Hasil Pencarian  ::  Simpan CSV :: Kembali

"

---

ABSTRACTDoing data science is difficult. Projects are typically very dynamic with requirements that change as data understanding grows. The data itself arrives piecemeal, is added to, replaced, contains undiscovered flaws and comes from a variety of sources. Teams also have mixed skill sets and tooling is often limited. Despite these disruptions, a data science team must get off the ground fast and begin demonstrating value with traceable, tested work products. This is when you need Guerrilla Analytics. n this book, you will learn about:The Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reporting.Reproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutiny.Practice tips and war stories 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research.Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions.Data gymnastics: over a dozen analytics patterns that your team will encounter again and again in projectsThe Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reportingReproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutinyPractice tips and war stories: 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and researchPreparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventionsData gymnastics: over a dozen analytics patterns that your team will encounter again and again in projects."

"Doing data science is difficult. Projects are typically very dynamic with requirements that change as data understanding grows. The data itself arrives piecemeal, is added to, replaced, contains undiscovered flaws and comes from a variety of sources. Teams also have mixed skill sets and tooling is often limited. Despite these disruptions, a data science team must get off the ground fast and begin demonstrating value with traceable, tested work products. This is when you need Guerrilla analytics.In this book, you will learn about :The Guerrilla analytics principles, simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reporting.Reproducible, traceable analytics, how to design and implement work products that are reproducible, testable and stand up to external scrutiny.Practice tips and war stories, 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research.Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions.Data gymnastics, over a dozen analytics patterns that your team will encounter again and again in projects."

"Summary:Engaging and accessible, this comprehensive introduction to statistics integrates Stata commands with numerous examples based on real data."

"This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China."