Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency.
With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes:
- A complete overview of big data and its notable characteristics
- Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases
- Comprehensive coverage of data mining, text analytics, and machine learning algorithms
- A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes
Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.
Keywords: Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners; Jared Dean; SAS; data analytics; big data analytics; big data; Hadoop; in-memory databases; high performance computing; massively parallel processing; big data algorithms; predictive analytics; predictive modeling; data mining; machine learning; data analysis; business analytics; big data modeling; NoSQL; characteristics of big data; MPP; recommendation systems; statistics; distributed computing; text analytics; text mining