A Data Science Central Community
Simplifies the understanding of analytics from a technical and functional perspective and shows a wide array of problems that can be tackled using existing technology
Provides a detailed step by step approach to identify opportunities, extract requirements, design variables and build and test models. It further explains the business decision strategies to use analytics models and provides an overview for governance and tuning
Helps formalize analytics projects from staffing, technology and implementation perspectives
Emphasizes machine learning and data mining over statistics and shows how the role of a Data Scientist can be broken down and still deliver the value by building a robust development process
Implementing Analytics demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption across all functions of an organization. Implementing Analytics simplifies and helps democratize a very specialized discipline to foster business efficiency and innovation without investing in multi-million dollar technology and manpower. A technology agnostic methodology that breaks down complex tasks like model design and tuning and emphasizes business decisions rather than the technology behind analytics.
Data warehouse professionals, data architects, IT managers, business professionals in data-intensive jobs, business and financial analysts, project managers.
Implementing Analytics, 1st Edition
2. What is Analytics?
3. Analytics Project Lifecycle
4. Analytics Project Business Case
5. Analytics Project Architecture
6. Analytics Project Team
7. Analytics Project Development Methodology
8. Existing Technology
9. Specialized Databases
10. Statistical Tools
11. Scoring and Rating Engine
12. Strategy Design Tool