# ===================================================================
# MODULE 10, LESSON 2: BUILDING YOUR CAREER AND PERSONAL BRAND
# ===================================================================

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')

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

print("="*70)
print("BUILDING YOUR CAREER AND PERSONAL BRAND")
print("="*70)

# ----------------------------------------------------------------
# PART A: PERSONAL BRAND POSITIONING
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Personal Brand Positioning")
print("-"*60)

brand_positioning = {
    "Purpose Statement": "I help banks leverage data and AI to make better lending decisions, reduce risk, and improve customer outcomes.",
    
    "Core Values": [
        "Integrity – Building trustworthy, explainable AI.",
        "Excellence – Delivering high-quality, robust solutions.",
        "Impact – Focusing on outcomes that matter.",
        "Collaboration – Working across teams to achieve shared goals.",
        "Innovation – Embracing new technologies and approaches."
    ],
    
    "Unique Value Proposition": "I combine deep financial domain knowledge with cutting-edge machine learning to build models that are both high-performing and regulatory-compliant.",
    
    "Target Audience": [
        "Heads of Risk and Credit at banks",
        "Data Science leaders in financial services",
        "Fintech product managers",
        "Recruiters and hiring managers",
        "Peers in the data science community"
    ],
    
    "Content Pillars": [
        "Credit Risk and Lending Analytics",
        "Machine Learning in Finance",
        "AI Governance and Explainability",
        "Career Development for Data Scientists"
    ]
}

print("Personal Brand Positioning:")
for key, value in brand_positioning.items():
    if isinstance(value, list):
        print(f"\n{key}:")
        for item in value:
            print(f"  • {item}")
    else:
        print(f"\n{key}: {value}")

# ----------------------------------------------------------------
# PART B: LINKEDIN PROFILE CHECKLIST
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: LinkedIn Profile Checklist")
print("-"*60)

linkedin_checklist = pd.DataFrame({
    'Section': ['Photo', 'Headline', 'Summary', 'Experience', 'Education', 
                'Skills', 'Recommendations', 'Posts', 'Projects', 'Open to Work'],
    'Status': ['✅ Complete', '✅ Complete', '🟡 In Progress', '✅ Complete', 
               '✅ Complete', '🟡 In Progress', '❌ Not Started', '🟡 In Progress', 
               '❌ Not Started', '❌ Not Started'],
    'Priority': ['High', 'High', 'High', 'High', 'Medium', 
                 'High', 'Medium', 'Medium', 'High', 'Medium'],
    'Action': [
        'Professional headshot updated',
        'Keyword-rich headline with value proposition',
        'Story-driven summary with achievements',
        'Detailed descriptions with metrics',
        'All degrees and certifications added',
        'Top 10 skills endorsed',
        'Request 3 recommendations',
        'Post weekly insights',
        'Add 3 key projects',
        'Turn on Open to Work'
    ]
})

print("LinkedIn Profile Checklist:")
print(linkedin_checklist.to_string(index=False))

# ----------------------------------------------------------------
# PART C: PORTFOLIO PROJECT TRACKER
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Portfolio Project Tracker")
print("-"*60)

portfolio_projects = pd.DataFrame({
    'Project': [
        'Credit Default Prediction',
        'Fraud Detection System',
        'Stock Price Forecasting',
        'Customer Segmentation',
        'Financial Sentiment Analysis',
        'Portfolio Risk Dashboard',
        'ESG Analytics Dashboard'
    ],
    'Status': ['Completed', 'Completed', 'In Progress', 'Planned', 
               'In Progress', 'Planned', 'Planned'],
    'Technologies': [
        'Python, XGBoost, SHAP',
        'Python, MLflow, Docker, Flask',
        'Python, LSTM, Prophet',
        'Python, Scikit-learn, PCA',
        'Python, FinBERT, Transformers',
        'Python, Flask, Plotly Dash',
        'Python, Plotly, Pandas'
    ],
    'GitHub Link': ['✅', '✅', '🟡', '❌', '🟡', '❌', '❌'],
    'Blog Post': ['✅', '✅', '❌', '❌', '❌', '❌', '❌'],
    'Priority': ['High', 'High', 'High', 'Medium', 'High', 'Medium', 'Low']
})

print("Portfolio Project Tracker:")
print(portfolio_projects.to_string(index=False))

# ----------------------------------------------------------------
# PART D: NETWORKING PLAN
# ----------------------------------------------------------------

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

networking_plan = {
    "Weekly": [
        "Connect with 2-3 new people on LinkedIn.",
        "Engage with 5 posts (comment, like, share).",
        "Share 1 insight or article."
    ],
    "Monthly": [
        "Attend 1 virtual meetup or webinar.",
        "Send 1 message to a mentor or peer.",
        "Participate in 1 online discussion (Kaggle, Reddit)."
    ],
    "Quarterly": [
        "Attend 1 conference or in-person event.",
        "Have 1 informational interview.",
        "Update LinkedIn and GitHub profiles."
    ],
    "Annually": [
        "Review and refresh your personal brand.",
        "Set new networking goals.",
        "Evaluate your network map."
    ]
}

print("Networking Plan:")
for period, actions in networking_plan.items():
    print(f"\n{period}:")
    for action in actions:
        print(f"  • {action}")

# ----------------------------------------------------------------
# PART E: INTERVIEW PREPARATION CHECKLIST
# ----------------------------------------------------------------

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

interview_prep = {
    "Technical Skills": [
        "Review algorithms and data structures.",
        "Practice SQL (joins, window functions, performance).",
        "Review machine learning fundamentals (supervised, unsupervised).",
        "Practice building models (end-to-end).",
        "Review model evaluation metrics (AUC, KS, calibration).",
        "Understand deep learning basics."
    ],
    "Finance Domain": [
        "Review financial products and markets.",
        "Understand credit risk fundamentals.",
        "Know regulatory requirements (SR 11-7, Fair Lending).",
        "Practice risk calculations (VaR, EL, UL).",
        "Understand bank business models."
    ],
    "Case Study": [
        "Practice solving business problems with data.",
        "Structure your approach (problem → data → model → results).",
        "Practice presenting to non-technical audiences.",
        "Use the STAR method for behavioural questions."
    ],
    "Behavioural": [
        "Prepare your story (background, journey, goals).",
        "Practice STAR responses (Situation, Task, Action, Result).",
        "Prepare questions to ask the interviewer.",
        "Research the company and role thoroughly."
    ],
    "Portfolio": [
        "Prepare to walk through your projects.",
        "Explain your technical choices and trade-offs.",
        "Discuss business impact and results.",
        "Be ready to share code and visualisations."
    ]
}

print("Interview Preparation Checklist:")
for category, items in interview_prep.items():
    print(f"\n{category}:")
    for item in items:
        print(f"  • {item}")

# ----------------------------------------------------------------
# PART F: CAREER ROADMAP (5-YEAR PLAN)
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Career Roadmap (5-Year Plan)")
print("-"*60)

career_roadmap = {
    "Year 1-2: Foundation": {
        "Focus": "Build technical expertise and domain knowledge.",
        "Goals": [
            "Master core ML and finance concepts.",
            "Build 3-5 portfolio projects.",
            "Earn 1-2 certifications (CFA/FRM/Cloud).",
            "Network actively."
        ],
        "Target Role": "Senior Data Scientist / Lead Data Scientist"
    },
    "Year 2-3: Leadership": {
        "Focus": "Develop leadership and strategic skills.",
        "Goals": [
            "Lead 2-3 projects end-to-end.",
            "Mentor junior team members.",
            "Publish articles and speak at events.",
            "Build a professional network."
        ],
        "Target Role": "Data Science Manager / Principal Data Scientist"
    },
    "Year 3-5: Thought Leadership": {
        "Focus": "Establish expertise and influence.",
        "Goals": [
            "Become a recognised expert in a domain.",
            "Publish research or a book.",
            "Lead a team and/or function.",
            "Shape industry standards."
        ],
        "Target Role": "Head of Data Science / Director / VP"
    }
}

for period, details in career_roadmap.items():
    print(f"\n{period}:")
    print(f"  Focus: {details['Focus']}")
    print("  Goals:")
    for goal in details['Goals']:
        print(f"    • {goal}")
    print(f"  Target Role: {details['Target Role']}")

# ----------------------------------------------------------------
# PART G: FINAL REFLECTION AND GRADUATION
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART G: Final Reflection and Graduation")
print("="*70)

final_reflection = """
--- DIPLOMA COMPLETION REFLECTION ---

Congratulations on completing the Diploma in Financial Data Analytics!

This diploma has covered:
  - Data Engineering and Warehousing
  - Exploratory Data Analysis
  - Statistical Foundations
  - Predictive Modelling and Machine Learning
  - Risk Analytics (Credit, Market, Operational, ALM)
  - Advanced Topics (NLP, Generative AI, XAI, RL, Blockchain)
  - Emerging Technologies (Edge AI, Synthetic Data, Federated Learning, Web3)
  - Practical Implementation (MLOps, Governance, Leadership)
  - Capstone Project (End-to-End)

You are now equipped with the knowledge and skills to:
  - Lead data science initiatives in banking and finance.
  - Build and deploy robust, regulatory-compliant models.
  - Manage financial risk using quantitative methods.
  - Apply cutting-edge AI and emerging technologies.
  - Lead teams and drive digital transformation.

Your Next Steps:
  1. Apply your skills to real-world problems.
  2. Build your portfolio and personal brand.
  3. Network with the community.
  4. Continuously learn and stay current.
  5. Seek opportunities to lead and mentor.
  6. Share your knowledge with others.

Remember: This is not the end – it's the beginning of your journey.
The field of financial data science is evolving rapidly, and the opportunities are immense.

Stay curious. Stay humble. Stay ambitious.

We look forward to seeing what you will achieve!

--- GRADUATION ---
Module 10 Complete
Diploma Complete
"""

print(final_reflection)

print("="*70)
print("END OF LESSON 2 – MODULE 10")
print("="*70)
print("END OF THE DIPLOMA IN FINANCIAL DATA ANALYTICS")
print("="*70)