Introduction and Setting Up
The first session introduces Python and sets up the required software and environments.
Introduction to Python: History and Importance
This topic covers the background, history, and importance of Python in modern computing.
Python vs Other Languages
This topic contrasts Python with other popular programming languages.
Installing Python and Setting Up Environment
This topic guides the user on how to install Python and setup the coding environment.
Introduction to Python IDEs and Jupyter Notebook
This topic introduces various Python IDEs and Jupyter Notebook, a popular tool for Python development.
Basic Python Syntax
This session covers the foundational elements of Python syntax, data types, and basic operations.
Basic Syntax and Variables
This topic introduces basic Python syntax, expressions, and variables.
Data Types in Python: Numeric, String, List
This topic explains different data types in Python like Numeric, String, and List.
Basic Operators
This topic covers basic operators in Python including arithmetic, comparison, and assignment operators.
Getting User Input
This topic explains how to take user input in Python.
Flow Control
This session introduces flow control concepts in Python, including conditionals and loops.
Conditional Statements: If, Else, Elif
This topic covers how to use conditional statements in Python.
Looping Statements: For, While
This topic introduces the concept of loops in Python.
Range function and Loop Control Statements: Break, Continue, Pass
This topic explains the range function and how to control loop execution with break, continue, and pass statements.
Data Structures - I
This session delves into Python data structures including lists and tuples.
Lists: Introduction, Indexing and Slicing
This topic introduces lists, a versatile Python data structure.
List Methods: Append, Extend, Insert, Remove, Pop, Count, Sort, Reverse
This topic details various methods to manipulate lists in Python.
Tuples: Introduction and Operations
This topic introduces tuples, an immutable Python data structure, and covers basic tuple operations.
Data Structures - II
This session further explores Python data structures, focusing on sets and dictionaries.
Sets: Introduction, Creating and Adding Elements
This topic introduces sets, a Python data structure that holds an unordered collection of unique items.
Set Operations: Union, Intersection, Difference, Symmetric Difference
This topic covers set operations such as union, intersection, and difference.
Dictionaries: Introduction, Creating, Accessing Values, Updating
This topic introduces dictionaries, a Python data structure that holds key-value pairs.
Functions
This session covers the concept of functions in Python.
Introduction to Functions: Defining and Calling
This topic introduces functions, which are reusable pieces of code.
Function Arguments: Required, Keyword, Default, Variable-length
This topic explains different types of function arguments in Python.
Return Statements
This topic explains the use of the return statement in Python functions.
Scope of Variables: Global vs Local
This topic discusses the scope of variables and how it impacts code.
Modules and Packages
This session introduces the concept of modules and packages in Python.
Introduction to Python Modules
This topic introduces the concept of Python modules.
Importing a Module and Using Its Functions
This topic explains how to import a module and use its functions in your Python code.
Understanding Packages and Sub-packages
This topic delves into Python packages, a way of structuring Python’s module namespace.
Using pip to Install External Packages
This topic covers the use of pip, a package manager for Python.
Exception Handling
This session covers how to handle errors and exceptions in Python code.
Introduction to Exception Handling
This topic introduces the concept of exception handling in Python.
Try, Except, Finally Blocks
This topic explains how to use try, except, and finally blocks to handle exceptions in Python.
Raising Exceptions
This topic covers how to raise exceptions in Python.
Built-in Exceptions in Python
This topic explains the built-in exceptions provided by Python.
File I/O
This session covers how to perform input and output operations with files in Python.
Reading from and Writing to Files
This topic explains how to read from and write to files in Python.
Working with Different File Modes
This topic details various file modes in Python for different types of file operations.
Handling CSV and TXT Files
This topic covers how to handle CSV and TXT files in Python.
Introduction to OOP Concepts
This session introduces the object-oriented programming (OOP) concepts in Python.
Classes and Objects
This topic introduces classes and objects, fundamental concepts of OOP.
Methods: Instance, Static, Class Methods
This topic covers different types of methods in Python classes.
Inheritance, Polymorphism, Encapsulation
This topic introduces key OOP concepts like inheritance, polymorphism, and encapsulation.
Standard Library
This session explores the Python Standard Library, an extensive suite of modules that comes with Python by default.
Overview of Python Standard Library
This topic provides an overview of Python's vast standard library.
Working with Datetime Module
This topic introduces the datetime module, which supplies classes to manipulate dates and times.
Working with Math and Random Module
This topic covers the math and random modules for mathematical functions and generating random numbers respectively.
Exploring Other Useful Modules
This topic provides an overview of other useful modules in Python's standard library.
Introduction to Python Libraries
This session introduces several popular Python libraries for data analysis and visualization.
Introduction to NumPy
This topic introduces NumPy, a Python library for numerical operations.
Introduction to pandas
This topic introduces pandas, a Python library for data manipulation and analysis.
Introduction to matplotlib
This topic introduces matplotlib, a Python library for data visualization.
Introduction to seaborn
This topic introduces seaborn, a Python library for statistical data visualization.
Basic Data Analysis
This session covers basic data analysis tasks using Python, including data loading, cleaning, statistical analysis, and visualization.
Loading and Inspecting Data with pandas
This topic explains how to load and inspect data using pandas.
Basic Data Cleaning and Preprocessing
This topic covers basic techniques for data cleaning and preprocessing in Python.
Descriptive Statistics with pandas
This topic introduces how to compute descriptive statistics using pandas.
Data Visualization with matplotlib and seaborn
This topic covers basic data visualization techniques using matplotlib and seaborn.
Mini Project
This session is dedicated to a mini project to apply the concepts learned throughout the curriculum.
Planning and Implementing a Small Python Project
This topic covers how to plan and implement a small Python project.
Applying Concepts Learnt So Far
This topic gives an opportunity to apply and consolidate all the concepts learnt throughout the course.
Debugging and Testing the Code
This topic introduces the concepts of debugging and testing in Python.
Reflection and Learning from the Project
This topic provides an opportunity to reflect on the project and learn from the experience.
Conclusion and Further Learning
The final session reflects on the learning journey and provides guidance for further learning.
Reflecting on the Learning Journey
This topic gives a chance to reflect on the learning journey and the progress made throughout the course.
Exploring Advanced Python Topics: Decorators, Generators, Asyncio, etc.
This topic introduces several advanced Python topics for further exploration.
Guidance on Learning Web Development with Django/Flask
This topic provides guidance on how to proceed with learning web development using Python's Django or Flask frameworks.
Guidance on Learning Data Science/Machine Learning with Python
This topic provides guidance on how to proceed with learning data science and machine learning using Python.