Certificate Course in Python Programming
Kickstart your career in Python programming with our 3-month CCIP program!
- Duration: 3 months
- Course Fee: ₹ 6,000
- Career Opportunities: Python Developer, Data Scientist, Machine Learning Engineer, Web Developer (using Django or Flask), Software Engineer, Data Analyst, AI Specialist, Python Automation Engineer, Backend Developer, Freelance Python Developer, Python Development Instructor/Trainer
Gain expert knowledge in Python programming, including web development, data science, machine learning, automation, and more, to excel in a variety of tech roles.
Course Curriculum
Module 1: Introduction to Python
- History of Python
- Why Python is popular (simplicity, versatility, and readability)
- Python's applications in Web Development, Data Science, AI, Automation, etc.
- Python 2.x vs Python 3.x
- Setting up the Python environment (Installation & IDE setup: PyCharm, Visual Studio Code, Jupyter Notebooks)
- Introduction to the Python Interpreter
- Running Python code interactively (REPL) and in script mode
- Writing and executing the first Python program: Hello, World!
- Python script structure: indentation, line breaks
- Variables and data types: int, float, str, list, tuple, dict, set
- Basic input/output: input(), print()
- Strings and string operations: concatenation, formatting, methods (.upper(), .lower(), .split(), etc.)
- Comments and documentation: inline comments (#), multi-line comments, docstrings
Overview of Python
First Steps in Python Programming
Basic Syntax and Structure
Module 2: Data Types and Operators
- Integers (int), Floating-point numbers (float), Strings (str)
- Complex numbers (complex), Boolean (bool)
- Lists, Tuples, Sets, Dictionaries
- Arithmetic operators: +, -, *, /, //, %, **
- Comparison operators: ==, !=, <, >, <=, >=
- Logical operators: and, or, not
- Assignment operators: =, +=, -=, *=, /=
- Identity operators: is, is not
- Membership operators: in, not in
- Implicit and explicit type casting
- int(), float(), str() functions
Data Types
Operators
Type Casting
Module 3: Control Flow (Conditional Statements and Loops)
- if, elif, else
- Nested conditions
- Conditional expressions (ternary operator)
- Boolean expressions in conditions
- for loop: iterating through sequences (lists, tuples, ranges)
- while loop: running based on a condition
- Nested loops
- Loop control: break, continue, pass
- List comprehensions for generating lists
- Dictionary comprehensions for building dictionaries
- Set comprehensions
Conditional Statements
Loops
Comprehensions
Module 4: Functions and Modules
- Defining functions with def
- Function parameters: positional arguments, default arguments, variable-length arguments (*args, **kwargs)
- Return values and return types
- Lambda functions (anonymous functions)
- Recursion: understanding recursive functions
- Variable scope: local, global, and nonlocal variables
- Introduction to Python modules and packages
- Importing standard modules: math, random, datetime, os, sys, etc.
- Creating and organizing custom modules
- Exploring third-party libraries: NumPy, Pandas, Matplotlib
- Python Package Index (PyPI) and pip for managing packages
Functions
Modules and Packages
Module 5: Data Structures
- Creating and modifying lists
- List slicing and indexing
- List methods: .append(), .remove(), .pop(), .sort(), etc.
- Nested lists
- Iterating through lists using loops and list comprehensions
- Creating and working with immutable tuples
- Tuple unpacking
- Using tuples for returning multiple values from functions
- Differences between lists and tuples
- Creating dictionaries: key: value pairs
- Accessing, modifying, and deleting items
- Dictionary methods: .get(), .keys(), .values(), .items()
- Nested dictionaries
- Creating sets: unique, unordered collections
- Set operations: union (|), intersection (&), difference (-)
- Using sets to remove duplicates from lists
- Set methods: .add(), .remove(), .discard()
Lists
Tuples
Dictionaries
Sets
Module 6: File Handling and Exception Handling
- Opening and closing files: open(), close()
- Reading files: read(), readline(), readlines()
- Writing to files: write(), writelines()
- File modes: r, w, a, b, rb, wb
- Using with statement for file handling (context manager)
- Introduction to errors and exceptions
- try, except, else, finally block
- Handling multiple exceptions
- Raising exceptions with raise
- Creating custom exceptions (user-defined exceptions)
File Handling
Exception Handling
Module 7: Object-Oriented Programming (OOP)
- Objects and classes
- Creating classes and instances
- Instance variables and methods
- The __init__ constructor
- self keyword
- Inheriting from a parent class
- Overriding parent class methods
- The super() function
- Method overloading (same name, different arguments)
- Method overriding (in derived class)
- Operator overloading
- Public, private, and protected attributes
- @property decorators for getter methods
- Abstract base classes (ABCs) and abc module
Introduction to OOP Concepts
Inheritance
Polymorphism
Encapsulation and Abstraction
Module 8: Advanced Python Concepts
- Introduction to regular expressions
- Python re module: match(), search(), findall(), sub()
- Metacharacters: ., *, +, [], (), |, ^, $
- Common regex patterns for matching strings
- Creating generator functions with yield
- Benefits of using generators (memory efficiency)
- Using next() to iterate through generators
- Understanding decorators and their syntax
- Creating simple function decorators
- Using decorators to modify behavior of functions
- Introduction to concurrency and parallelism
- Creating threads using threading module
- Thread synchronization (locks, semaphores)
- Using multiprocessing module for parallel processing
Regular Expressions (Regex)
Generators
Decorators
Multithreading and Multiprocessing
Module 9: Working with External Libraries
- Understanding arrays: creating and manipulating NumPy arrays
- Array indexing, slicing, and reshaping
- Mathematical operations on arrays
- Working with DataFrames and Series
- Data cleaning: handling missing values, filtering, and sorting data
- Data aggregation and groupby operations
- Reading/writing data from/to CSV, Excel, and SQL databases
- Creating line plots, bar charts, histograms, etc.
- Customizing plots (titles, labels, legends)
- Subplots and multiple axes
NumPy for Scientific Computing
Pandas for Data Manipulation
Matplotlib for Data Visualization
Module 10: Introduction to Web Development (Optional)
- Introduction to web development with Python
- Setting up a Flask/Django project
- Handling HTTP requests and routing
- Templates and rendering HTML
- Handling forms and user input
Flask/Django Basics (choose one framework)
Module 11: Final Project
- Students will choose or be assigned a real-world project to apply their learning.
- Example projects: A web scraper, a small web app, data analysis project, etc.
- Students will work on the project independently or in groups.
- Emphasis on debugging, optimization, and clean code writing.
Capstone Project
Conclusion and Certification
- Review of key concepts
- Final written exam or quiz covering all modules
- Project evaluation: presentation of the final project to peers/instructors
- Upon successful completion of exams and projects, a certificate is awarded to students.