Guerrilla Analytics

A Practical Approach to Working with Data

Enda Ridge (Data Scientist, London, United Kingdom)

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English

Morgan Kaufmann Publishers In
23 September 2014

Library, archive & information management; Business applications; Database programming; Data capture & analysis

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

By: Enda Ridge (Data Scientist London United Kingdom)
Imprint: Morgan Kaufmann Publishers In
Country of Publication: United States
Dimensions: Height: 229mm, Width: 152mm, Spine: 15mm
Weight: 410g
ISBN: 9780128002186
ISBN 10: 0128002182
Pages: 276
Publication Date: 23 September 2014
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

1. Preface Part 1: Principles 2. Introducing Guerrilla Analytics 3. Guerrilla Analytics: Challenges and Risks 4. Guerrilla Analytics Principles Part 2: Practice 5. Stage 1: Data Extraction 6. Step 2: Data Receipt 7. Step 3: Data Load 8. Stage 4: Analytics Coding for Ease of Review 9. Stage 5: Analytics Coding to Maintain Data Provenance 10. Stage 6: Creating Work Products 11. Stage 7: Reporting 12. Stage 8: Consolidating Knowledge in Builds Part 3: Testing 13. Introduction to Testing 14. Testing Data 15. Testing Builds 16. Testing Work Products Part 4: Building Guerrilla Capability 17. People 18. Process 19. Technology 20. Closing Remarks Appendix 21. Data Gymnastics References

Enda Ridge is an accomplished data scientist whose experience spans consulting, pre-sales of analytics software and research in academia. He has consulted to clients in the public and private sectors including financial services, insurance, audit and IT security. Enda is an expert in agile analytics for real world projects where data and requirements change often, resources and tooling are sometimes very limited and results must be traceable and auditable for high profile stakeholders. His experience includes analytics to support the forensic investigation of a major US bankruptcy and the remediation a UK bank’s mis-selling of financial products. He has also applied machine learning and NoSQL approaches to problems in document classification, surveillance and IT access controls. His PhD used Design of Experiments techniques to methodically evaluate algorithm performance. Enda has authored or co-authored 12 academic research papers, is an invited contributor to edited books and has spoken at several analytics practitioner conferences. Enda holds a Bachelor’s degree in Mechanical Engineering and Master’s in Applied Computing from the National University of Ireland at Galway and was awarded the National University of Ireland’s Travelling Studentship in Engineering. His PhD was awarded by the University of York, UK.

Reviews for Guerrilla Analytics: A Practical Approach to Working with Data

... a very pleasant read.very useful to practitioners and managers who are newly responsible for data analytics or who have had difficulty in previous projects. --Computing Reviews