Advanced Python for Data Science

This comprehensive course covers a wide range of topics, providing students with a solid foundation in Python programming. It starts with an introduction to Python and covers syntax, data types, operators, input/output functions, flow control, command line arguments, functions, modules, OOP concepts (encapsulation, inheritance, polymorphism, abstraction), exception handling, file handling, date/time operations, threads, networking, and popular libraries like NumPy, Pandas, and Matplotlib. By the end of the course, students will have a strong understanding of Python programming, enabling them to develop applications, work with data efficiently, and utilize libraries for data analysis and visualization. 

*Continuing education unit equals 10 contact hours.

By the end of this course, learners will be able to

1. Explore Python programming language and various collection types in Python
2. Define logic using conditional statements and looping constructs.
3. Explore the different types of operators available in Python.
4. Analyze how to pass command line arguments to Python programs.
5. Create and utilize functions, lambdas, decorators, and generators in Python.
6. Discuss the fundamentals of Object-Oriented Programming (OOP) and its four principles.
7. Implement inheritance, abstraction, polymorphism, and encapsulation in your code.
8. Utilize abstract classes and interfaces to implement abstraction in your programs.
9. Read and write files using the Files API in Python.
10. Handle data and time-related operations in Python.

• Applicants must obtain the score for course ENGGB1A on the SEPT/OEPT or pass ENGGA2D.
• Passing Data Analysis and Visualization.

EGP 2,500