Guerrilla Analytics

Guerrilla Analytics : A Practical Approach to Working with Data

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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
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Product details

  • Paperback | 276 pages
  • 152 x 228 x 24mm | 379.99g
  • Morgan Kaufmann Publishers In
  • San Francisco, United States
  • English
  • black & white illustrations, black & white tables, figures
  • 0128002182
  • 9780128002186
  • 1,082,644

Table of contents

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


21. Data Gymnastics

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Review Text

"... 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
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Review quote

"... 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
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About Enda Ridge

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.
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5 ratings
3.6 out of 5 stars
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4 60% (3)
3 0% (0)
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1 20% (1)
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