Big Data Mining and Complexity: The SAGE Quantitative Research Kit
Autor Brian C. Castellani, Rajeev Rajaramen Limba Engleză Electronic book text – 8 apr 2022
- Digestible overviews of key terms and concepts relevant to using social media data in quantitative research.
- A critical review of data mining and ‘big data’ from a complexity science perspective, including its future potential and limitations
- A practical exploration of the challenges of putting together and managing a ‘big data’ database
- An evaluation of the core mathematical and conceptual frameworks, grounded in a case-based computational modeling perspective, which form the foundations of all data mining techniques
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Specificații
ISBN-13: 9781529711011
ISBN-10: 1529711010
Pagini: 232
Dimensiuni: 170 x 242 mm
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications Ltd
Seria The SAGE Quantitative Research Kit
Locul publicării:London, United Kingdom
ISBN-10: 1529711010
Pagini: 232
Dimensiuni: 170 x 242 mm
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications Ltd
Seria The SAGE Quantitative Research Kit
Locul publicării:London, United Kingdom
Cuprins
Chapter
1:
Introduction
Part 1: Thinking Complex and Critically
Chapter 2: The Failure of Quantitative Social Science
Chapter 3: What is Big Data?
Chapter 4: What is Data Mining
Chapter 5: The Complexity Turn
Part 2: The Tools and Techniques of Data Mining
Chapter 6: Case-Based Complexity: A Data Mining Vocabulary
Chapter 7: Classification and Clustering
Chapter 8: Machine Learning
Chapter 9: Predictive Analytics and Data Forecasting
Chapter 10: Longitudinal Analysis
Chapter 11: Geospatial Modeling
Chapter 12: Complex Network Analysis
Chapter 13: Textual and Visual Data Mining
Chapter 14: Conclusion: Advancing A Complex Digital Social Science
Part 1: Thinking Complex and Critically
Chapter 2: The Failure of Quantitative Social Science
Chapter 3: What is Big Data?
Chapter 4: What is Data Mining
Chapter 5: The Complexity Turn
Part 2: The Tools and Techniques of Data Mining
Chapter 6: Case-Based Complexity: A Data Mining Vocabulary
Chapter 7: Classification and Clustering
Chapter 8: Machine Learning
Chapter 9: Predictive Analytics and Data Forecasting
Chapter 10: Longitudinal Analysis
Chapter 11: Geospatial Modeling
Chapter 12: Complex Network Analysis
Chapter 13: Textual and Visual Data Mining
Chapter 14: Conclusion: Advancing A Complex Digital Social Science
Descriere
This
book
offers
a
much
needed
critical
introduction
to
data
mining
and
‘big
data’.
Supported
by
multiple
case
studies
and
examples,
the
authors
provide
everything
needed
to
explore,
evaluate
and
review
big
data
concepts
and
techniques.
Notă biografică
In addition to being a Professor of Sociology at Durham University, I am currently Adjunct Professor of Psychiatry (Northeast Ohio Medical University), Fellow of the Wolfson Research Institute for Health and Wellbeing, and Co-Editor of the Routledge Complexity in Social Science series. I am also a member of the editorial board for International Journal of Social Research Methodology and Complexity, Governance and Networks.
Trained as a sociologist, clinical psychologist and methodologist (statistics and computational social science), I have spent the past ten years developing a new case-based, data-mining approach to modeling complex social systems ¿ called the SACS Toolkit ¿ which my colleagues and I have used to help researchers, policy makers and service providers address and improve complex public health issues such as community health and well-being; infrastructure and grid reliability; mental health and inequality; big data and data mining; and globalization and global civil society. We have also recently developed the COMPLEX-IT R-studio software app, which allows everyday users seamless access to such high-powered techniques as machine intelligence, neural nets, and agent-based modeling to make better sense of the complex world(s) in which they live and work.
Trained as a sociologist, clinical psychologist and methodologist (statistics and computational social science), I have spent the past ten years developing a new case-based, data-mining approach to modeling complex social systems ¿ called the SACS Toolkit ¿ which my colleagues and I have used to help researchers, policy makers and service providers address and improve complex public health issues such as community health and well-being; infrastructure and grid reliability; mental health and inequality; big data and data mining; and globalization and global civil society. We have also recently developed the COMPLEX-IT R-studio software app, which allows everyday users seamless access to such high-powered techniques as machine intelligence, neural nets, and agent-based modeling to make better sense of the complex world(s) in which they live and work.