Week 1: Introduction and Basics
– Introduction to Python: Overview, history, and applications.
– Setting Up: Installing Python and setting up a development environment.
– Basic Syntax: Variables, data types, basic operators.
– Basic Input/Output: Using print() and input() functions.
Week 2: Control Structures
– Conditional Statements: if, elif, else.
– Loops: for and while loops.
– Loop Control: break, continue, pass.
– Comprehensions: List comprehensions and generator expressions.
Week 3: Functions
– Defining Functions: Syntax, arguments, and return values.
– Scope and Lifetime: Local vs global variables.
– Lambda Functions: Anonymous functions.
– Built-in Functions: Commonly used functions like map(), filter(), and reduce().
Week 4: Data Structures – Basics
– Lists: Creation, indexing, slicing, methods.
– Tuples: Immutable sequences, basic operations.
– Sets: Unordered collections, set operations.
– Dictionaries: Key-value pairs, methods, and use cases.
Week 5: Data Structures – Advanced
– Nested Data Structures: Lists of lists, dictionaries of lists, etc.
– Data Structure Manipulation: Advanced operations and methods.
– Comprehensions: Nested comprehensions.
Week 6: File Handling and Exceptions
– File Operations: Reading from and writing to files using open(), read(), write().
– File Path Handling: Using os and pathlib modules.
– Exception Handling: try, except, finally, creating custom exceptions.
Week 7: Object-Oriented Programming (OOP)
– Classes and Objects: Basic class definition, attributes, methods.
– Inheritance: Creating subclasses, method overriding.
– Encapsulation and Polymorphism: Using private attributes, method overloading.
Week 8: Advanced OOP Concepts
– Abstract Classes and Interfaces: Using abc module.
– Multiple Inheritance: Combining multiple base classes.
– Design Patterns: Introduction to common design patterns (e.g., Singleton, Factory).
Week 9: Modules and Packages
– Standard Library Modules: Overview of commonly used libraries (e.g., math, datetime).
– Creating Modules and Packages: Writing and importing your own modules.
– Package Management: Using pip, managing dependencies with requirements.txt.
Week 10: Working with APIs
– HTTP Requests: Using requests library for API calls.
– Handling API Responses: Parsing JSON, handling errors.
– Creating APIs: Building a basic RESTful API using Flask or FastAPI.
Week 11: Data Analysis and Visualization
– Data Manipulation: Using Pandas for data handling and analysis.
– Data Visualization: Basic plotting with Matplotlib and Seaborn.
– Working with Real Data: Loading, cleaning, and visualizing datasets.
Week 12: Final Project and Review
– Project Development: Apply concepts to build a comprehensive project.
– Code Review and Feedback: Review project code and provide constructive feedback.
– Course Summary: Recap of key topics, next steps, and further learning resources.
Additional Components
– Assignments and Exercises: Weekly coding assignments and challenges to reinforce learning.
– Quizzes: Periodic quizzes to test understanding of key concepts.
– Office Hours and Q&A: Scheduled times for additional support and clarification.
The Python course was a fantastic introduction to programming and data science! It covered everything from basic syntax to advanced topics like data analysis and machine learning. The hands-on projects were engaging and helped solidify my skills. I'm now excited to apply Python in various real-world scenarios!
The Python course was a fantastic introduction to programming and data science! It covered everything from basic syntax to advanced topics like data analysis and machine learning. The hands-on projects were engaging and helped solidify my skills. I'm now excited to apply Python in various real-world scenarios!
