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2 produkter
2 produkter
E-bok
Engelska, 20133 015 kr
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For organizations operating in a modern business environment, adopting the latest information technologies (IT) is of paramount importance. Organizational decision makers are increasingly interested in IT acquisition, constantly seeking the most advanced solutions in order to give their constituents a distinct competitive advantage. Managing Enterprise Information Technology Acquisitions: Assessing Organizational Preparedness provides leaders and innovators with research and strategies to make the most of their options involving IT and organizational management approaches. This book will serve as a critical resource for leaders, managers, strategists, and other industry professionals who must be prepared to meet the constant changes in the field of information technologies in order to effectively guide their organizations and achieve their respective goals.
E-bok
Engelska, 20214 644 kr
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In today's digital world, the huge amount of data being generated is unstructured, messy, and chaotic in nature. Dealing with such data, and attempting to unfold the meaningful information, can be a challenging task. Feature engineering is a process to transform such data into a suitable form that better assists with interpretation and visualization. Through this method, the transformed data is more transparent to the machine learning models, which in turn causes better prediction and analysis of results. Data science is crucial for the data scientist to assess the trade-offs of their decisions regarding the effectiveness of the machine learning model implemented. Investigating the demand in this area today and in the future is a necessity. The Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science provides an in-depth analysis on both the theoretical and the latest empirical research findings on how features can be extracted and transformed from raw data. The chapters will introduce feature engineering and the recent concepts, methods, and applications with the use of various data types, as well as examine the latest machine learning applications on the data. While highlighting topics such as detection, tracking, selection techniques, and prediction models using data science, this book is ideally intended for research scholars, big data scientists, project developers, data analysts, and computer scientists along with practitioners, researchers, academicians, and students interested in feature engineering and its impact on data.