Programming Skills: Proficiency in Python programming language, including knowledge of syntax, data structures, control flow, and object-oriented programming principles. Writing clean, efficient, and maintainable code following best practices and coding standards. Web Development: Experience with web development frameworks and libraries such as Django, Flask, or FastAPI for building web applications and APIs. Understanding of MVC (Model-View-Controller) architecture, routing, templating, and ORM (Object-Relational Mapping) for database interaction. Data Science and Analytics: Familiarity with data manipulation and analysis libraries such as NumPy, Pandas, and SciPy for data processing, exploration, and visualization. Experience with machine learning frameworks like TensorFlow or Scikit-learn for building predictive models. Scripting and Automation: Writing scripts and automation tools using Python for tasks such as data parsing, file manipulation, system administration, and workflow automation. Automating repetitive tasks to improve efficiency and productivity. Database Integration: Integrating Python applications with relational databases (e.g., MySQL, PostgreSQL, SQLite) or NoSQL databases (e.g., MongoDB, Redis) for data storage and retrieval. Writing SQL queries, managing database connections, and performing CRUD (Create, Read, Update, Delete) operations. API Development: Designing and implementing RESTful APIs using frameworks like Flask or Django Rest Framework for building scalable and extensible backend services. Defining API endpoints, request-response formats, authentication mechanisms, and error handling. Testing and Debugging: Writing unit tests, integration tests, and end-to-end tests to ensure code reliability, functionality, and performance. Using debugging tools, logging, and exception handling techniques to identify and fix issues in Python code. Version Control: Proficiency in version control systems such as Git for managing codebase changes, collaborating with team members, and tracking project history. Understanding of branching, merging, and pull request workflows for effective code management. Deployment and DevOps: Experience with deploying Python applications to production environments using containerization tools like Docker or virtualization platforms like Kubernetes. Understanding of CI/CD (Continuous Integration/Continuous Deployment) pipelines for automated testing and deployment. Problem-Solving and Troubleshooting: Strong problem-solving skills and ability to troubleshoot technical issues in Python applications, libraries, and dependencies. Analyzing errors, stack traces, and performance metrics to identify root causes and implement effective solutions.