Computational Business Analytics (chapman & Hall/crc Data Mining And Knowledge Discovery Series)
by Subrata Das /
2013 / English / PDF
10.7 MB Download
Learn How to Properly Use the Latest Analytics Approaches in
Your Organization
Learn How to Properly Use the Latest Analytics Approaches in
Your OrganizationComputational Business Analytics
Computational Business Analytics presents tools
and techniques for descriptive, predictive, and prescriptive
analytics applicable across multiple domains. Through many
examples and challenging case studies from a variety of fields,
practitioners easily see the connections to their own problems
and can then formulate their own solution strategies.
presents tools
and techniques for descriptive, predictive, and prescriptive
analytics applicable across multiple domains. Through many
examples and challenging case studies from a variety of fields,
practitioners easily see the connections to their own problems
and can then formulate their own solution strategies.
The book first covers core descriptive and inferential statistics
for analytics. The author then enhances numerical statistical
techniques with symbolic artificial intelligence (AI) and machine
learning (ML) techniques for richer predictive and prescriptive
analytics. With a special emphasis on methods that handle time
and textual data, the text:
The book first covers core descriptive and inferential statistics
for analytics. The author then enhances numerical statistical
techniques with symbolic artificial intelligence (AI) and machine
learning (ML) techniques for richer predictive and prescriptive
analytics. With a special emphasis on methods that handle time
and textual data, the text:Enriches principal component and factor analyses with
subspace methods, such as latent semantic analyses
Enriches principal component and factor analyses with
subspace methods, such as latent semantic analysesCombines regression analyses with probabilistic graphical
modeling, such as Bayesian networks
Combines regression analyses with probabilistic graphical
modeling, such as Bayesian networksExtends autoregression and survival analysis techniques with
the Kalman filter, hidden Markov models, and dynamic Bayesian
networks
Extends autoregression and survival analysis techniques with
the Kalman filter, hidden Markov models, and dynamic Bayesian
networksEmbeds decision trees within influence diagrams
Embeds decision trees within influence diagramsAugments nearest-neighbor and
Augments nearest-neighbor andk
k-means clustering
techniques with support vector machines and neural networks
-means clustering
techniques with support vector machines and neural networks
These approaches are not replacements of traditional
statistics-based analytics; rather, in most cases, a generalized
technique can be reduced to the underlying traditional base
technique under very restrictive conditions. The book shows how
these enriched techniques offer efficient solutions in areas,
including customer segmentation, churn prediction, credit risk
assessment, fraud detection, and advertising campaigns.
These approaches are not replacements of traditional
statistics-based analytics; rather, in most cases, a generalized
technique can be reduced to the underlying traditional base
technique under very restrictive conditions. The book shows how
these enriched techniques offer efficient solutions in areas,
including customer segmentation, churn prediction, credit risk
assessment, fraud detection, and advertising campaigns.
