Guerrilla Analytics: A Practical Approach to Working with Data

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

The 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

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