Career Growth & Specializations

1# ============================================================
2# Career Growth: Portfolio-Grade Python Project Patterns
3# ============================================================
4# This demonstrates professional code structure, typing,
5# documentation, error handling, and testing patterns that
6# employers look for in a portfolio project.
7# ============================================================
8from dataclasses import dataclass, field
9from datetime import datetime, timedelta
10from enum import Enum
11from typing import Optional, Protocol
12from abc import ABC, abstractmethod
13import logging
14
15
16# --- Professional logging setup ---
17logger = logging.getLogger(__name__)
18logging.basicConfig(
19    level=logging.INFO,
20    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
21)
22
23
24# --- Domain models with type hints and validation ---
25class SkillLevel(Enum):
26    BEGINNER = "beginner"
27    INTERMEDIATE = "intermediate"
28    ADVANCED = "advanced"
29    EXPERT = "expert"
30
31
32@dataclass
33class Skill:
34    name: str
35    level: SkillLevel
36    years_experience: float
37
38    def __post_init__(self):
39        if self.years_experience < 0:
40            raise ValueError("Years of experience cannot be negative")
41
42
43@dataclass
44class CareerProfile:
45    name: str
46    title: str
47    specialization: str
48    skills: list[Skill] = field(default_factory=list)
49    goals: list[str] = field(default_factory=list)
50    created_at: datetime = field(default_factory=datetime.now)
51
52    @property
53    def skill_summary(self) -> dict[str, int]:
54        """Count skills by level."""
55        summary = {}
56        for skill in self.skills:
57            level = skill.level.value
58            summary[level] = summary.get(level, 0) + 1
59        return summary
60
61    def add_skill(self, name: str, level: SkillLevel, years: float):
62        """Add a skill with validation."""
63        if any(s.name.lower() == name.lower() for s in self.skills):
64            raise ValueError(f"Skill '{name}' already exists")
65        self.skills.append(Skill(name, level, years))
66        logger.info(f"Added skill: {name} ({level.value})")
67
68    def get_growth_areas(self) -> list[str]:
69        """Identify skills that need improvement."""
70        return [
71            s.name for s in self.skills
72            if s.level in (SkillLevel.BEGINNER, SkillLevel.INTERMEDIATE)
73        ]
74
75
76# --- Strategy Pattern for career path recommendations ---
77class CareerStrategy(Protocol):
78    """Protocol (structural typing) for career strategies."""
79
80    def recommend_skills(self, profile: CareerProfile) -> list[str]:
81        ...
82
83    def recommend_projects(self, profile: CareerProfile) -> list[str]:
84        ...
85
86    def get_interview_topics(self) -> list[str]:
87        ...
88
89
90class BackendStrategy:
91    """Career strategy for backend engineering path."""
92
93    def recommend_skills(self, profile: CareerProfile) -> list[str]:
94        core = [
95            "FastAPI / Django REST Framework",
96            "PostgreSQL + query optimization",
97            "Redis caching strategies",
98            "Docker + Kubernetes basics",
99            "System design patterns",
100            "API security (OAuth2, JWT)",
101        ]
102        existing = {s.name.lower() for s in profile.skills}
103        return [s for s in core if s.lower() not in existing]
104
105    def recommend_projects(self, profile: CareerProfile) -> list[str]:
106        return [
107            "REST API with auth, rate limiting, and pagination",
108            "Real-time chat service with WebSockets",
109            "Task queue system with Celery and Redis",
110            "Microservice with Docker Compose deployment",
111        ]
112
113    def get_interview_topics(self) -> list[str]:
114        return [
115            "Design a URL shortener",
116            "Design a rate limiter",
117            "REST vs GraphQL trade-offs",
118            "Database indexing and query optimization",
119            "Caching strategies (cache-aside, write-through)",
120            "Horizontal vs vertical scaling",
121        ]
122
123
124class DataScienceStrategy:
125    """Career strategy for data science path."""
126
127    def recommend_skills(self, profile: CareerProfile) -> list[str]:
128        core = [
129            "Pandas + NumPy for data manipulation",
130            "SQL (window functions, CTEs, joins)",
131            "Statistical hypothesis testing",
132            "Data visualization (Matplotlib, Seaborn)",
133            "Feature engineering techniques",
134            "A/B testing methodology",
135        ]
136        existing = {s.name.lower() for s in profile.skills}
137        return [s for s in core if s.lower() not in existing]
138
139    def recommend_projects(self, profile: CareerProfile) -> list[str]:
140        return [
141            "End-to-end EDA on a public dataset with insights",
142            "A/B test analysis with statistical significance",
143            "Interactive dashboard with Streamlit or Plotly Dash",
144            "Predictive model with cross-validation and deployment",
145        ]
146
147    def get_interview_topics(self) -> list[str]:
148        return [
149            "Explain p-values and statistical significance",
150            "SQL: window functions and complex joins",
151            "Pandas: groupby, merge, pivot operations",
152            "How to handle missing data and outliers",
153            "Bias-variance trade-off in modeling",
154            "Design an A/B test for a product feature",
155        ]
156
157
158class MLEngineerStrategy:
159    """Career strategy for ML engineering path."""
160
161    def recommend_skills(self, profile: CareerProfile) -> list[str]:
162        core = [
163            "PyTorch or TensorFlow fundamentals",
164            "scikit-learn pipeline and preprocessing",
165            "MLOps (MLflow, model versioning, serving)",
166            "Feature stores and data pipelines",
167            "Model evaluation and metrics",
168            "Cloud ML services (SageMaker, Vertex AI)",
169        ]
170        existing = {s.name.lower() for s in profile.skills}
171        return [s for s in core if s.lower() not in existing]
172
173    def recommend_projects(self, profile: CareerProfile) -> list[str]:
174        return [
175            "Train and deploy a model with FastAPI serving",
176            "Build an ML pipeline with feature engineering",
177            "Model monitoring dashboard with drift detection",
178            "Fine-tune a pre-trained model for a custom task",
179        ]
180
181    def get_interview_topics(self) -> list[str]:
182        return [
183            "Explain gradient descent and backpropagation",
184            "Precision vs recall vs F1 — when to use each",
185            "How to handle class imbalance",
186            "Model serving: batch vs real-time inference",
187            "Feature engineering best practices",
188            "Design an ML system for recommendation",
189        ]
190
191
192# --- Career Advisor: uses strategy pattern ---
193class CareerAdvisor:
194    """Main service that generates career growth plans."""
195
196    STRATEGIES = {
197        "backend": BackendStrategy,
198        "data_science": DataScienceStrategy,
199        "ml_engineering": MLEngineerStrategy,
200    }
201
202    def __init__(self, profile: CareerProfile):
203        self.profile = profile
204        strategy_cls = self.STRATEGIES.get(profile.specialization)
205        if not strategy_cls:
206            valid = ", ".join(self.STRATEGIES.keys())
207            raise ValueError(
208                f"Unknown specialization: {profile.specialization}. "
209                f"Valid options: {valid}"
210            )
211        self.strategy = strategy_cls()
212
213    def generate_growth_plan(self) -> dict:
214        """Generate a comprehensive career growth plan."""
215        return {
216            "profile": self.profile.name,
217            "specialization": self.profile.specialization,
218            "current_skills": self.profile.skill_summary,
219            "growth_areas": self.profile.get_growth_areas(),
220            "recommended_skills": self.strategy.recommend_skills(
221                self.profile
222            ),
223            "recommended_projects": self.strategy.recommend_projects(
224                self.profile
225            ),
226            "interview_topics": self.strategy.get_interview_topics(),
227        }
228
229
230# --- Demo usage ---
231if __name__ == "__main__":
232    profile = CareerProfile(
233        name="Alex Developer",
234        title="Junior Python Developer",
235        specialization="backend",
236    )
237    profile.add_skill("Python", SkillLevel.INTERMEDIATE, 2.0)
238    profile.add_skill("SQL", SkillLevel.BEGINNER, 0.5)
239    profile.add_skill("Git", SkillLevel.INTERMEDIATE, 1.5)
240
241    advisor = CareerAdvisor(profile)
242    plan = advisor.generate_growth_plan()
243
244    print(f"\nCareer Growth Plan for {plan['profile']}")
245    print(f"Specialization: {plan['specialization']}")
246    print(f"\nCurrent skill levels: {plan['current_skills']}")
247    print(f"\nGrowth areas: {plan['growth_areas']}")
248    print(f"\nRecommended skills to learn:")
249    for skill in plan["recommended_skills"]:
250        print(f"  - {skill}")
251    print(f"\nPortfolio projects:")
252    for project in plan["recommended_projects"]:
253        print(f"  - {project}")
254    print(f"\nInterview prep topics:")
255    for topic in plan["interview_topics"]:
256        print(f"  - {topic}")