# ===================================================================
# MODULE 8, LESSON 7: PROFESSIONAL DEVELOPMENT AND CAREER GROWTH
# ===================================================================

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("PROFESSIONAL DEVELOPMENT AND CAREER GROWTH")
print("="*70)

# ----------------------------------------------------------------
# PART A: CAREER ROADMAP PLANNER
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Career Roadmap Planner")
print("-"*60)

def create_career_roadmap():
    """Generate a personal career roadmap."""
    roadmap = {
        "Short-Term (6-12 months)": {
            "Technical Goals": [
                "Complete a course on advanced ML (e.g., deep learning).",
                "Build a portfolio project (e.g., fraud detection model).",
                "Get certified in cloud computing (AWS/Azure)."
            ],
            "Professional Goals": [
                "Update LinkedIn and GitHub profile.",
                "Attend 2-3 conferences/meetups.",
                "Write 2-3 blog posts on financial data science."
            ],
            "Learning Objectives": [
                "Improve Python coding skills.",
                "Learn a new ML framework (e.g., PyTorch).",
                "Deepen financial domain knowledge."
            ]
        },
        "Medium-Term (1-2 years)": {
            "Technical Goals": [
                "Master MLOps and model deployment.",
                "Contribute to an open-source project.",
                "Complete a certification (CFA/FRM)."
            ],
            "Professional Goals": [
                "Start mentoring junior team members.",
                "Lead a project end-to-end.",
                "Build a professional network."
            ],
            "Learning Objectives": [
                "Learn about alternative data sources.",
                "Explore advanced NLP techniques.",
                "Understand regulatory frameworks in AI."
            ]
        },
        "Long-Term (3-5 years)": {
            "Technical Goals": [
                "Become a domain expert in a specific area (e.g., credit risk).",
                "Innovate with emerging technologies (e.g., quantum).",
                "Contribute to industry standards."
            ],
            "Professional Goals": [
                "Move into a leadership role.",
                "Build a team and mentor others.",
                "Become a thought leader in financial AI."
            ],
            "Learning Objectives": [
                "Develop strategic thinking skills.",
                "Understand business strategy.",
                "Build a personal brand as an expert."
            ]
        }
    }
    return roadmap

career_roadmap = create_career_roadmap()

for timeline, goals in career_roadmap.items():
    print(f"\n{timeline}:")
    for category, items in goals.items():
        print(f"  {category}:")
        for item in items:
            print(f"    • {item}")

# ----------------------------------------------------------------
# PART B: SKILLS GAP ANALYSIS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Skills Gap Analysis")
print("-"*60)

def skills_gap_analysis(current_skills, target_skills):
    """Analyse gaps between current and target skills."""
    skills = pd.DataFrame({
        'Skill': list(set(current_skills.keys()) | set(target_skills.keys())),
        'Current': [current_skills.get(s, 0) for s in list(set(current_skills.keys()) | set(target_skills.keys()))],
        'Target': [target_skills.get(s, 5) for s in list(set(current_skills.keys()) | set(target_skills.keys()))]
    })
    skills['Gap'] = skills['Target'] - skills['Current']
    skills = skills.sort_values('Gap', ascending=False)
    return skills

# Example current skills (1-10)
current_skills = {
    'Python': 8, 'SQL': 9, 'Statistics': 7, 'Machine Learning': 8,
    'Deep Learning': 5, 'Data Engineering': 6, 'MLOps': 4,
    'Cloud': 4, 'Finance Domain': 7, 'Regulatory': 5,
    'Communication': 7, 'Leadership': 4
}

# Target skills for a Senior Data Scientist
target_skills = {
    'Python': 9, 'SQL': 9, 'Statistics': 8, 'Machine Learning': 9,
    'Deep Learning': 8, 'Data Engineering': 7, 'MLOps': 7,
    'Cloud': 7, 'Finance Domain': 8, 'Regulatory': 7,
    'Communication': 8, 'Leadership': 7
}

gap_analysis = skills_gap_analysis(current_skills, target_skills)

print("Skills Gap Analysis (Current vs Target):")
print(gap_analysis.to_string(index=False))

# Visualise
fig, ax = plt.subplots(figsize=(12, 6))
skills = gap_analysis['Skill'].tolist()
current = gap_analysis['Current'].tolist()
target = gap_analysis['Target'].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, target, width, label='Target', color='green', alpha=0.7)

ax.set_yticks(x)
ax.set_yticklabels(skills)
ax.set_xlabel('Skill Level (1-10)')
ax.set_title('Skills Gap Analysis')
ax.legend()
ax.grid(True, alpha=0.3, axis='x')

plt.tight_layout()
plt.savefig('skills_gap_analysis.png', dpi=300, bbox_inches='tight')
plt.show()
print("Skills gap analysis chart saved as 'skills_gap_analysis.png'")

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

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

projects = pd.DataFrame({
    'Project': [
        'Loan Default Prediction',
        'Fraud Detection System',
        'Stock Price Forecasting',
        'Customer Segmentation',
        'Financial Sentiment Analysis',
        'Credit Card Churn Prediction',
        'VaR Calculation',
        'ESG Analytics Dashboard'
    ],
    'Status': ['Completed', 'Completed', 'In Progress', 'Planned', 
               'In Progress', 'Planned', 'Planned', 'Planned'],
    'Skills Demonstrated': [
        'ML, Feature Engineering, Validation',
        'ML, MLOps, API Deployment',
        'Time Series, LSTM, Prophet',
        'Unsupervised Learning, PCA',
        'NLP, FinBERT, Sentiment Analysis',
        'Classification, Business Impact',
        'Risk Analytics, Monte Carlo',
        'ESG Data, Visualisation'
    ],
    'Start Date': [
        '2024-01-15', '2024-03-01', '2024-07-01', '2024-10-01',
        '2024-06-01', '2024-11-01', '2024-12-01', '2025-01-01'
    ],
    'Completed Date': [
        '2024-03-15', '2024-05-15', None, None,
        None, None, None, None
    ],
    'GitHub Link': [
        'github.com/project1', 'github.com/project2', None, None,
        None, None, None, None
    ],
    'Blog Post': [
        'Yes', 'Yes', 'No', 'No',
        'No', 'No', 'No', 'No'
    ]
})

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

# ----------------------------------------------------------------
# PART D: CERTIFICATION PLANNER
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Certification Planner")
print("-"*60)

certifications = pd.DataFrame({
    'Certification': ['CFA Level I', 'FRM Part I', 'AWS Certified ML', 'Google Cloud ML', 
                      'Data Science Cert (Coursera)', 'SnowPro Core'],
    'Status': ['Planned', 'Planned', 'Completed', 'In Progress', 'Planned', 'Not Planned'],
    'Estimated Duration': ['6 months', '4 months', '2 months', '3 months', '4 months', '1 month'],
    'Target Date': ['2025-06-30', '2025-03-31', '2024-12-15', '2025-03-15', '2025-09-30', '2025-12-31'],
    'Priority': ['High', 'High', 'Medium', 'Medium', 'Low', 'Low']
})

print("Certification Planner:")
print(certifications.to_string(index=False))

# ----------------------------------------------------------------
# PART E: NETWORKING TRACKER
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Networking Tracker")
print("-"*60)

networking = pd.DataFrame({
    'Event/Activity': ['ODSC Conference', 'PyData Meetup', 'AI in Finance Webinar', 
                       'Kaggle Competition', 'Data Science Panel', 'LinkedIn Networking'],
    'Date': ['2024-10-15', '2024-11-01', '2024-11-15', '2024-12-01', '2025-01-15', 'Ongoing'],
    'Type': ['Conference', 'Meetup', 'Webinar', 'Competition', 'Panel', 'Online'],
    'Status': ['Registered', 'Planned', 'Planned', 'In Progress', 'Planned', 'Active'],
    'Networking Goal': ['Meet 10 new people', 'Connect with 3 speakers', 'Connect with 5 attendees', 
                        'Top 20% finish', 'Ask 2 questions', 'Connect with 50 new contacts']
})

print("Networking Tracker:")
print(networking.to_string(index=False))

# ----------------------------------------------------------------
# PART F: WORK-LIFE BALANCE AND BURNOUT PREVENTION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Work-Life Balance and Burnout Prevention")
print("-"*60)

print("""
Work-Life Balance in Data Science:

1. Set Boundaries:
   - Define work hours and stick to them.
   - Avoid checking emails and Slack after hours.
   - Take regular breaks during the day.

2. Prioritise Health:
   - Regular exercise (at least 30 min/day).
   - Healthy eating and hydration.
   - Adequate sleep (7-8 hours/night).

3. Manage Workload:
   - Prioritise tasks (urgent vs important).
   - Learn to say 'no' to non-essential requests.
   - Delegate when possible.

4. Take Time Off:
   - Use vacation days.
   - Take mental health days when needed.
   - Unplug completely during holidays.

5. Continuous Learning:
   - Balance work and learning.
   - Schedule learning time, don't overdo it.
   - Learn at a sustainable pace.

6. Social Connection:
   - Build relationships with colleagues.
   - Connect with peers outside of work.
   - Engage in hobbies and interests.

Burnout Warning Signs:
  - Chronic fatigue and exhaustion.
  - Loss of motivation and passion.
  - Difficulty concentrating.
  - Increased cynicism or negativity.
  - Physical symptoms (headaches, sleep issues).
  - Withdrawal from social connections.

If you experience these signs, take action:
  - Talk to your manager or HR.
  - Take a break or holiday.
  - Seek professional help if needed.
  - Re-evaluate your workload and priorities.
""")

# Burnout risk assessment
burnout_risk = pd.DataFrame({
    'Indicator': [
        'Working more than 50 hours/week',
        'Checking work emails after hours',
        'Missing meals or breaks',
        'Feeling overwhelmed by workload',
        'Lack of work-life boundaries',
        'Decreased job satisfaction',
        'Physical symptoms (fatigue, headaches)',
        'Withdrawing from colleagues'
    ],
    'Frequency': ['Often', 'Sometimes', 'Sometimes', 'Often', 'Sometimes', 'Sometimes', 'Rarely', 'Rarely'],
    'Risk Level': ['High', 'Medium', 'Medium', 'High', 'Medium', 'Medium', 'Low', 'Low']
})

print("\nBurnout Risk Self-Assessment:")
print(burnout_risk.to_string(index=False))

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

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

print("""
Professional Development and Career Growth – Key Takeaways:

1. Career Landscape: Diverse sectors and roles in financial data science.
2. Skills: Continuous learning of technical and soft skills.
3. Certifications: CFA, FRM, Cloud, and AI/ML certifications add value.
4. Portfolio: GitHub, projects, blog, and speaking engagements.
5. Networking: Conferences, meetups, online communities.
6. Professional Development: Structured plan for 1, 2, and 5 years.
7. Work-Life Balance: Prevent burnout; prioritise health and well-being.
8. Career Progression: Technical, management, hybrid, consulting, or entrepreneurial paths.

Recommendations:
  - Create a personal development plan with clear goals.
  - Build and maintain a portfolio of projects.
  - Network actively and build your professional brand.
  - Pursue relevant certifications.
  - Seek mentorship and mentorship opportunities.
  - Prioritise work-life balance and well-being.
  - Stay curious and continuously learn.
""")

print("="*70)
print("END OF LESSON 7 – MODULE 8")
print("="*70)