Reference Shelf
Index of technical and non-technical references I use for engineering, data work, and broader context.
Technical Books
Technical references covering statistics, software engineering, human–computer interaction, e-commerce systems, and programming.
Data Science
The Art of Statistics: How to Learn from Data
by David J. Spiegelhalter (2019)
Introduces core statistical ideas for interpreting data, uncertainty, and risk in applied settings.
“Used as a reference when translating statistical findings into practical decision rules.”
Software Engineering
Refactoring: Improving the Design of Existing Code
by Martin Fowler (2019)
Catalog of behavior-preserving refactorings for improving code structure, clarity, and testability in object-oriented systems.
“Primary reference for planning incremental design changes in existing codebases.”
Clean Code: A Handbook of Agile Software Craftsmanship
by Robert C. Martin (2008)
Outlines naming, structuring, and dependency management practices for readable, maintainable code.
“Reference checklist when reviewing code for clarity and complexity.”
UX/UI Design
Human-Computer Interaction
by Alan Dix, Janet Finlay, Gregory D. Abowd, Russell Beale (2004)
Survey of interaction design principles, input modalities, and evaluation methods for user interfaces.
“Reference for structuring usability studies and reasoning about interface constraints.”
E-commerce Development
Magento 2: Build World-Class Online Stores
by Fernando J. Miguel (2017)
Guide to Magento 2 architecture, theming, extension development, and deployment for e-commerce systems.
“Used for understanding Magento's module structure and customization points in production storefronts.”
Data Engineering
Designing Data-Intensive Applications
by Martin Kleppmann (2017)
Analyzes architectures for storage, replication, streaming, and batch processing in distributed data systems.
“Primary reference when comparing data models and reliability trade-offs across system designs.”
Programming
Python Crash Course
by Eric Matthes (2019)
Introductory coverage of Python syntax, data structures, and basic project workflows.
“Used as a quick reference for core Python constructs in small utilities and scripts.”
Non-Technical Books
Non-technical references on habits, decision-making, business, and history that inform how I work and communicate.
Self-Improvement
Atomic Habits
by James Clear (2018)
Describes a model of habit formation based on small, compounding behavioral changes and feedback loops.
“Reference for designing lightweight routines around study, experimentation, and exercise.”
Psychology
Thinking, Fast and Slow
by Daniel Kahneman (2011)
Presents a dual-system model of human judgment and documents common cognitive biases.
“Used to reason about decision-making failure modes in product design and data interpretation.”
Everything Is F*cked: A Book About Hope
by Mark Manson (2019)
Examines modern concepts of hope using psychology and philosophy, connecting abstract values to behavior in a highly networked world.
“Currently reading; used to test assumptions about motivation, meaning, and resilience under persistent uncertainty.”
Business
The Lean Startup
by Eric Ries (2011)
Outlines build–measure–learn cycles, validated learning, and metric-based product iteration for new initiatives.
“Reference framework for structuring experiments and feedback loops in early-stage projects.”
History
Sapiens: A Brief History of Humankind
by Yuval Noah Harari (2014)
Traces large-scale patterns in human history, focusing on cognition, institutions, and technology.
“Used for contextualizing technological change within longer-term social and historical dynamics.”
Reference Practice
Selection Criteria
References are kept when they provide durable models, procedures, or architectures that remain useful across specific tools and frameworks.
Operational Use
Titles are treated as working tools: they are revisited to seed designs, stress-test assumptions, and debug failures as project requirements change.