# ===================================================================
# MODULE 8, LESSON 8: THE FUTURE OF FINANCIAL DATA SCIENCE
# ===================================================================

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

# Set style
sns.set_style("whitegrid")
np.random.seed(42)

print("="*70)
print("THE FUTURE OF FINANCIAL DATA SCIENCE")
print("="*70)

# ----------------------------------------------------------------
# PART A: TREND FORECASTING
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Trend Impact and Maturity Forecast")
print("-"*60)

trends = pd.DataFrame({
    'Trend': [
        'Generative AI / LLMs',
        'Quantum Computing',
        'Federated Learning',
        'Synthetic Data',
        'Explainable AI',
        'Edge AI',
        'Digital Twins',
        'Web3 / DeFi',
        'Automated ML',
        'Neural Symbolic AI'
    ],
    'Impact (1-10)': [10, 8, 7, 8, 9, 6, 7, 6, 7, 6],
    'Maturity (1-10)': [6, 2, 4, 5, 6, 5, 4, 3, 7, 3],
    'Timeline to Scale (years)': [2, 8, 4, 3, 3, 4, 5, 6, 2, 7],
    'Regulatory Risk (1-10)': [8, 3, 5, 4, 6, 3, 4, 7, 3, 5],
    'Investment Need ($M)': [5, 20, 8, 6, 4, 7, 10, 12, 3, 15]
})

print("Technology Trends in Finance:")
print(trends.to_string(index=False))

# Visualise trends
fig, axes = plt.subplots(1, 2, figsize=(14, 6))

# Impact vs Maturity
ax = axes[0]
scatter = ax.scatter(trends['Maturity'], trends['Impact'], 
                     s=trends['Investment Need'] * 50,
                     c=trends['Regulatory Risk'], cmap='RdYlGn', alpha=0.7)
ax.set_xlabel('Maturity')
ax.set_ylabel('Impact')
ax.set_title('Trend Impact vs Maturity (size = investment need)')
for i, row in trends.iterrows():
    ax.annotate(row['Trend'], (row['Maturity'] + 0.1, row['Impact'] + 0.1), fontsize=8)
plt.colorbar(scatter, ax=ax, label='Regulatory Risk')

# Timeline to scale
ax = axes[1]
ax.barh(trends['Trend'], trends['Timeline to Scale (years)'], 
        color=['green' if t < 4 else 'orange' if t < 6 else 'red' for t in trends['Timeline to Scale (years)']])
ax.set_xlabel('Years to Scale')
ax.set_title('Timeline to Scale')
ax.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.savefig('future_trends.png', dpi=300, bbox_inches='tight')
plt.show()
print("Future trends chart saved as 'future_trends.png'")

# ----------------------------------------------------------------
# PART B: SKILLS FOR THE FUTURE
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Skills for the Future Data Scientist")
print("-"*60)

future_skills = pd.DataFrame({
    'Skill Area': [
        'AI Literacy',
        'Prompt Engineering',
        'Data Storytelling',
        'Ethical AI',
        'Domain Expertise (Finance)',
        'AI Orchestration',
        'Change Management',
        'Strategic Thinking',
        'Quantum Literacy',
        'Regulatory Knowledge'
    ],
    'Current Importance (1-10)': [6, 3, 8, 7, 9, 4, 6, 7, 2, 7],
    'Future Importance (1-10)': [10, 9, 9, 10, 10, 9, 9, 10, 7, 10],
    'Urgency': ['High', 'Medium', 'High', 'High', 'High', 'Medium', 'Medium', 'High', 'Low', 'High']
})

print("Skills for the Future Data Scientist:")
print(future_skills.to_string(index=False))

# Visualise
fig, ax = plt.subplots(figsize=(12, 6))

skills = future_skills['Skill Area'].tolist()
current = future_skills['Current Importance (1-10)'].tolist()
future = future_skills['Future Importance (1-10)'].tolist()

x = np.arange(len(skills))
width = 0.35

ax.barh(x - width/2, current, width, label='Current', color='blue', alpha=0.7)
ax.barh(x + width/2, future, width, label='Future', color='green', alpha=0.7)

ax.set_yticks(x)
ax.set_yticklabels(skills)
ax.set_xlabel('Importance (1-10)')
ax.set_title('Skills Importance: Current vs Future')
ax.legend()
ax.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.savefig('future_skills.png', dpi=300, bbox_inches='tight')
plt.show()
print("Future skills chart saved as 'future_skills.png'")

# ----------------------------------------------------------------
# PART C: CAREER RESILIENCE PLAN
# ----------------------------------------------------------------

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

resilience_plan = {
    "1. Stay Current": {
        "Actions": [
            "Follow industry thought leaders (e.g., Andrew Ng, Yann LeCun, and financial AI leaders).",
            "Subscribe to newsletters (e.g., MIT Tech Review, Financial Times, AI in Finance).",
            "Attend conferences and webinars."
        ]
    },
    "2. Continuous Learning": {
        "Actions": [
            "Dedicate 2-4 hours per week to learning.",
            "Take online courses (Coursera, edX, Udacity).",
            "Read research papers (arXiv, NeurIPS, ICML)."
        ]
    },
    "3. Diversify Skills": {
        "Actions": [
            "Learn adjacent skills (e.g., data engineering, MLOps).",
            "Develop soft skills (communication, leadership).",
            "Understand business and finance deeply."
        ]
    },
    "4. Build a Network": {
        "Actions": [
            "Connect with professionals on LinkedIn.",
            "Join industry groups and communities.",
            "Find a mentor and be a mentor."
        ]
    },
    "5. Stay Agile": {
        "Actions": [
            "Be open to new roles and responsibilities.",
            "Embrace change and uncertainty.",
            "Develop an entrepreneurial mindset."
        ]
    },
    "6. Maintain Well-being": {
        "Actions": [
            "Prioritise health and work-life balance.",
            "Manage stress and avoid burnout.",
            "Practice mindfulness and reflection."
        ]
    }
}

print("Career Resilience Plan:")
for pillar, details in resilience_plan.items():
    print(f"\n{pillar}:")
    for action in details['Actions']:
        print(f"  • {action}")

# ----------------------------------------------------------------
# PART D: ACTION PLAN
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Personal Action Plan")
print("-"*60)

action_plan = {
    "1. Immediate Actions (Next 30 Days)": [
        "Subscribe to 3 financial data science newsletters.",
        "Update LinkedIn and GitHub profiles.",
        "Identify 1 mentor and schedule a meeting.",
        "Complete a short course on a new topic.",
        "Join an online community (e.g., Reddit, Slack)."
    ],
    "2. Short-Term (3-6 months)": [
        "Complete a certification (CFA/FRM/Cloud).",
        "Build a portfolio project in generative AI.",
        "Attend a conference or meetup.",
        "Write a blog post on a financial data science topic.",
        "Learn prompt engineering."
    ],
    "3. Medium-Term (1-2 years)": [
        "Deepen domain expertise in a specific finance area.",
        "Develop leadership and communication skills.",
        "Explore emerging technologies (quantum, Web3).",
        "Build a professional network in financial AI.",
        "Consider a mentorship or coaching program."
    ],
    "4. Long-Term (3-5 years)": [
        "Transition into a leadership role.",
        "Become a thought leader in financial AI.",
        "Contribute to industry standards or research.",
        "Explore entrepreneurial or consulting opportunities.",
        "Build a personal brand as a financial data science expert."
    ]
}

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

# ----------------------------------------------------------------
# PART E: COURSE SUMMARY AND REFLECTION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Course Summary and Reflection")
print("-"*60)

print("""
YOU HAVE COMPLETED THE DIPLOMA IN FINANCIAL DATA ANALYTICS!

SUMMARY OF THE JOURNEY:

Module 1: Financial Data Landscape & Data Warehousing
  - Understanding the data ecosystem in banking.
  - Data warehousing, ETL, governance.

Module 2: EDA & Data Preprocessing
  - Exploratory analysis, feature engineering, time series.

Module 3: Foundational Statistics & Probability
  - Inference, hypothesis testing, MLE, Bayesian.

Module 4: Predictive Modelling & Machine Learning
  - Logistic regression, decision trees, SVM, XGBoost, deep learning.

Module 5: Advanced Risk Analytics
  - VaR, GARCH, model validation, credit risk, operational risk.

Module 6: Advanced Topics
  - NLP, generative AI, XAI, RL, AI governance, blockchain.

Module 7: Emerging Technologies
  - Edge AI, synthetic data, federated learning, digital twins, Web3.

Module 8: Financial Data Science in Practice
  - Team building, project management, MLOps, model governance, communication, leadership, career growth, future trends.

REFLECTION QUESTIONS:

1. What are your top 3 key learnings from this course?

2. Which skills do you need to develop further?

3. What is one project you can apply your new skills to?

4. Who is one person you can share this knowledge with?

5. What is your next career step?
""")

# ----------------------------------------------------------------
# PART F: FINAL RECOMMENDATIONS
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART F: Final Recommendations")
print("="*70)

print("""
FINAL RECOMMENDATIONS FOR YOUR CAREER IN FINANCIAL DATA SCIENCE:

1. Stay Curious
   - The field is evolving rapidly; embrace lifelong learning.

2. Build Your Network
   - Connect with peers, mentors, and industry leaders.

3. Create Your Portfolio
   - Showcase your projects and skills.

4. Develop Domain Expertise
   - Deep financial knowledge is your competitive advantage.

5. Master Communication
   - Translate complex insights into business value.

6. Embrace AI
   - Use AI tools to augment your capabilities.

7. Be Ethical
   - Build responsible AI that serves society.

8. Think Strategically
   - Align your work with business goals.

9. Lead by Example
   - Mentor others and share your knowledge.

10. Stay Resilient
    - Embrace change and uncertainty as opportunities.

THE FUTURE OF FINANCIAL DATA SCIENCE IS BRIGHT.
YOU ARE NOW EQUIPPED TO BE A LEADER IN THIS FIELD.

GOOD LUCK ON YOUR JOURNEY!
""")

print("="*70)
print("END OF LESSON 8 – MODULE 8")
print("="*70)
print("END OF THE DIPLOMA IN FINANCIAL DATA ANALYTICS")
print("="*70)