Research Engineering Learning Path

Beta Notice

This Research Engineering Learning Path is currently in beta. We're actively improving and refining the content based on community feedback and real-world usage.

Help us improve:

• Share feedback in our Discord community - use #research-engineering-path channel
• Submit improvements by opening a pull request to the GitHub repository
• Report issues or suggest enhancements through GitHub Issues

Your input helps make this learning path better for everyone in the community!

Welcome to the Average Joes Lab Research Engineering Learning Path - your comprehensive guide to becoming a research engineer and contributing to democratized research.

What is Research Engineering?

Research Engineering is the bridge between cutting-edge research and practical implementation. As a research engineer, you'll:

• Design and conduct original research across any field of interest
• Implement research findings into working prototypes and systems
• Collaborate with researchers to translate ideas into reality
• Contribute to open science through reproducible research practices
• Democratize research by making it accessible outside traditional institutions

Why Research Engineering at Average Joes Lab?

Open and Accessible

• No gatekeeping based on credentials or institutional affiliation
• Learn from real practitioners and community experts
• Contribute to meaningful research from day one

Community-Driven

• Collaborate with fellow citizen researchers
• Get mentorship from experienced research engineers
• Participate in real research projects with global impact

Practical Focus

• Learn by doing real research, not just theory
• Build a portfolio of actual research contributions
• Develop skills that translate directly to career opportunities

Prerequisites - Start Where You Are

The Reality: You Need Less Than You Think

Research engineering is more about methodology and systematic thinking than having perfect prerequisites. The most important requirements are:

Universal Prerequisites (All Fields):

• Curiosity - Genuine interest in understanding how things work
• Basic computer literacy - Comfortable with files, software, internet research - Digital Literacy Basics
• High school mathematics - Algebra basics and basic statistics (you can learn more as needed)
• Willingness to learn - Persistence when things don't work the first time - Growth Mindset Guide
• Basic programming - Any language (Python recommended for versatility)

What You DON'T Need to Start:

• ❌ Advanced degrees or credentials
• ❌ Years of domain expertise
• ❌ Perfect mathematical background
• ❌ Expensive equipment or software
• ❌ Institutional affiliation

Field-Specific Variations

Prerequisites vary by field, but you can learn as you go:

Lower Math Requirements:

• Biology, Psychology, History: Basic statistics, research methods
• Digital Humanities: Minimal math, computational thinking helpful

Medium Math Requirements:

• Economics, Sociology: Statistics, basic calculus helpful
• Chemistry: Calculus, some linear algebra for advanced topics

Higher Math Requirements:

• Physics, Engineering: Calculus, linear algebra, differential equations
• Computer Science/AI: Linear algebra, calculus, probability, discrete math

Key Insight: Even "high math" fields can be approached gradually. You learn the math you need for each specific problem, not everything upfront.

Perceptron Example Prerequisites

Our neural network example demonstrates the "learn as you go" approach:

To Start (Week 1-4):

• Basic algebra - Understanding variables, equations, simple functions
• High school math - No calculus required initially
• Basic programming - Variables, loops, functions in any language
• Curiosity about learning - How do brains/computers learn patterns? - Introduction to Learning

As You Progress (Week 5-8):

• Linear algebra basics - Dot products, vectors (learned when implementing perceptron)
• Basic statistics - Accuracy, precision, evaluation metrics (learned when testing)
• Python fundamentals - Learned through hands-on implementation

For Advanced Work (Week 9-12):

• Calculus concepts - Derivatives for understanding backpropagation (learned when needed)
• Probability theory - For understanding learning algorithms (learned in context)
• Advanced statistics - For rigorous experimental analysis (learned through practice)

Specialization (Week 13+):

• Advanced mathematics - Only as needed for specific research directions
• Domain expertise - Built through continuous research and community engagement

The Learning Strategy: Just-in-Time Knowledge

1. Start with genuine interest in a specific problem or field
2. Pick a foundational paper that seems approachable (see guidance below)
3. Learn the minimum math/tools needed for that specific paper
4. Build knowledge incrementally as you tackle more complex problems
5. Join the community to learn from others on similar journeys

Remember: Every expert started as a beginner. The research engineering methodology teaches you to build knowledge systematically through hands-on work with real problems. You don't need to master everything upfront - you need to be willing to learn what you need as you encounter it.

🚀 Get Started with the Template Repository

Fork the Research Engineering Starter Template - A complete repository structure designed to guide you through all 4 phases of the learning path.

What's Included:

• Organized directory structure for each phase
• Detailed README files with phase-specific guidance
• Best practices for research engineering
• Templates and examples for documentation
• Proper .gitignore for research projects
• Links to all learning resources from this guide

Quick Start:

1. Fork the template to your GitHub account
2. Clone locally and choose your research paper
3. Follow the 4-phase structure with built-in guidance
4. Document your journey using the organized folders

Learning Path Overview

Phase 1: Research Foundations (Weeks 1-4)

Build your research methodology foundation

Week 1-2: Research Fundamentals

• Scientific method and research design - Khan Academy: Scientific Method
• Literature review techniques - MIT Libraries: How to Conduct a Literature Review
• Research ethics and open science principles - NIH: Introduction to Research Ethics
• Introduction to reproducible research - The Turing Way: Guide to Reproducible Research

Week 3-4: Data and Analysis

• Data collection methodologies - Coursera: Research Methods Fundamentals (audit for free)
• Statistical analysis fundamentals - Khan Academy: Statistics and Probability
• Research tools and software - Software Carpentry: Research Computing Skills
• Documentation and version control - Git Handbook by GitHub

Milestone: Complete your first literature review on a topic of interest

Example Research Path - The Perceptron (1958):

This neural network example demonstrates the framework, but the same approach works for any field - biology, psychology, economics, physics, etc.

• Literature review of Frank Rosenblatt's "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain" (1958)
• Study the historical context: What problem was Rosenblatt trying to solve? - History of Neural Networks
• Understand the theoretical foundation: How does the perceptron learn? - 3Blue1Brown: Neural Networks
• Identify the paper's significance: Why was this breakthrough important? - MIT: Introduction to Neural Networks

Phase 2: Technical Skills (Weeks 5-8)

Develop the technical skills for research engineering

Week 5-6: Programming for Research

• Python for research and data analysis - Python.org Beginner's Guide | Automate the Boring Stuff (Free)
• Jupyter notebooks and research workflows - Jupyter.org Getting Started | Real Python: Jupyter Notebook Tutorial
• Version control with Git for research - Git Tutorial by Atlassian | GitHub Skills
• Collaborative coding practices - GitHub Flow Guide | Best Practices for Scientific Computing

Week 7-8: Research Engineering Tools and Methodologies

• Experiment Tracking: Tools for logging experiments, parameters, and results - MLflow Documentation | Weights & Biases Tutorial
• Version Control for Research: Managing code, data, and model versions - DVC (Data Version Control) | Git LFS Guide
• Reproducible Environments: Containerization and environment management - Docker Getting Started | Conda User Guide
• Research Validation: Automated testing and validation pipelines - pytest Documentation | GitHub Actions Tutorial
• Domain-specific frameworks: Tools specific to your research field - NumPy Quickstart | pandas Getting Started

Milestone: Implement and reproduce a research paper

Continuing the Perceptron Example:

• Week 5-6: Implement the perceptron algorithm from scratch (~50 lines of code) - Neural Networks from Scratch Tutorial | Perceptron Implementation Guide
• Week 7-8: Apply research engineering practices:
  • Experiment Tracking: Log learning rates, initialization methods, convergence metrics - MLflow Tracking Tutorial
  • Baseline Comparison: Compare against random classifier performance - Scikit-learn Dummy Classifiers
  • Systematic Testing: Test on AND, OR, XOR logic gates with proper evaluation - Logic Gates Dataset
  • Hyperparameter Analysis: Sweep learning rates (0.01, 0.1, 1.0) and document effects - Hyperparameter Tuning Guide
  • Reproducible Setup: Version control code, document exact environment and data - Reproducible Research Guide
• Discovery: Notice the perceptron fails on XOR - this is a significant limitation! - XOR Problem Explained
• Research Engineering Insight: Proper experimental methodology helped you systematically identify the limitation
• Documentation: Create reproducible research report with all experimental details - Scientific Writing Guide

Phase 3: Research Practice (Weeks 9-12)

Apply your skills to real research projects

Week 9-10: Research Project Design & Experimental Rigor

• Hypothesis Formation: Clear, testable research questions - Research Question Formulation Guide | PICO Framework
• Experimental Design: Controls, variables, and validation methodology - Experimental Design Basics | Randomized Controlled Trials
• Ablation Planning: Systematic component analysis strategy - Ablation Studies Guide | Component Analysis Methods
• Statistical Planning: Sample sizes, significance testing, evaluation metrics - Power Analysis Guide | Effect Size Calculator
• Reproducibility Protocol: Documentation and replication standards - FAIR Data Principles | Reproducibility Checklist

Week 11-12: Research Execution & Analysis

• Systematic Experimentation: Controlled experiments with proper baselines - Experimental Controls Guide | A/B Testing Principles
• Ablation Studies: Isolate and test individual components - Feature Ablation Tutorial | Systematic Component Testing
• Statistical Analysis: Significance testing, confidence intervals, effect sizes - Statistical Tests Guide | Confidence Intervals Explained
• Error Analysis: Failure mode analysis and edge case investigation - Error Analysis Methods | Failure Mode Analysis
• Bias Detection: Identify and mitigate experimental and data biases - Research Bias Types | Bias Mitigation Strategies

Milestone: Complete an original research project

The Research Innovation Flywheel in Action:

• Week 9-10: Apply research engineering to the XOR problem:
  • Hypothesis: "Multi-layer networks can solve XOR where single-layer perceptrons cannot"
  • Experimental Design: Compare single-layer vs multi-layer performance on XOR
  • Ablation Planning: Test different architectures (2-layer, 3-layer), activation functions, learning rates
  • Historical Research: Discover Rosenblatt's 1962 "Principles of Neurodynamics" proposed MLPs - Principles of Neurodynamics Summary
  • The Real Problem: MLPs could theoretically solve XOR, but no training method existed! - The XOR Problem History
• Week 11-12: Systematic experimentation and analysis:
  • Controlled Experiments: Implement MLP with random weight updates (baseline) - Multi-layer Perceptron Tutorial
  • Ablation Studies: Test impact of hidden layer size, initialization methods, activation functions - Neural Network Architecture Guide
  • Error Analysis: Document which XOR patterns fail and why during training attempts - Training Dynamics Analysis
  • Statistical Analysis: Quantify performance differences and training instability - Learning Curves Analysis
  • Breakthrough Discovery: Research leads to 1986 Rumelhart, Hinton & Williams backpropagation paper - Backpropagation Paper
• Research Engineering Insight: Systematic methodology revealed the training bottleneck that stalled the field for 25 years!

Phase 4: Community Contribution (Weeks 13-16)

Contribute to the research community

Week 13-14: Research Communication

• Writing research papers and reports - Nature: How to Write a Research Paper | Scientific Writing Guide
• Creating research presentations - Presentation Zen Principles | Academic Presentation Guide
• Open science publication practices - PLOS ONE Submission Guidelines | arXiv Submission Guide
• Community engagement and networking - Academic Twitter Guide | Research Networking Tips

Week 15-16: Research Leadership

• Mentoring new researchers - Mentoring Guide for Scientists | Effective Mentoring Practices
• Leading collaborative projects - Project Management for Researchers | Team Science Guide
• Contributing to research tools and frameworks - Open Source Contribution Guide | Research Software Engineering
• Building research communities - Community Building Guide | Scientific Community Management

Milestone: Publish your research and mentor another community member

Completing the Research Cycle:

• Week 13-14: Write up your complete research journey: from perceptron to the training problem discovery
• Key insight to share: The difference between architectural solutions and practical implementation methods
• Community contribution: Create a tutorial showing the evolution from perceptron → MLP concept → backpropagation training
• Week 15-16: Mentor a newcomer through this same discovery process
• The flywheel continues: Your mentee might explore modern training methods like Adam optimization or discover new architectural limitations!
• Next cycle: You're now ready to tackle papers that built on backpropagation, like LeNet-5 (1998) or explore other "known but untrainable" architectures

The Research Engineering Flywheel

The perceptron example above demonstrates how research engineering creates a continuous cycle of innovation:

🔄 The Flywheel Process:

1. Literature Review → Understand existing knowledge
2. Implementation → Discover limitations through hands-on work
3. Original Research → Address the limitations you found
4. Community Sharing → Teach others, who find new limitations
5. Repeat → Each cycle builds on the previous, driving innovation forward

This Same Pattern Works in Any Field:

Biology Example - DNA Structure Research:

• Phase 1: Literature review of Watson & Crick's DNA structure paper (1953)
• Phase 2: Research Engineering Application:
  • Experiment Tracking: Log X-ray crystallography parameters and results
  • Baseline Comparison: Compare new structural models against existing proposals
  • Reproducible Methods: Document exact crystallography conditions and analysis procedures
• Phase 3: Systematic Experimentation:
  • Ablation Studies: Test impact of different crystallography angles and conditions
  • Statistical Analysis: Quantify model fit quality and measurement uncertainties
  • Error Analysis: Identify which structural features are most/least reliable
• Phase 4: Share validated structural analysis methodology with the community

Psychology Example - Memory Research:

• Phase 1: Review Miller's "Magical Number Seven" memory paper (1956)
• Phase 2: Research Engineering Application:
  • Experiment Tracking: Log participant demographics, test conditions, response times
  • Baseline Comparison: Compare against chance performance and existing memory tests
  • Reproducible Protocol: Standardize testing procedures and environmental conditions
• Phase 3: Systematic Experimentation:
  • Ablation Studies: Test impact of stimulus type, presentation time, interference conditions
  • Statistical Analysis: Power analysis, effect sizes, confidence intervals for memory capacity
  • Bias Detection: Control for participant selection, experimenter effects, cultural factors
• Phase 4: Publish validated memory testing protocols and mentor others in experimental design

Economics Example - Market Behavior Research:

• Phase 1: Study Akerlof's "Market for Lemons" paper (1970)
• Phase 2: Research Engineering Application:
  • Experiment Tracking: Log market simulation parameters, participant behaviors, outcomes
  • Baseline Comparison: Compare against perfect information market models
  • Reproducible Setup: Document exact experimental economics protocols and incentive structures
• Phase 3: Systematic Experimentation:
  • Ablation Studies: Test impact of information asymmetry levels, market size, reputation systems
  • Statistical Analysis: Significance testing of market efficiency measures and behavioral patterns
  • Error Analysis: Identify which market conditions lead to failure of theoretical predictions
• Phase 4: Share validated experimental economics methodology and behavioral insights

Key Insight: Every field has foundational papers with limitations that drove further innovation. By experiencing this process yourself, you learn how research really works!

Critical Research Lesson from the Perceptron Journey: Sometimes the biggest breakthroughs aren't new architectures or theories, but practical methods to implement existing ideas. The MLP architecture existed for ~25 years before anyone figured out how to train it effectively. This pattern repeats throughout research history - the concept exists, but the implementation method is the real innovation.

How to Choose Your Starting Paper

Now that you understand the framework and prerequisites, here's how to select your first research paper:

What Makes a Paper Beginner-Friendly:

• Foundational importance - Introduced key concepts still used today
• Simple enough to understand - Clear methodology without requiring advanced background
• Implementable scope - Can be reproduced with available tools and reasonable effort
• Historical significance - Shaped the field's development
• Clear limitations - Has known problems that drove further research

Examples of Great Starting Papers by Field:

• Computer Science: Perceptron (1958), Dijkstra's Algorithm (1959)
• Biology: DNA Structure (1953), Central Dogma (1958)
• Psychology: Classical Conditioning (1927), Cognitive Load Theory (1988)
• Economics: Efficient Market Hypothesis (1970), Game Theory basics (1944)
• Physics: Brownian Motion (1905), Photoelectric Effect (1905)
• Chemistry: Molecular Orbital Theory (1932), Reaction Mechanisms

Your Research Journey Starts Here:

1. Pick a field that genuinely interests you
2. Find a foundational paper using the criteria above
3. Follow the 4-phase framework with your chosen paper
4. Experience the research flywheel as limitations lead to new questions
5. Join the community and share your journey with others

Core Research Engineering Skills You'll Develop

Research Methodology

• Hypothesis Formation: Clear, testable research questions - Research Question Guide
• Experimental Design: Controls, variables, and validation methodology - Experimental Design Principles
• Literature Review: Systematic analysis and synthesis of existing work - Systematic Review Guide
• Research Ethics: Integrity, bias awareness, and responsible practices - Research Ethics Training

Experimental Rigor

• Ablation Studies: Systematic component analysis to understand what matters - Ablation Study Guide
• Baseline Comparisons: Proper benchmarking against established methods - Baseline Methods Guide
• Statistical Analysis: Significance testing, confidence intervals, effect sizes - Statistical Testing Guide
• Error Analysis: Failure mode investigation and edge case understanding - Error Analysis Methods
• Bias Detection: Identifying and mitigating experimental and data biases - Research Bias Guide

Research Engineering Tools

• Experiment Tracking: Systematic logging of parameters, results, and insights - MLflow Tutorial | Weights & Biases Guide
• Version Control: Managing code, data, and model versions for reproducibility - Git Tutorial | DVC Guide
• Reproducible Environments: Containerization and environment documentation - Docker Tutorial | Conda Guide
• Automated Validation: Testing pipelines and continuous integration for research - pytest Guide | GitHub Actions
• Domain-Specific Tools: Frameworks and instruments specific to your field - NumPy | pandas | scikit-learn

Analysis and Communication

• Data Visualization: Effective presentation of research findings - Matplotlib Tutorial | Data Visualization Principles
• Statistical Interpretation: Understanding uncertainty and significance - Statistical Interpretation Guide | P-values Explained
• Research Documentation: Clear, reproducible research reports - Scientific Writing Guide | Jupyter Book
• Peer Collaboration: Working effectively in distributed research teams - Remote Collaboration Guide | Team Science Toolkit
• Knowledge Transfer: Teaching and mentoring others in research practices - Teaching Guide | Mentoring Best Practices

Open Science Practices

• Reproducible Workflows: End-to-end reproducibility protocols - The Turing Way | FAIR Data Principles
• Open Data and Code: Transparent sharing of research assets - Open Data Guide | GitHub for Research
• Community Peer Review: Collaborative validation and feedback - Open Peer Review | PeerJ Guide
• Research Democratization: Making research accessible beyond traditional institutions - Citizen Science Guide | Open Science Framework

Getting Started: Resources and Projects

Essential Learning Resources

Free Online Resources

• ArXiv - Open access research papers across all disciplines
• Google Scholar - Academic search engine and citation tracking
• PLOS ONE - Open access scientific journal
• ResearchGate - Academic social network and paper sharing

Research Tools

• Jupyter Notebooks - Interactive research and analysis environment
• R/RStudio - Statistical computing and data analysis
• Zotero - Reference management and citation tools
• OSF (Open Science Framework) - Research project management and sharing

Hands-on Projects by Experience Level

Beginner Projects (Weeks 1-8)

1. Literature Review: Comprehensive review of a research area
2. Paper Reproduction: Implement and validate published research
3. Dataset Analysis: Explore and analyze open datasets
4. Research Proposal: Design an original research project

Intermediate Projects (Weeks 9-16)

1. Original Research: Conduct novel research in your chosen field
2. Tool Development: Build tools to support research workflows in your domain
3. Collaborative Project: Work with team members on interdisciplinary research
4. Community Contribution: Contribute to open research initiatives

Advanced Projects (Ongoing)

1. Research Leadership: Lead a multi-person research project
2. Publication: Publish research in open access venues
3. Mentorship: Guide newcomers through their research journey
4. Innovation: Develop new methodologies or frameworks

Join the Average Joes Lab Community

Community Resources and Support

• Discord Server: Real-time collaboration, Q&A, and peer support across dedicated channels:
  • #research-engineering-path: Discuss the 4-phase learning journey
  • #paper-selection: Get help choosing your foundational paper
  • #phase-1-foundations through #phase-4-community: Phase-specific support
  • #paper-discussions: Share and analyze research papers across all fields
  • #statistics-help: Statistical analysis and experimental design support
  • #tools-and-setup: Research environment and software help
  • #troubleshooting: Technical problem-solving assistance
  • #study-groups: Form collaborative learning groups
  • #mentorship: Connect with mentors and offer guidance
  • #research-updates: Share progress and celebrate milestones
• GitHub Organization: Open research projects and collaborative code
• Research Papers: Community publications and ongoing findings
• Mentorship Program: Connect with experienced research engineers through dedicated Discord channels
• Monthly Research Meetups: Virtual sessions on latest research and techniques
• Project Collaboration: Team up on meaningful research initiatives via #project-collaboration
• Skill Sharing: Learn from and teach fellow community members
• Open Source Focus: All research is transparent and community-driven

Ready to Start Your Research Engineering Journey?

Get the Template Repository:

🚀 Fork the Research Engineering Starter Template - Complete repository structure with guides for all 4 phases

Immediate Next Steps:

1. Fork the starter template - Get organized structure and guidance
2. Join our Discord community - Connect with fellow researchers
3. Explore ongoing projects - Find collaboration opportunities
4. Read our research papers - See what the community is working on
5. Choose your first project - Start with a literature review or paper reproduction based on your interests
6. Find a mentor - Connect with experienced community members for guidance

Career Opportunities in Research Engineering:

• Research Scientist across various industries and institutions
• Applied Research Engineer in industry R&D departments
• Independent Researcher in citizen science and open research initiatives
• Research & Development Lead at organizations driving innovation
• Open Source Research Contributor to global research projects
• Research Consultant for organizations needing research expertise

Success Stories

Coming soon: Stories from our community members who have successfully transitioned into research engineering roles through our program.

---

"Research is not just for academics. Every curious mind can contribute to human knowledge."

Welcome to Average Joes Lab - where ordinary people do extraordinary research!