• Proficiency in Python 3: In-depth understanding of Python syntax, semantics, and idioms.
• Data Structures and Algorithms: Knowledge of essential data structures (lists, dictionaries, sets, tuples) and algorithms (searching, sorting, recursion).
• Object-Oriented Programming (OOP): Strong grasp of classes, inheritance, polymorphism, and encapsulation.
• Error Handling: Competence in using try/except blocks, custom exceptions, and debugging techniques.

Web Development

• Frameworks: Expertise in Django and/or Flask for building web applications.
• RESTful APIs: Experience in designing, implementing, and consuming RESTful web services.
• Front-End Integration: Basic knowledge of HTML, CSS, and JavaScript for integrating back-end services with front-end frameworks.
• Databases: Proficiency in working with SQL databases (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Redis).

Data Science and Machine Learning

• Libraries: Familiarity with NumPy, Pandas, Matplotlib, Seaborn for data manipulation and visualization.
• Machine Learning: Experience with Scikit-Learn, TensorFlow, or PyTorch for building and deploying machine learning models.
• Data Analysis: Skills in data cleaning, transformation, and exploratory data analysis (EDA).

Automation and Scripting

• Scripting: Writing scripts for automating routine tasks and data processing.
• Web Scraping: Using libraries like BeautifulSoup, Scrapy, or Selenium for extracting data from websites.
• Task Automation: Familiarity with tools like Celery or Airflow for automating workflows and scheduled tasks.

DevOps and Deployment

• Version Control: Proficient in using Git and platforms like GitHub or GitLab for version control.
• CI/CD: Experience with continuous integration and deployment tools (Jenkins, Travis CI, GitHub Actions).
• Containerization: Knowledge of Docker for containerizing applications and Kubernetes for orchestration.
• Cloud Services: Familiarity with cloud platforms like AWS, Azure, or Google Cloud for deploying applications and services.