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.

Table of Contents

PART 1 Principles
Chapter 1 Introducing Guerrilla Analytics
Chapter 2 Guerrilla Analytics: Challenges and Risks
Chapter 3 Guerrilla Analytics Principles

PART 2 Practice
Chapter 4 Stage 1: Data Extraction
Chapter 5 Stage 2: Data Receipt
Chapter 6 Stage 3: Data Load
Chapter 7 Stage 4: Analytics Coding for Ease of Review
Chapter 8 Stage 5: Analytics Coding to Maintain Data Provenance
Chapter 9 Stage 6: Creating Work Products
Chapter 10 Stage 7: Reporting
Chapter 11 Stage 8: Consolidating Knowledge in Builds

PART 3 Testing
Chapter 12 Introduction to Testing
Chapter 13 Testing Data
Chapter 14 Testing Builds
Chapter 15 Testing Work Products

Part 4 Building Guerrilla Analytics Capability
Chapter 16 People
Chapter 17 Process
Chapter 18 Technology
Chapter 19 Closing Remarks