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Record Nr. |
UNINA9910369900003321 |
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Autore |
Kampakis Stylianos |
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Titolo |
The Decision Maker's Handbook to Data Science : A Guide for Non-Technical Executives, Managers, and Founders / / by Stylianos Kampakis |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020 |
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ISBN |
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9781523150502 |
1523150505 |
9781484254943 |
1484254945 |
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Edizione |
[2nd ed. 2020.] |
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Descrizione fisica |
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1 online resource (154 pages) |
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Disciplina |
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Soggetti |
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Data structures (Computer science) |
Data mining |
Big data |
Data Structures and Information Theory |
Data Mining and Knowledge Discovery |
Big Data/Analytics |
Big Data |
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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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Note generali |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Chapter 1: Demystifying Data Science and All the Other Buzzwords -- Chapter 2: Data Management -- Chapter 3: Data Collection Problems -- Chapter 4: How to Keep Data Tidy -- Chapter 5: Thinking like a Data Scientist (Without Being One) -- Chapter 6: A Short Introduction to Statistics -- Chapter 7: A Short Introduction to Machine Learning -- Chapter 8: Problem Solving -- Chapter 9: Pitfalls -- Chapter 10: Hiring and Managing Data Scientists -- Chapter 11: Building a Data-Science Culture -- Chapter 12: Epilogue: Data Science Rules the World -- Appendix A: Tools for Data Science -- . |
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Sommario/riassunto |
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Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a |
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confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don’t realize is that data science is in fact quite multidisciplinary—useful in the hands of business analysts, communications strategists, designers, and more. With the second edition of The Decision Maker’s Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. |
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2. |
Record Nr. |
UNINA9910299471803321 |
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Titolo |
Social Networking : Mining, Visualization, and Security / / edited by Mrutyunjaya Panda, Satchidananda Dehuri, Gi-Nam Wang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
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ISBN |
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Edizione |
[1st ed. 2014.] |
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Descrizione fisica |
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1 online resource (313 p.) |
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Collana |
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Intelligent Systems Reference Library, , 1868-4394 ; ; 65 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Application software |
Computational Intelligence |
Artificial Intelligence |
Information Systems Applications (incl. Internet) |
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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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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Diffusion of Information in Social Networks -- Structure and Evolution of Online Social Networks -- Machine Learning for Auspicious Social Network Mining -- Testing Community Detection Algorithms: A Closer Look at Datasets -- Societal Networks: The networks of Dynamics of Interpersonal Associations -- Methods of tracking online community in social network -- Social Network Analysis Approach for Studying Caste, Class and Social Support in Rural Jharkhand and West Bengal: An Empirical Attempt -- Evaluating the Propagation Strength of Malicious Metaphor in Social Network: Flow Through Inspiring Influence of Members -- Social Network Analysis: A methodology for studying Terrorism -- Privacy and Anonymization in Social Networks -- On the use of Brokerage Approach to discover Influencing Nodes in Terrorist Networks. |
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Sommario/riassunto |
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With the proliferation of social media and on-line communities in networked world a large gamut of data has been collected and stored in |
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databases. The rate at which such data is stored is growing at a phenomenal rate and pushing the classical methods of data analysis to their limits. This book presents an integrated framework of recent empirical and theoretical research on social network analysis based on a wide range of techniques from various disciplines like data mining, social sciences, mathematics, statistics, physics, network science, machine learning with visualization techniques, and security. The book illustrates the potential of multi-disciplinary techniques in various real life problems and intends to motivate researchers in social network analysis to design more effective tools by integrating swarm intelligence and data mining. . |
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