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1. |
Record Nr. |
UNINA9910878056703321 |
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Autore |
Olson David L. <1944-> |
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Titolo |
Business Analytics with R and Python / / by David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (201 pages) |
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Collana |
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AI for Risks, , 2731-6335 |
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Disciplina |
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Soggetti |
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Business information services |
Business Information Systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Data Mining in Business -- Data Mining Processes -- Data Mining Software -- Association Rules -- Cluster Analysis.-Regression Algorithms in Data Mining -- Classification Tools -- Variable Selection -- Dataset Balancing. |
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Sommario/riassunto |
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This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence. |
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2. |
Record Nr. |
UNINA9910369957903321 |
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Titolo |
The Application of Content Analysis in Nursing Science Research / / edited by Helvi Kyngäs, Kristina Mikkonen, Maria Kääriäinen |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (115 pages) : illustrations |
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Disciplina |
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Soggetti |
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Nursing—Research |
Nursing Research |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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PART I. CONTENT ANALYSIS -- 1. Qualitative research and content analysis -- 2. Inductive content analysis -- 3. Deductive content analysis -- 4. Content analysis in mixed methods research -- 5. The trustworthiness of content analysis -- 6. Qualitative research – ethical considerations -- PART II. INTEGRATING CONTENT ANALYSIS INTO THEORY DEVELOPMENT -- 7. Theory development from the results of content analysis -- 8. Instrument development based on content analysis -- 9. Statistical testing of a theory -- 10. Content analysis in systematic review. |
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Sommario/riassunto |
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This book provides principles on content analysis and its application into development of nursing theory. It offers clear guidance to students, lecturers and researchers to gain a deeper understanding of the method of content analysis, its implementation into their own research and criteria of trustworthiness evaluation. The book is written in user-friendly language with provided research examples and cases, and the content is illustrated by figures and tables. The authors offer their expertise in providing a well thought through explanation of content analysis in didactical style, which will enhance university education. The book includes highly experienced researchers who have published articles on content analysis and the trustworthiness of the method with more than 10 000 citations. Divided into two parts, this book explores the application of content analysis into nursing science. |
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The first part presents the philosophical position of content analysis, inductive and deductive methods of using content analysis, trustworthiness of the method, and ethical consideration of using content analysis. The second part informs on the theory development based on content analysis, conceptualization of the concepts of content analysis into generation of items and instrument development, and statistical testing of a hypothetical model. The last chapter shows a new approach to using content analysis in systematic reviews and quality evaluation of methodology within systematic review process. The book is an essential tool for nursing science, providing instruction on key methodological elements in order to provide rigorously conducted empirical research for clinical practice and nursing education. . |
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