1. Core Python:
- Variables: Containers for storing data values.
- Numbers: Integers, floating-point numbers, and complex numbers for numerical operations.
- Strings: Ordered sequences of characters used for text manipulation.
- If condition: Conditional statements for executing code based on certain conditions.
- For loop: Iterative control structure used for iterating over sequences or collections.
- Lists, Dictionaries, Sets, Tuples: Fundamental data structures for storing and organizing data.
- JSON: Lightweight data interchange format for transmitting data between a server and a web application.
- Functions and Lambda Functions: Blocks of reusable code for performing specific tasks.
- Classes and objects, OOPs Concepts, Inheritance, Polymorphism: Object-oriented programming concepts for creating modular and reusable code.
- Scope: Defines the visibility and accessibility of variables within a program.
- Logging: Mechanism for recording events occurring in a software application.
- Modules: Python files containing definitions and statements to be used in other Python scripts.
- Exception handling: Mechanism for handling errors and exceptions in Python code.
- Generators, Iterators: Constructs for creating iterators in an efficient and concise manner.
- File handling: Operations for reading from and writing to files.
- List Comprehensions: Concise syntax for creating lists based on existing lists.
- Decorators: Functions that modify the behavior of other functions without changing their code.
- Multithreading, Multiprocessing, Async IO: Techniques for concurrent execution of code to improve performance and responsiveness.
2. Data Structures:
- Memory Management: Mechanisms for efficiently allocating and deallocating memory resources.
- Hashing Concepts: Techniques for generating fixed-size representations of data.
- Queues, Stacks, and other primary Python Structures: Abstract data types for storing and retrieving data in specific orders.
3. Git:
- Version control system for tracking changes in source code during software development.
4. Numpy, Pandas, Data Visualization, Exploratory Data Analysis:
- Numpy: Library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices.
- Pandas: Library for data manipulation and analysis, offering data structures and operations for working with structured data.
- Data Visualization: Techniques for visually representing data to extract insights and patterns.
- Exploratory Data Analysis (EDA): Process of analyzing datasets to summarize their main characteristics, often using statistical graphics and other data visualization methods.
5. Two Projects on Python which can be hosted on Streamlit Cloud for free
6. Assignments and quiz for each Topic
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