Python for Finance
6-8 weeksSkills You'll Build
Python syntax and data typesPandas for financial dataNumPy for calculationsWorking with APIsGit version control
Finance Professional / Analyst → AI Engineer (FinTech Focus)
Finance Professional to AI Engineer Learning Path
Your finance background in data analysis, risk assessment, and quantitative thinking provides a strong foundation for AI engineering. Financial professionals who can build AI systems for trading analysis, risk modeling, and financial document processing are in high demand. Expect 6-10 months of focused learning, with earning potential of $150k-$250k+ in fintech AI roles that value your domain expertise.
6-10 months
Prerequisites
- Data analysis experience (Excel, SQL)
- Quantitative and analytical thinking
- Understanding of financial concepts
- Basic statistics knowledge
- Attention to detail
Your Learning Path
ML Fundamentals for Finance
4-5 weeksSkills You'll Build
Machine learning conceptsTime series basicsClassification and regressionFeature engineeringModel evaluation
LLM APIs & Applications
4-5 weeksSkills You'll Build
OpenAI API integrationClaude API implementationPrompt engineeringStructured output parsingCost management
Document Processing for Finance
4-5 weeksSkills You'll Build
PDF and document extractionFinancial report analysisSEC filing processingStructured data extractionCompliance considerations
RAG for Financial Knowledge
4-5 weeksSkills You'll Build
Vector databases for financial dataDocument chunking strategiesFinancial Q&A systemsHybrid search implementationData security and compliance
Portfolio & FinTech Career
5-7 weeksSkills You'll Build
Building financial AI demosGitHub portfolio setupResume for finance-to-tech transitionInterview preparationFinTech networking