Python

Proficiency Level: Advanced

Python is a versatile programming language that I've extensively used across various domains including data analysis, web development, automation, and machine learning. I am proficient in a wide range of Python libraries and frameworks.

Core Python Skills

Data Science & Analytics:

  • NumPy: Advanced array operations, mathematical functions, and numerical computing
  • Pandas: Complex data manipulation, cleaning, transformation, and analysis
  • Matplotlib/Seaborn: Creating sophisticated data visualizations and statistical plots
  • Scikit-learn: Machine learning algorithms, model selection, and evaluation
  • Jupyter Notebooks: Interactive development and data exploration

Web Development:

  • Flask: Building RESTful APIs and web applications
  • Django: Full-stack web development with ORM and admin interface
  • FastAPI: Modern, fast web APIs with automatic documentation
  • Requests: HTTP client library for API integration

Automation & Scripting:

  • Selenium: Web scraping and browser automation
  • BeautifulSoup: HTML/XML parsing and web scraping
  • Schedule: Task scheduling and automation
  • Openpyxl: Excel file manipulation and automation

Notable Projects

Actuarial Data Pipeline

  • Developed automated data cleaning and analysis workflows for actuarial reports
  • Implemented ETL processes using Pandas and SQLAlchemy
  • Created scheduled reports that reduced manual work by 80%

Insurance Analytics Dashboard

  • Built a Flask web application for real-time insurance analytics
  • Integrated multiple data sources using Python APIs
  • Implemented interactive visualizations with Plotly and Dash

Machine Learning Models

  • Developed predictive models for insurance claim frequency and severity
  • Implemented customer segmentation using clustering algorithms
  • Created automated model validation and monitoring systems

Process Automation

  • Automated regulatory reporting processes using Python scripts
  • Built data validation tools that improved data quality by 95%
  • Created automated testing frameworks for actuarial models

Web Development Experience

Flask Applications:

  • RESTful API development with authentication and authorization
  • Database integration using SQLAlchemy ORM
  • Template rendering with Jinja2
  • Error handling and logging implementation

Django Projects:

  • Full-stack web applications with user management
  • Custom admin interfaces for business users
  • Integration with third-party services and APIs
  • Deployment on cloud platforms

Data Science Workflow

  1. Data Collection: Web scraping, API integration, database queries
  2. Data Cleaning: Handling missing values, outliers, and data quality issues
  3. Exploratory Analysis: Statistical analysis and visualization
  4. Model Development: Feature engineering and algorithm selection
  5. Model Evaluation: Cross-validation, metrics analysis, and interpretation
  6. Deployment: Model serving and monitoring in production

Best Practices

  • Code Quality: Following PEP 8 standards, type hints, and documentation
  • Testing: Unit testing with pytest, integration testing
  • Version Control: Git workflows and collaborative development
  • Virtual Environments: Dependency management with pip and conda
  • Performance: Code optimization and profiling

Continuous Learning

I continuously expand my Python knowledge by:

  • Exploring new libraries and frameworks
  • Contributing to open-source projects
  • Following Python community best practices
  • Experimenting with emerging technologies like async programming
  • Staying updated with the latest Python releases and features

Python serves as my primary tool for solving complex data problems and building robust applications that streamline business processes and provide valuable insights.