacy, namely, GDPR (General Data Protection Regulation) in the European Union and other data protection laws from other countries. They also delineate how organizations must meticulously put their efforts into ensuring data privacy. To help our understanding of data privacy, the authors provide examples from projects, case studies and real-world scenarios to support and discuss how data analytics create positive and negative impacts. In Chapter 11, Vijayakumar Ponnusamy, Nallarasan V., Rajasegar R. S., Arivazhagan N. and Gouthaman P. discuss how the advent of modern technology has enabled technologies like machine learning, big data and data analytics that are revolutionizing how businesses and marketers, healthcare, finance, manufacturing and supply chains, transportation and logistics, and energy utilization are using these applications to improve their decision-making. The authors discuss data analytics, big data and ML, which extensively apply to all disciplines. Consequently, these applications have revolutionized decision-making processes, increased productivity and stimulated creativity. Prince Shiva Chaudhary, Mohit R. Khurana and Mukund Ayalasomayajula, in their chapter, discuss the profound impact of Data Analytics, Big Data and AI as used in real-world applications from the large amount of data available and delve into various algorithms and techniques employed in ML, DL and analytics. For example, they facilitate early disease detection and customized treatments for patients, which changes the scope of how health care is administered. Another example is that analytics shape investment strategies used in finance. Regardless, numerous methods in data science can be applied to many disciplines. In Chapter 13, Hitesh Mohapatra and Soumya Ranjan Mishra critically evaluate the AI Tool's information, coupled with verification from reliable sources, which requires verification from other sources. More specifically, the AI Tool, a powerful large language model (LLM), is designed to create human-like responses in natural language conversations, leveraging extensive training on internet data for information. This chapter underscores AI Tool's potential as a promising tool for natural language processing, with applications spanning disciplines. Swati Chauhan, Nidhi Malik and Rekha Vig's next chapter provide a specific application of CT lung nodule segmentation, which is complicated and affects medical research, surgical planning and diagnostic decision-making support. The latter is essential as global lung cancer mortality is growing, requiring early cancer screenings to detect lung cancer. The authors do not discuss these methods, but they also include a summary of datasets open to the public, which are the primary resources scholars use in this area. Chapter 15, prepared by Abhishek Bhattacherjee and Ajay Kumar Badhan, provide a concise yet thorough overview of the influential trio of big data, ML and data analytics. From this chapter, the authors encourage further investigation of their potential to reshape industries and allow for innovation using real-world applications. They offer a brief overview of their practical uses, illuminating how these technologies reshape markets and drive creativity. Data analytics is emphasized first in this chapter to illuminate its capacity to extract valuable insights from different data sources. In the final chapter, Parijata Majumdar and Sanjoy Mitra provide an overview of data analytics applications for business. The authors thoroughly review the literature, emphasizing the necessity of business transformation, the function of big data analytics, and AI's role. They present a case study of Big Mart Sales forecasting that was discussed, compared and analysed in the context of business transformation. The chapter also discusses the possible obstacles to firms implementing AI and Big Data Analytics (BDA). It will offer firms a roadmap for utilizing AI and big data analytics that generate commercial value for organizations. This book was jam-packed with new information, big data and AI applications. Put another way, there is a confluence between big data, machine learning and data analytics. While the advancements in AI offer unprecedented capabilities to extract knowledge from various datasets, they could pose ethical, societal and technological challenges that require care when completing such an analysis. The use of big data and AI will continue to grow over time. Therefore, we need to understand its role and also focus on ethical practices of AI and the use of big data that enables suitable interdisciplinary research that provides favourable to enhance our lives.