# ===================================================================
# MODULE 10, LESSON 3: ENGAGING WITH THE FINANCIAL DATA SCIENCE COMMUNITY
# ===================================================================

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

print("="*70)
print("ENGAGING WITH THE FINANCIAL DATA SCIENCE COMMUNITY")
print("="*70)

# ----------------------------------------------------------------
# PART A: COMMUNITY ENGAGEMENT PLAN
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Community Engagement Plan")
print("-"*60)

engagement_plan = {
    "Weekly": [
        "Spend 1 hour on Kaggle (competitions or notebooks).",
        "Read and comment on 3 Reddit posts (r/datascience, r/quant).",
        "Engage with 5 LinkedIn posts (like, comment, share).",
        "Check Slack/Discord communities (reply to 2 questions)."
    ],
    "Monthly": [
        "Attend 1 virtual meetup or webinar.",
        "Write 1 blog post or article.",
        "Contribute to an open-source project (1 PR or issue).",
        "Connect with 5 new people on LinkedIn."
    ],
    "Quarterly": [
        "Attend 1 conference (in-person or virtual).",
        "Give a talk or workshop (local meetup, webinar).",
        "Mentor 1 junior colleague or student.",
        "Review your network and identify gaps."
    ],
    "Annually": [
        "Evaluate your community engagement and set new goals.",
        "Identify 1-2 new communities to join.",
        "Reach out to a mentor or thought leader.",
        "Contribute to a significant open-source project."
    ]
}

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

# ----------------------------------------------------------------
# PART B: CONFERENCE AND MEETUP TRACKER
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Conference and Meetup Tracker")
print("-"*60)

conferences = pd.DataFrame({
    'Event': [
        'ODSC London',
        'AI & Data Science in Finance',
        'PyData NYC',
        'NeurIPS',
        'Quant Conference',
        'RiskMinds',
        'FinTech Week',
        'Local Data Science Meetup'
    ],
    'Date': [
        '2024-09-15',
        '2024-10-20',
        '2024-11-10',
        '2024-12-05',
        '2025-01-25',
        '2025-03-15',
        '2025-04-10',
        'Monthly'
    ],
    'Location': [
        'London',
        'NYC',
        'New York',
        'Vancouver',
        'London',
        'London',
        'Global',
        'City Center'
    ],
    'Status': [
        'Registered',
        'Planned',
        'Planned',
        'Interested',
        'Interested',
        'Interested',
        'Interested',
        'Attending'
    ],
    'Focus': [
        'General Data Science',
        'Finance AI',
        'Python Data Science',
        'ML Research',
        'Quantitative Finance',
        'Risk Management',
        'Fintech',
        'Networking'
    ]
})

print("Conference and Meetup Tracker:")
print(conferences.to_string(index=False))

# ----------------------------------------------------------------
# PART C: OPEN SOURCE CONTRIBUTION TRACKER
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Open Source Contribution Tracker")
print("-"*60)

oss_contributions = pd.DataFrame({
    'Project': [
        'Pandas',
        'Scikit-learn',
        'XGBoost',
        'SHAP',
        'MLflow',
        'QuantLib'
    ],
    'Status': [
        'Contributor',
        'User',
        'Contributor',
        'User',
        'User',
        'Interested'
    ],
    'Contributions': [
        'Bug fixes, documentation',
        'None yet',
        'Feature enhancement',
        'None yet',
        'None yet',
        'Planning'
    ],
    'Last Activity': [
        '2024-05-15',
        'N/A',
        '2024-06-01',
        'N/A',
        'N/A',
        'N/A'
    ],
    'Next Step': [
        'Review open issues',
        'Find a good first issue',
        'Submit next PR',
        'Explore issues',
        'Set up local environment',
        'Review documentation'
    ]
})

print("Open Source Contribution Tracker:")
print(oss_contributions.to_string(index=False))

# ----------------------------------------------------------------
# PART D: MENTORSHIP PLAN
# ----------------------------------------------------------------

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

mentorship_plan = {
    "Seeking a Mentor": {
        "Goals": [
            "Gain career guidance in financial data science.",
            "Learn about leadership and strategy.",
            "Get feedback on my portfolio and work."
        ],
        "Target Profile": [
            "Senior data science leader in a bank or fintech.",
            "Someone with 10+ years of experience.",
            "Has a strong reputation in the field."
        ],
        "Next Steps": [
            "Identify 3 potential mentors on LinkedIn.",
            "Reach out with a personalised message.",
            "Request a 15-minute call to discuss mentorship."
        ]
    },
    "Being a Mentor": {
        "Goals": [
            "Give back to the community.",
            "Develop leadership and coaching skills.",
            "Stay connected with emerging talent."
        ],
        "Target Mentees": [
            "Junior data scientists or students.",
            "People from underrepresented groups in tech.",
            "Career changers entering financial data science."
        ],
        "Next Steps": [
            "Offer mentorship through local meetups or online platforms.",
            "Mentor 1 junior colleague at work.",
            "Join a formal mentorship program."
        ]
    }
}

print("Mentorship Plan:")
for category, details in mentorship_plan.items():
    print(f"\n{category}:")
    for key, value in details.items():
        if isinstance(value, list):
            print(f"  {key}:")
            for item in value:
                print(f"    • {item}")
        else:
            print(f"  {key}: {value}")

# ----------------------------------------------------------------
# PART E: ONLINE COMMUNITY CHECKLIST
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Online Community Checklist")
print("-"*60)

community_checklist = pd.DataFrame({
    'Platform': [
        'LinkedIn',
        'GitHub',
        'Kaggle',
        'Reddit',
        'Slack/Discord',
        'Twitter/X',
        'Medium/Blog'
    ],
    'Account Created': ['✅', '✅', '✅', '✅', '✅', '✅', '✅'],
    'Profile Optimised': ['✅', '✅', '🟡', '✅', '✅', '🟡', '🟡'],
    'Active Participation': ['✅', '🟡', '🟡', '🟡', '🟡', '🟡', '🟡'],
    'Next Action': [
        'Post weekly insights',
        'Submit first PR to an OSS project',
        'Complete a competition',
        'Answer 3 questions this week',
        'Introduce yourself in a new channel',
        'Start a thread on financial AI',
        'Write a blog post this month'
    ]
})

print("Online Community Checklist:")
print(community_checklist.to_string(index=False))

# ----------------------------------------------------------------
# PART F: COMMUNITY ENGAGEMENT METRICS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Community Engagement Metrics")
print("-"*60)

metrics = {
    "LinkedIn": {
        "Connections": 500,
        "Posts/Articles": 12,
        "Engagement Rate": "5%",
        "Target": "Grow to 1000 connections, 24 posts/year"
    },
    "GitHub": {
        "Repositories": 8,
        "Stars": 45,
        "Contributions": 12,
        "Target": "10 repos, 100 stars, 50 contributions"
    },
    "Kaggle": {
        "Competitions": 6,
        "Notebooks": 10,
        "Medals": "2 Bronze",
        "Target": "10 competitions, 20 notebooks, 1 Silver"
    },
    "Community": {
        "Meetups Attended": 8,
        "Talks Given": 2,
        "Mentoring Hours": 10,
        "Target": "20 meetups, 5 talks, 50 mentoring hours"
    }
}

print("Community Engagement Metrics:")
for platform, data in metrics.items():
    print(f"\n{platform}:")
    for metric, value in data.items():
        print(f"  {metric}: {value}")

# ----------------------------------------------------------------
# PART G: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

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

print("""
Community Engagement – Key Takeaways:

1. Participating in the community accelerates your learning and career growth.
2. Engage on multiple platforms: LinkedIn, GitHub, Kaggle, Reddit, Slack/Discord.
3. Contribute to open source to build skills and visibility.
4. Attend conferences and meetups to network and learn.
5. Find a mentor to guide your career, and be a mentor to others.
6. Share your knowledge through writing, speaking, and teaching.
7. Track your engagement and set goals for improvement.

Recommendations:
  - Create a community engagement plan with clear goals.
  - Start small and build up your participation.
  - Focus on quality over quantity in your contributions.
  - Be consistent and reliable in your engagement.
  - Give more than you take – share generously.
  - Build genuine relationships, not just connections.
  - Celebrate others' successes and learn from their experiences.
""")

print("="*70)
print("END OF LESSON 3 – MODULE 10")
print("="*70)