I'm on a 37-month journey combining finance, trading, data science, and AI to build intelligent trading systems.

Current Focus (Stage 1 - Months 1-5):
📊 Securing my first Data Analyst role while building a unique skill set at the intersection of finance and technology.

🎯 Rare Skill Combination:

• 📈 Finance Background: Financial services professional + active trader (swing/day trading)
• 🐍 Technical Skills: Python, SQL, Data Analysis, Statistics
• 🤖 AI Focus: Building toward ML & LLM Engineering
• 💰 Domain Expertise: Market structure, trading algorithms, quantitative finance

The Edge: Most data analysts don't understand trading. Most traders can't code. I'm building both.

🌐 GitHub Pages Site - Landing page

📋 View detailed 37-month roadmap

Stage 1 (NOW - Month 5): Data Analyst 🟢
Goal: Land first tech job!

• CS50 (Harvard) - Computer Science fundamentals
• Python for Everybody (University of Michigan)
• Google Data Analytics Professional Certificate
• IBM Data Analyst Professional Certificate (11 courses)
• Statistics with Python (University of Michigan)
• Portfolio: Market data analysis, technical indicators dashboard

Stage 2 (Months 6-15): Data Engineer ⚪
Build production data systems for trading

• AWS, PostgreSQL, PySpark, Airflow
• Real-time trading data pipelines

Stage 3 (Months 16-29): ML Engineer ⚪
Apply machine learning to trading

• ML algorithms, model training, backtesting
• Portfolio optimization with ML

Stage 4 (Months 30-34): LLM Engineer ⚪
Build AI-powered trading systems

• Prompt engineering, RAG, fine-tuning
• Capstone: AI Trading Assistant

Stage 5 (Months 35-37): Senior Engineer ⚪
Thought leadership & monetization

• Production AI systems, consulting, content creation

Current (Stage 1):

Languages:     Python, SQL
Analysis:      Pandas, NumPy, Matplotlib, Seaborn, Plotly
Tools:         Jupyter, Git, VS Code
Databases:     PostgreSQL basics
Platforms:     Kaggle, HackerRank

Learning Next:

Cloud:         AWS (Data Engineer, Solutions Architect)
Big Data:      PySpark, Airflow
ML:            Scikit-learn, TensorFlow, PyTorch
LLM:           LangChain, Vector DBs, Fine-tuning
Trading:       QuantLib, Backtrader, yfinance

Central index of all data analysis & engineering projects

• Purpose: Portfolio hub linking to production-ready projects
• Tech: Python, SQL, pandas, Excel automation
• Includes: Financial data pipelines, market analysis, reconciliation systems
• Explore all projects →

Production ETL pipeline for retirement plan distributions

• Impact: 95% time reduction, $15K annual savings, 98% accuracy
• Tech: Python, pandas, openpyxl, Excel
• Skills: Data cleaning, fuzzy matching, automated reporting
• Status: Production deployment (synthetic demo data available)

Correlating stock volume with news mentions, Wikipedia views, and sentiment

• Tech: Python, SQLite, pandas, yfinance, Wikipedia API
• Skills: Multi-format parsing (CSV, JSON, XML), time-series analysis, visualization
• Stage: Phase 2 - Live API integration
• Note: Capstone for Python for Everybody Specialization

Technology trends analysis using Stack Overflow data (Professional Certificate)

• Tech: Python, SQL, Jupyter, Matplotlib, Cognos
• Focus: Data cleaning, EDA, statistical analysis, dashboard creation
• Status: Completed - IBM Data Analyst Professional Certificate

I'm building in public and documenting everything:

📖 learning-journey - Daily practice, experiments, and enhancements

• Python exercises & experiments
• SQL query practice & optimization
• Trading analysis & research
• Course notes & summaries

Not just homework - I enhance, test, and optimize every exercise!

Completing:

• CS50: Introduction to Computer Science (Harvard) - In Progress
• Python for Everybody Specialization (University of Michigan) - In Progress

Next in line:

• Google Data Analytics Professional Certificate
• IBM Data Analyst Professional Certificate (11 courses)
• Statistics with Python Specialization (University of Michigan)

Planned (2025-2027):

• AWS Certified Data Engineer Associate
• TensorFlow Developer Certificate
• Deep Learning Specialization (Andrew Ng)
• 4+ LLM Engineering courses

✅ CS50 Week 0 - Scratch completed
🔄 Python basics - loops, functions, data structures
🔄 SQL fundamentals - SELECT, JOIN, WHERE
📝 Setting up trading data sources
💻 20 min/day practice on Sololearn

"Innovation is the only path for the future. I'm combining 5+ years of finance experience with modern data & AI skills to build something unique."

Building in public: I share my code, struggles, and solutions. Every commit shows learning, not just completion.

Long-term thinking: 37 months to go from Financial Services Professional to Senior LLM Engineer building AI trading systems. One day at a time.

📧 Open to:

• Data Analyst roles (remote, finance/trading preferred)
• Networking with data professionals
• Trading + tech collaborations
• Mentorship opportunities

November 2025: Started learning journey
Target Month 5 (April 2026): Land Data Analyst role
Target Month 15 (Feb 2027): Data Engineer position
Target Month 29 (April 2028): ML Engineer role
Target Month 37 (Dec 2028): LLM Engineer role + AI Trading Assistant live

The Ultimate Goal:

A production-grade AI Trading Assistant powered by LLMs that:

• Analyzes markets in real-time
• Generates trading signals using ML
• Provides natural language insights
• Executes algorithmic strategies
• Learns and adapts continuously

Why This Matters:

• Combines finance domain knowledge with cutting-edge AI
• Solves real problems (trading analysis is time-consuming)
• Rare skill set in the market
• Foundation for consulting/startup opportunities

1. 🤖 [algorithmic-trading-dashboard] - Stage 1: Market analysis with Python
2. 📊 [ibm-data-analyst-capstone] - Professional certification project
3. 📈 [learning-journey] - 37-month public learning documentation
4. 💼 [data-portfolio] - Collection of data analysis projects

• 📈 I've been trading for 10+ years (swing & day trading)
• 🎯 I study 25 hours/week (4:30 AM club member!)
• ♟️ I'm really good at Chess!
• 🤖 Fascinated by how AI is transforming financial markets
• 📚 Reading: "Machine Learning for Algorithmic Trading" + "Hands-On LLMs"

💡 "From Financial Services Professional to LLM engineering - proving it's never too late to reinvent yourself."

⭐️ Star my repos if you find them useful!
🔔 Follow for daily updates on my journey!

Last updated: Week 1 of 160 (37 months)