Learning Path

A structured 12-month curriculum designed for your 2-3 hour weekly time commitment.

Your 12-Month Learning Journey

This learning path is designed to progressively build your knowledge and skills, with each phase building on the previous one.

Time Commitment

Each week's content is designed to be completed in 2-3 hours, with approximately 1 hour for theoretical learning, 1 hour for practical application, and 30 minutes for reflection and note-taking.

Phase 1: Foundation Building

Months 1-2: Python programming fundamentals and mathematics refresher

Week 1-2
Python Basics
Topics:
  • Introduction to Python syntax and environment setup
  • Basic data types, variables, and operations
  • Control structures (if-else, loops)
  • Functions and modules
Weekly Time Commitment: 2-3 hours
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Week 3-4
Python for Data Analysis
Topics:
  • Introduction to NumPy for numerical computing
  • Introduction to Pandas for data manipulation
  • Basic data visualization with Matplotlib
  • Working with CSV and Excel files
Weekly Time Commitment: 2-3 hours
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Week 5-6
Mathematics Refresher - Linear Algebra
Topics:
  • Vectors and matrices
  • Matrix operations
  • Eigenvalues and eigenvectors
  • Applications in AI
Weekly Time Commitment: 2-3 hours
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Week 7-8
Mathematics Refresher - Statistics and Probability
Topics:
  • Descriptive statistics
  • Probability distributions
  • Hypothesis testing
  • Bayesian statistics
Weekly Time Commitment: 2-3 hours
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Phase 2: AI Fundamentals

Months 3-4: Core AI concepts and banking applications

Week 9-10
Introduction to AI and Machine Learning
Topics:
  • AI vs. Machine Learning vs. Deep Learning
  • Types of machine learning (supervised, unsupervised, reinforcement)
  • Machine learning workflow
  • Evaluation metrics
Weekly Time Commitment: 2-3 hours
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Week 11-12
Basic Machine Learning Algorithms
Topics:
  • Linear and logistic regression
  • Decision trees and random forests
  • Support vector machines
  • K-means clustering
Weekly Time Commitment: 2-3 hours
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Week 13-14
Banking AI Applications - Part 1
Topics:
  • Fraud detection systems
  • Credit risk assessment
  • Customer segmentation
  • Regulatory compliance
Weekly Time Commitment: 2-3 hours
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Week 15-16
Banking AI Applications - Part 2
Topics:
  • Chatbots and virtual assistants
  • Algorithmic trading
  • Document processing automation
  • Anti-money laundering systems
Weekly Time Commitment: 2-3 hours
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Phase 3: AI Leadership and Technical Oversight

Months 5-6: AI team management and technical evaluation skills

Week 17-18
AI Team Structure and Roles
Topics:
  • Data science team composition
  • Roles and responsibilities
  • Skill requirements and evaluation
  • Team collaboration models
Weekly Time Commitment: 2-3 hours
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Week 19-20
AI Project Management
Topics:
  • AI project lifecycle
  • Agile methodologies for AI projects
  • Managing expectations and timelines
  • Common challenges and solutions
Weekly Time Commitment: 2-3 hours
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Week 21-22
Technical Oversight - Part 1
Topics:
  • Evaluating AI solution proposals
  • Technical risk assessment
  • Data quality and governance
  • Model selection criteria
Weekly Time Commitment: 2-3 hours
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Week 23-24
Technical Oversight - Part 2
Topics:
  • Model performance evaluation
  • Monitoring and maintenance strategies
  • Technical documentation review
  • Translating technical concepts for stakeholders
Weekly Time Commitment: 2-3 hours
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Phase 4: Advanced AI and Strategic Implementation

Months 7-9: Deeper technical knowledge and strategic planning

Week 25-26
Introduction to Deep Learning
Topics:
  • Neural network fundamentals
  • Deep learning architectures
  • Transfer learning
  • Applications in banking
Weekly Time Commitment: 2-3 hours
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Week 27-28
Natural Language Processing for Banking
Topics:
  • Text preprocessing techniques
  • Sentiment analysis
  • Named entity recognition
  • Document classification
Weekly Time Commitment: 2-3 hours
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Week 29-30
Time Series Analysis for Financial Applications
Topics:
  • Time series components
  • Forecasting methods
  • Anomaly detection
  • Market trend analysis
Weekly Time Commitment: 2-3 hours
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Week 31-32
AI System Architecture
Topics:
  • End-to-end AI system components
  • Data pipelines and infrastructure
  • Model deployment options
  • Cloud-based AI services
Weekly Time Commitment: 2-3 hours
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Week 33-36
Strategic AI Implementation
Topics:
  • Aligning AI with business objectives
  • AI roadmap development
  • Change management for AI adoption
  • ROI measurement for AI projects
Weekly Time Commitment: 2-3 hours
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Phase 5: AI Ethics, Governance, and Organizational Transformation

Months 10-12: Responsible AI leadership and organizational scaling

Week 37-40
AI Ethics and Fairness
Topics:
  • Bias detection and mitigation
  • Fairness metrics and evaluation
  • Transparency and explainability
  • Ethical frameworks for AI
Weekly Time Commitment: 2-3 hours
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Week 41-44
AI Governance in Banking
Topics:
  • Regulatory requirements
  • Model risk management
  • Audit and compliance procedures
  • Documentation standards
Weekly Time Commitment: 2-3 hours
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Week 45-48
AI Organizational Transformation
Topics:
  • Scaling AI capabilities
  • AI center of excellence models
  • Managing resistance to AI adoption
  • AI maturity assessment
Weekly Time Commitment: 2-3 hours
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