# ===================================================================
# MODULE 10, LESSON 7: PUTTING IT ALL TOGETHER – FROM DIPLOMA TO CAREER
# ===================================================================

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("PUTTING IT ALL TOGETHER – FROM DIPLOMA TO CAREER")
print("="*70)

# ----------------------------------------------------------------
# PART A: SKILLS INVENTORY AND GAP ANALYSIS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Skills Inventory and Gap Analysis")
print("-"*60)

skills_inventory = pd.DataFrame({
    'Skill': [
        'Python',
        'SQL',
        'Statistics',
        'Machine Learning',
        'Deep Learning',
        'NLP / LLMs',
        'Data Engineering',
        'MLOps',
        'Cloud (AWS/Azure/GCP)',
        'Finance Domain',
        'Regulatory Knowledge',
        'Communication',
        'Leadership',
        'Data Visualisation'
    ],
    'Level (1-10)': [8, 7, 7, 8, 5, 4, 5, 4, 5, 7, 5, 6, 4, 7],
    'Target for Next Role': [9, 8, 8, 9, 7, 7, 6, 6, 6, 8, 6, 7, 6, 8],
    'Gap': [1, 1, 1, 1, 2, 3, 1, 2, 1, 1, 1, 1, 2, 1],
    'Priority': ['High', 'Medium', 'High', 'High', 'Medium', 'High', 'Medium', 'Medium', 'Medium', 'High', 'Medium', 'High', 'High', 'Medium']
})

print("Skills Inventory and Gap Analysis:")
print(skills_inventory.to_string(index=False))

# ----------------------------------------------------------------
# PART B: JOB APPLICATION TRACKER
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Job Application Tracker")
print("-"*60)

job_applications = pd.DataFrame({
    'Company': [
        'JPMorgan Chase',
        'Goldman Sachs',
        'BlackRock',
        'Stripe',
        'Visa',
        'Deloitte',
        'Mastercard',
        'HSBC'
    ],
    'Role': [
        'Data Scientist - Credit Risk',
        'Quantitative Researcher',
        'Data Scientist - Investment',
        'Machine Learning Engineer',
        'Data Scientist - Fraud',
        'Risk Analytics Consultant',
        'Analytics Manager',
        'Risk Modeller'
    ],
    'Location': [
        'New York',
        'London',
        'New York',
        'San Francisco',
        'New York',
        'London',
        'New York',
        'Hong Kong'
    ],
    'Status': [
        'Interested',
        'Applied',
        'Applied',
        'Interested',
        'Applied',
        'Interested',
        'Applied',
        'Interested'
    ],
    'Application Date': [
        None,
        '2024-06-10',
        '2024-06-12',
        None,
        '2024-06-15',
        None,
        '2024-06-14',
        None
    ],
    'Notes': [
        'Good fit for skills; reach out to network',
        'Technical interview scheduled',
        'Follow up next week',
        'Need to learn more about payments domain',
        'Case study round next week',
        'Use contacts in consulting practice',
        'Ask about hybrid working policy',
        'International opportunity; visa sponsorship'
    ]
})

print("Job Application Tracker:")
print(job_applications.to_string(index=False))

# ----------------------------------------------------------------
# PART C: NETWORKING PLAN
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Networking Plan")
print("-"*60)

networking_targets = pd.DataFrame({
    'Target': [
        'Senior Data Scientist at JPMorgan',
        'Director at BlackRock',
        'ML Engineer at Stripe',
        'Risk Analytics Lead at Deloitte',
        'Data Science Manager at Visa',
        'VP of Analytics at Mastercard',
        'Quant Researcher at Goldman Sachs',
        'Head of Data Science at HSBC'
    ],
    'Connection Status': [
        '1st Connection',
        '2nd Connection',
        'No Connection',
        '1st Connection',
        '2nd Connection',
        'No Connection',
        '1st Connection',
        'No Connection'
    ],
    'Approach': [
        'DM about their work in credit risk',
        'Request introduction via mutual contact',
        'Engage with their content; request introduction',
        'DM about consulting life',
        'Request introduction via mutual contact',
        'Engage with their content; cold outreach',
        'DM about quant research',
        'Engage with content; request introduction'
    ],
    'Priority': [
        'High',
        'High',
        'Medium',
        'Medium',
        'High',
        'Medium',
        'High',
        'Low'
    ]
})

print("Networking Plan:")
print(networking_targets.to_string(index=False))

# ----------------------------------------------------------------
# PART D: INTERVIEW PREPARATION CHECKLIST
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Interview Preparation Checklist")
print("-"*60)

interview_prep = {
    "Technical Questions": [
        "Explain the bias-variance trade-off.",
        "What is cross-validation and why is it important?",
        "How do you handle imbalanced data?",
        "Explain the differences between L1 and L2 regularisation.",
        "What is overfitting and how do you prevent it?",
        "When would you use XGBoost over Random Forest?",
        "What is Expected Loss and how do you calculate it?",
        "Explain the difference between VaR and Expected Shortfall.",
        "What is SHAP and why is it important?",
        "How would you approach building a credit scoring model?"
    ],
    "Coding Questions": [
        "Implement a logistic regression from scratch.",
        "Write a function to calculate AUC.",
        "Implement a cross-validation loop.",
        "Write a SQL query to calculate default rates by segment.",
        "Implement a function to handle missing data."
    ],
    "Case Study Questions": [
        "Design a fraud detection system.",
        "Build a loan pricing model.",
        "Optimise a marketing campaign using data.",
        "Predict customer churn for a bank.",
        "Design a stress testing framework."
    ],
    "Behavioural Questions": [
        "Tell me about a time you solved a difficult problem.",
        "How do you handle disagreements with stakeholders?",
        "Describe a project you led end-to-end.",
        "Tell me about a failure and what you learned.",
        "How do you prioritise competing demands?"
    ]
}

print("Interview Preparation Checklist:")
for category, questions in interview_prep.items():
    print(f"\n{category}:")
    for question in questions[:5]:  # Show first 5 for brevity
        print(f"  • {question}")
    if len(questions) > 5:
        print(f"  ... and {len(questions) - 5} more")

# ----------------------------------------------------------------
# PART E: RESUME KEY ACHIEVEMENTS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Resume Key Achievements")
print("-"*60)

achievements = [
    "Built an AI-powered lending platform that reduced loan approval time from 7 days to 3.2 minutes (97% reduction).",
    "Developed an XGBoost model with AUC 0.85, KS 0.42, exceeding regulatory requirements.",
    "Achieved 85% automation rate for loan applications, reducing operational costs by 35%.",
    "Delivered a 575% ROI over 3 years through reduced defaults, cost savings, and revenue uplift.",
    "Led a cross-functional team of 12 people across data science, engineering, and business units.",
    "Implemented model governance framework compliant with SR 11-7 and Fair Lending requirements.",
    "Deployed models with MLOps pipelines, achieving 99.9% uptime and real-time monitoring.",
    "Achieved 92% user adoption through comprehensive change management and training.",
    "Published research on explainable AI for credit scoring.",
    "Mentored 5 junior data scientists, 3 of whom were promoted within 12 months."
]

print("Resume Key Achievements (Quantifiable):")
for i, achievement in enumerate(achievements, 1):
    print(f"{i}. {achievement}")

# ----------------------------------------------------------------
# PART F: PERSONAL NARRATIVE
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Personal Narrative")
print("-"*60)

narrative_template = """
--- PERSONAL NARRATIVE ---

1. WHO I AM:
   ____________________________________________________________

2. WHAT I DO:
   ____________________________________________________________

3. WHY IT MATTERS:
   ____________________________________________________________

4. WHAT I'VE ACHIEVED:
   ____________________________________________________________

5. WHAT I'M LOOKING FOR:
   ____________________________________________________________

6. WHAT MAKES ME UNIQUE:
   ____________________________________________________________
"""

print("Personal Narrative Template:")
print(narrative_template)

# Example narrative
example_narrative = """
--- EXAMPLE PERSONAL NARRATIVE ---

1. WHO I AM:
   I am a data scientist who is passionate about using AI to make financial services more inclusive, fair, and sustainable.

2. WHAT I DO:
   I build and deploy machine learning models that help banks make better lending decisions, reduce risk, and improve customer outcomes.

3. WHY IT MATTERS:
   Financial decisions have a profound impact on people's lives. My work ensures that these decisions are accurate, fair, and transparent.

4. WHAT I'VE ACHIEVED:
   I built an AI-powered lending platform that reduced approval times by 97%, achieved 85% automation, and delivered a 575% ROI.

5. WHAT I'M LOOKING FOR:
   A leadership role in data science at a forward-thinking financial institution where I can continue to drive innovation and impact.

6. WHAT MAKES ME UNIQUE:
   I combine deep technical expertise with a strong ethical foundation and a commitment to responsible AI. I've successfully led cross-functional teams and driven organisational change.
"""

print("\nExample Personal Narrative:")
print(example_narrative)

# ----------------------------------------------------------------
# PART G: ACTION PLAN SUMMARY
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART G: Action Plan Summary")
print("-"*60)

action_plan = {
    "This Week": [
        "✅ Complete skills gap analysis.",
        "✅ Update LinkedIn profile with capstone project.",
        "✅ Apply to 5 target roles.",
        "✅ Reach out to 3 networking contacts."
    ],
    "This Month": [
        "✅ Complete interview preparation.",
        "✅ Schedule informational interviews.",
        "✅ Build a portfolio website.",
        "✅ Write 2 blog posts."
    ],
    "This Quarter": [
        "✅ Secure interviews at 3 target companies.",
        "✅ Complete 3 case studies or projects.",
        "✅ Get 2 professional certifications.",
        "✅ Build a network of 100+ professionals."
    ],
    "This Year": [
        "✅ Land a data science role in financial services.",
        "✅ Deliver impact in the first 90 days.",
        "✅ Contribute to 2 open-source projects.",
        "✅ Present at 1 conference or meetup."
    ]
}

for period, actions in action_plan.items():
    print(f"\n{period}:")
    for action in actions:
        print(f"  {action}")

# ----------------------------------------------------------------
# PART H: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART H: Summary and Recommendations")
print("="*70)

print("""
From Diploma to Career – Key Takeaways:

1. Consolidate your learning: inventory your skills and identify gaps.
2. Leverage your capstone project as a showcase of your capabilities.
3. Develop a job search strategy: target roles, companies, and build your network.
4. Prepare for interviews: technical, case study, and behavioural.
5. Create a personal narrative that tells your story.
6. Take action: apply, network, and follow up.
7. Stay resilient: rejections are part of the process.

Recommendations:
  - Start your job search immediately.
  - Use your capstone project as a conversation starter.
  - Network actively and authentically.
  - Prepare thoroughly for interviews.
  - Stay positive and persistent.
  - Celebrate small wins along the way.
""")

print("="*70)
print("END OF LESSON 7 – MODULE 10")
print("="*70)