--- LEARNING JOURNAL ---

Date: ________________

1. What did I learn today?
   - [Key concept 1]
   - [Key concept 2]
   - [Key concept 3]

2. What questions do I have?
   - [Question 1]
   - [Question 2]

3. How can I apply this?
   - [Application 1]
   - [Application 2]

4. Resources to explore further:
   - [Resource 1]
   - [Resource 2]

5. Reflection:
   - What was the most interesting part?
   - What was challenging?
   - What do I want to learn next?

# ===================================================================
# MODULE 10, LESSON 1: CONTINUOUS LEARNING IN FINANCIAL DATA SCIENCE
# ===================================================================

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("CONTINUOUS LEARNING IN FINANCIAL DATA SCIENCE")
print("="*70)

# ----------------------------------------------------------------
# PART A: PERSONAL LEARNING PLAN TEMPLATE
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: Personal Learning Plan Template")
print("-"*60)

learning_plan = {
    "Vision": "Become a leader in financial data science and AI.",
    "Mission": "Continuously learn and apply cutting-edge techniques to solve financial problems.",
    "Core Values": ["Curiosity", "Excellence", "Collaboration", "Impact"],
    
    "Short-Term Goals (6 months)": [
        "Complete 2 advanced courses (e.g., deep learning, NLP).",
        "Build 1 portfolio project (e.g., credit risk model).",
        "Read 2 books on finance/data science.",
        "Join 1 professional community."
    ],
    
    "Medium-Term Goals (1-2 years)": [
        "Earn a certification (CFA, FRM, or cloud).",
        "Contribute to an open-source project.",
        "Present at 1 conference or meetup.",
        "Mentor 1-2 junior data scientists."
    ],
    
    "Long-Term Goals (3-5 years)": [
        "Lead a data science team.",
        "Develop expertise in an emerging area (e.g., quantum, generative AI).",
        "Build a personal brand as a thought leader.",
        "Publish research or a book."
    ],
    
    "Learning Resources": {
        "Books": [
            "The Elements of Statistical Learning",
            "Advances in Financial Machine Learning",
            "Storytelling with Data"
        ],
        "Courses": [
            "Deep Learning Specialization (DeepLearning.AI)",
            "Financial Engineering and Risk Management (Columbia, Coursera)",
            "Generative AI with LLMs (DeepLearning.AI)"
        ],
        "Podcasts": [
            "Data Skeptic",
            "AI in Finance",
            "Lex Fridman Podcast"
        ],
        "Communities": [
            "Kaggle",
            "Data Science Society",
            "Financial Data Science LinkedIn Group"
        ]
    }
}

print("Personal Learning Plan:")
for category, details in learning_plan.items():
    if isinstance(details, list):
        print(f"\n{category}:")
        for item in details:
            print(f"  • {item}")
    elif isinstance(details, dict):
        print(f"\n{category}:")
        for sub_category, resources in details.items():
            print(f"  {sub_category}:")
            for resource in resources:
                print(f"    - {resource}")
    else:
        print(f"\n{category}: {details}")

# ----------------------------------------------------------------
# PART B: SKILLS EVOLUTION TRACKER
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Skills Evolution Tracker")
print("-"*60)

# Define skills and their importance over time
skills_data = {
    'Skill': ['Python', 'SQL', 'Statistics', 'Machine Learning', 'Deep Learning', 
              'NLP / LLMs', 'Data Engineering', 'MLOps', 'Cloud', 'Finance Domain', 
              'Regulatory', 'Communication', 'Leadership', 'Quantum Computing'],
    'Current Level (1-10)': [8, 7, 7, 8, 5, 4, 5, 4, 5, 7, 5, 6, 4, 2],
    'Target Level (1-10)': [9, 8, 8, 9, 8, 8, 7, 7, 7, 9, 7, 8, 7, 6],
    'Priority (1-10)': [8, 6, 7, 9, 8, 8, 6, 7, 6, 9, 7, 8, 7, 5],
    'Action Plan': [
        'Contribute to open-source; advanced libraries.',
        'Learn window functions; performance tuning.',
        'Bayesian methods; causal inference.',
        'Ensemble methods; model validation.',
        'PyTorch; transformer architectures.',
        'LangChain; RAG; prompt engineering.',
        'Spark; data pipelines; Airflow.',
        'Docker; Kubernetes; CI/CD.',
        'AWS/Azure certifications.',
        'CFA/FRM; financial products.',
        'SR 11-7; Fair Lending; GDPR.',
        'Presentation skills; storytelling.',
        'Mentorship; team leadership.',
        'Quantum computing fundamentals.'
    ]
}

skills_df = pd.DataFrame(skills_data)
print("Skills Tracker:")
print(skills_df.to_string(index=False))

# ----------------------------------------------------------------
# PART C: SKILLS RADAR VISUALISATION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Skills Radar Visualisation")
print("-"*60)

# Create a radar chart
from math import pi

fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={'projection': 'polar'})

skills = skills_df['Skill'].tolist()
current = skills_df['Current Level (1-10)'].tolist()
target = skills_df['Target Level (1-10)'].tolist()

N = len(skills)
angles = [n / float(N) * 2 * pi for n in range(N)]
angles += angles[:1]

current += current[:1]
target += target[:1]

ax.plot(angles, current, 'o-', linewidth=2, label='Current Level', color='blue')
ax.fill(angles, current, alpha=0.1, color='blue')
ax.plot(angles, target, 'o-', linewidth=2, label='Target Level', color='green', linestyle='--')
ax.fill(angles, target, alpha=0.1, color='green')

ax.set_xticks(angles[:-1])
ax.set_xticklabels(skills, size=8)
ax.set_ylim(0, 10)
ax.set_yticks([2, 4, 6, 8, 10])
ax.set_yticklabels(['2', '4', '6', '8', '10'], size=8)
ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.0))
ax.set_title('Skills Radar – Current vs Target', size=14, pad=20)

plt.tight_layout()
plt.savefig('skills_radar.png', dpi=300, bbox_inches='tight')
plt.show()
print("Skills radar saved as 'skills_radar.png'")

# ----------------------------------------------------------------
# PART D: LEARNING RESOURCE RECOMMENDATIONS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Learning Resource Recommendations by Skill")
print("-"*60)

skill_resources = {
    "Python": {
        "Books": ["Python for Data Analysis (McKinney)", "Fluent Python"],
        "Courses": ["Python for Everybody (Coursera)", "DataCamp Python Track"],
        "Practice": ["LeetCode", "HackerRank", "CodeWars"]
    },
    "Machine Learning": {
        "Books": ["Hands-On ML (Géron)", "The Elements of Statistical Learning"],
        "Courses": ["ML Specialization (Coursera)", "Fast.ai"],
        "Practice": ["Kaggle", "UCI ML Repository"]
    },
    "Deep Learning": {
        "Books": ["Deep Learning (Goodfellow)", "Deep Learning with Python (Chollet)"],
        "Courses": ["Deep Learning Specialization (DeepLearning.AI)", "PyTorch for Deep Learning"],
        "Practice": ["Papers with Code", "Hugging Face"]
    },
    "NLP / LLMs": {
        "Books": ["Speech and Language Processing (Jurafsky)", "Natural Language Processing with Transformers"],
        "Courses": ["NLP Specialization (Coursera)", "Generative AI with LLMs"],
        "Practice": ["Hugging Face", "Kaggle NLP Competitions"]
    },
    "Finance Domain": {
        "Books": ["Advances in Financial Machine Learning", "Quantitative Risk Management"],
        "Courses": ["Financial Engineering and Risk Management (Coursera)"],
        "Certifications": ["CFA", "FRM"]
    },
    "MLOps": {
        "Books": ["Machine Learning Engineering (MLE)", "MLOps with Kubernetes"],
        "Courses": ["MLOps (Coursera)", "MLOps for Data Scientists"],
        "Tools": ["MLflow", "Docker", "Kubernetes"]
    },
    "Cloud": {
        "Books": ["AWS Certified Machine Learning Study Guide"],
        "Courses": ["AWS ML Specialization", "Google Cloud ML"],
        "Certifications": ["AWS Certified ML", "Google Professional ML Engineer"]
    },
    "Communication": {
        "Books": ["Storytelling with Data", "The Pyramid Principle"],
        "Courses": ["Business Communication (Coursera)"],
        "Practice": ["Toastmasters", "Blogging"]
    },
    "Leadership": {
        "Books": ["The First 90 Days", "Leaders Eat Last", "Radical Candor"],
        "Courses": ["Leadership (Coursera)", "Harvard Management"],
        "Practice": ["Mentorship", "Team Lead Role"]
    }
}

print("Recommended Resources by Skill Area:")
for skill, resources in skill_resources.items():
    print(f"\n{skill}:")
    for category, items in resources.items():
        print(f"  {category}: {', '.join(items)}")

# ----------------------------------------------------------------
# PART E: PROFESSIONAL DEVELOPMENT PLAN (12 MONTHS)
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: Professional Development Plan (12 Months)")
print("-"*60)

dev_plan = {
    "Month 1-3: Foundation": {
        "Activities": [
            "Complete advanced Python course.",
            "Build first portfolio project.",
            "Read 1 book (e.g., Python for Data Analysis).",
            "Join a data science community."
        ],
        "Outputs": [
            "GitHub repository with project.",
            "Blog post on project."
        ]
    },
    "Month 4-6: Specialisation": {
        "Activities": [
            "Complete deep learning course.",
            "Build project using deep learning (e.g., NLP for finance).",
            "Start certification study (CFA/FRM).",
            "Attend a conference or meetup."
        ],
        "Outputs": [
            "Deep learning project on GitHub.",
            "Conference/meetup network."
        ]
    },
    "Month 7-9: Integration": {
        "Activities": [
            "Learn MLOps and cloud.",
            "Deploy a model to production.",
            "Continue certification study.",
            "Write 2 blog posts."
        ],
        "Outputs": [
            "Deployed model.",
            "Published articles."
        ]
    },
    "Month 10-12: Leadership": {
        "Activities": [
            "Complete certification.",
            "Mentor 1-2 junior colleagues.",
            "Present at a meetup or conference.",
            "Begin planning a personal brand."
        ],
        "Outputs": [
            "Certification achieved.",
            "Presentation delivered.",
            "Mentoring relationships."
        ]
    }
}

for period, details in dev_plan.items():
    print(f"\n{period}:")
    print("  Activities:")
    for activity in details['Activities']:
        print(f"    • {activity}")
    print("  Outputs:")
    for output in details['Outputs']:
        print(f"    • {output}")

# ----------------------------------------------------------------
# PART F: LEARNING JOURNAL TEMPLATE (DOWNLOADABLE)
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: Learning Journal Template")
print("-"*60)

learning_journal_template = """
--- LEARNING JOURNAL ---
Week: _____

Date: _______________

1. What did I learn today/this week?
   - 
   - 
   - 

2. What questions do I have?
   - 
   - 

3. How can I apply this to my work?
   - 
   - 

4. Resources I want to explore:
   - 
   - 

5. Reflection:
   - What was the most interesting part?
   - What was challenging?
   - What do I want to learn next?

6. Next week's learning goal:
   - 
"""

print("Learning Journal Template:")
print(learning_journal_template)

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

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

print("""
Continuous Learning – Key Takeaways:

1. Continuous learning is essential in financial data science.
2. Use the 70-20-10 model: experiential, social, and formal learning.
3. Build a diverse learning portfolio: books, courses, podcasts, blogs.
4. Create a sustainable learning habit with a weekly schedule.
5. Track your skills and progress with a learning journal.
6. Engage with the community: conferences, meetups, online forums.
7. Apply learning to real projects (portfolio, Kaggle, open source).
8. Balance depth (deep expertise) and breadth (awareness of emerging trends).

Recommendations:
  - Create a personal learning plan with clear goals.
  - Set aside dedicated time for learning each week.
  - Track your progress and adjust your plan regularly.
  - Share your learning (blog, presentations, mentoring).
  - Stay curious and embrace the journey.
""")

print("="*70)
print("END OF LESSON 1 – MODULE 10")
print("="*70)