Mastering Python : Beginner to Advanced

Enhance your Python skills with our comprehensive course that takes you from a beginner to an advanced level.

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Mastering Python : Beginner to Advanced

What you'll learn

  • Foundational Python Programming Skills: Master core Python concepts, including variables, data types, loops, functions, and object-oriented programming.
  • Intermediate and Advanced Techniques: Gain expertise in error handling, decorators, generators, and working with advanced data structures.
  • Real-World Applications: Learn to build web applications, automate tasks, and analyze data using popular libraries like pandas, numpy, and matplotlib.
  • Problem-Solving with Projects: Apply your knowledge to practical projects such as web scraping, building APIs, and developing basic machine learning models.
  • ndustry Readiness: Acquire the skills required for Python-based careers in data science, web development, and automation, with hands-on experience.

Course Syllabus

Module 1: Introduction to Python (Beginners)
  • a:16:{i:0;s:109:"Getting Started: What is Python? Installing Python and Setting Up the Environment (Anaconda Jupyter VS Code)";i:1;s:33:"Writing Your First Python Program";i:2;s:21:"Python IDEs and Tools";i:3;s:55:"Core Concepts: Variables Data Types and Type Conversion";i:4;s:23:"Input/Output Operations";i:5;s:47:"Basic Operators (Arithmetic Comparison Logical)";i:6;s:32:"Strings and String Manipulations";i:7;s:12:"Control Flow";i:8;s:18:"If/Else Statements";i:9;s:17:"Loops (For While)";i:10;s:5:"Break";i:11;s:8:"Continue";i:12;s:4:"Pass";i:13;s:56:"Basic Data Structures Lists Tuples Sets and Dictionaries";i:14;s:38:"Indexing Slicing and Basic Operations";i:15;s:33:"Iteration through Data Structures";}
Module 2: Intermediate Python
  • a:20:{i:0;s:9:"Functions";i:1;s:30:"Defining and Calling Functions";i:2;s:27:"Arguments and Return Values";i:3;s:43:"Lambda Functions and Higher-Order Functions";i:4;s:9:"Recursion";i:5;s:28:"Error and Exception Handling";i:6;s:35:"Understanding Errors and Exceptions";i:7;s:3:"Try";i:8;s:6:"Except";i:9;s:7:"Finally";i:10;s:17:"Custom Exceptions";i:11;s:18:"Working with Files";i:12;s:37:"Reading and Writing Text/Binary Files";i:13;s:33:"Context Managers (with statement)";i:14;s:13:"File Handling";i:15;s:9:"Use Cases";i:16;s:16:"Python Libraries";i:17;s:35:"Introduction to os sys math random";i:18;s:38:"Working with Dates and Time (datetime)";i:19;s:26:"Basics of pandas and numpy";}
Module 3: Object-Oriented Programming (OOP)
  • a:12:{i:0;s:19:"Classes and Objects";i:1;s:28:"Defining Classes and Objects";i:2;s:30:"Instance Variables and Methods";i:3;s:28:"Inheritance and Polymorphism";i:4;s:31:"Single and Multiple Inheritance";i:5;s:29:"Method Overriding and Super()";i:6;s:13:"Encapsulation";i:7;s:29:"Private and Protected Members";i:8;s:19:"Property Decorators";i:9;s:21:"Advanced OOP Concepts";i:10;s:20:"Magic/Dunder Methods";i:11;s:24:"Static and Class Methods";}
Module 4: Advanced Python
  • a:18:{i:0;s:24:"Advanced Data Structures";i:1;s:6:"Stacks";i:2;s:38:"Queues and Linked Lists (Using Python)";i:3;s:55:"Introduction to Collections (deque Counter defaultdict)";i:4;s:24:"Iterators and Generators";i:5;s:28:"Creating and Using Iterators";i:6;s:30:"Building Generators with yield";i:7;s:21:"Generator Expressions";i:8;s:10:"Decorators";i:9;s:29:"Function and Class Decorators";i:10;s:19:"Chaining Decorators";i:11;s:11:"Concurrency";i:12;s:29:"Multithreading with threading";i:13;s:36:"Multiprocessing with multiprocessing";i:14;s:30:"Async Programming with asyncio";i:15;s:29:"Error Handling Best Practices";i:16;s:30:"Raising Exceptions Effectively";i:17;s:31:"Logging with the logging Module";}
Module 5: Applications and Real-World Projects
  • a:16:{i:0;s:12:"Web Scraping";i:1;s:32:"Using BeautifulSoup and requests";i:2;s:34:"Automating Tasks with Scraped Data";i:3;s:31:"Data Analysis and Visualization";i:4;s:26:"Advanced pandas Operations";i:5;s:44:"Visualizing Data with matplotlib and seaborn";i:6;s:38:"Exploratory Data Analysis (EDA) Basics";i:7;s:15:"Web Development";i:8;s:43:"Introduction to Flask and Django Frameworks";i:9;s:19:"Building a REST API";i:10;s:10:"Automation";i:11;s:39:"Automating Emails and File Organization";i:12;s:37:"Using selenium for Browser Automation";i:13;s:23:"Machine Learning Basics";i:14;s:57:"Introduction to Machine Learning Libraries (scikit-learn)";i:15;s:48:"Building a Basic Regression/Classification Model";}
Module 6: Capstone Projects
  • a:4:{i:0;s:36:"Project Options (Choose One or More)";i:1;s:62:"1. Building a Personal Finance Tracker with Data Visualization";i:2;s:49:"2. Developing a Web Scraper for Live Stock Prices";i:3;s:45:"3. Automating Daily Tasks with Python Scripts";}

Course Syllabus

  • Getting Started: What is Python? Installing Python and Setting Up the Environment (Anaconda Jupyter VS Code)
  • Writing Your First Python Program
  • Python IDEs and Tools
  • Core Concepts: Variables Data Types and Type Conversion
  • Input/Output Operations
  • Basic Operators (Arithmetic Comparison Logical)
  • Strings and String Manipulations
  • Control Flow
  • If/Else Statements
  • Loops (For While)
  • Break
  • Continue
  • Pass
  • Basic Data Structures Lists Tuples Sets and Dictionaries
  • Indexing Slicing and Basic Operations
  • Iteration through Data Structures

  • Functions
  • Defining and Calling Functions
  • Arguments and Return Values
  • Lambda Functions and Higher-Order Functions
  • Recursion
  • Error and Exception Handling
  • Understanding Errors and Exceptions
  • Try
  • Except
  • Finally
  • Custom Exceptions
  • Working with Files
  • Reading and Writing Text/Binary Files
  • Context Managers (with statement)
  • File Handling
  • Use Cases
  • Python Libraries
  • Introduction to os sys math random
  • Working with Dates and Time (datetime)
  • Basics of pandas and numpy

  • Classes and Objects
  • Defining Classes and Objects
  • Instance Variables and Methods
  • Inheritance and Polymorphism
  • Single and Multiple Inheritance
  • Method Overriding and Super()
  • Encapsulation
  • Private and Protected Members
  • Property Decorators
  • Advanced OOP Concepts
  • Magic/Dunder Methods
  • Static and Class Methods

  • Advanced Data Structures
  • Stacks
  • Queues and Linked Lists (Using Python)
  • Introduction to Collections (deque Counter defaultdict)
  • Iterators and Generators
  • Creating and Using Iterators
  • Building Generators with yield
  • Generator Expressions
  • Decorators
  • Function and Class Decorators
  • Chaining Decorators
  • Concurrency
  • Multithreading with threading
  • Multiprocessing with multiprocessing
  • Async Programming with asyncio
  • Error Handling Best Practices
  • Raising Exceptions Effectively
  • Logging with the logging Module

  • Web Scraping
  • Using BeautifulSoup and requests
  • Automating Tasks with Scraped Data
  • Data Analysis and Visualization
  • Advanced pandas Operations
  • Visualizing Data with matplotlib and seaborn
  • Exploratory Data Analysis (EDA) Basics
  • Web Development
  • Introduction to Flask and Django Frameworks
  • Building a REST API
  • Automation
  • Automating Emails and File Organization
  • Using selenium for Browser Automation
  • Machine Learning Basics
  • Introduction to Machine Learning Libraries (scikit-learn)
  • Building a Basic Regression/Classification Model

  • Project Options (Choose One or More)
  • 1. Building a Personal Finance Tracker with Data Visualization
  • 2. Developing a Web Scraper for Live Stock Prices
  • 3. Automating Daily Tasks with Python Scripts

Requirements

  • Laptop
  • Good wifi

Description

Phython CPurse

This Python Course is designed to take you from a complete beginner to an advanced Python programmer, equipping you with the skills needed to solve real-world problems. You’ll start with the basics, including variables, loops, and functions, and progress to advanced topics such as object-oriented programming, decorators, and asynchronous programming. The course emphasizes hands-on learning through interactive coding exercises, real-world projects, and the use of popular Python libraries like pandas, numpy, and matplotlib. Whether you're looking to build web applications, automate tasks, or dive into data science, this course provides a comprehensive foundation for a successful Python career.

Who this course is for:

  • Beginners
  • Intermediate
  • Learners
  • Aspiring Data Scientists
  • Automation Enthusiasts Career Switchers

Meet your instructors

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Rashmi Ranjan Mangaraj

AI & Data Sceince
View Profile

Rashmi Ranjan Mangaraj is a highly skilled AI/ML Engineer with 6 years of practical, hands-on experience in developing and deploying artificial intelligence and machine learning solutions. His expertise lies in creating data-driven models and AI-powered solutions tailored to solve complex business c...

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Why enroll in this course?

Lead digital transformation in your organization by mastering the core concepts of generative artificial intelligence and its potential impact.

Master and integrate prompt engineering to optimize day-to-day tasks and automate workflows.

Foster an “AI friendly” culture in your organization by understanding the ethical aspects and the risks associated with the implementation of this technology.

Explore tools such as ChatGPT, as well as other emerging technologies, to improve productivity.

Interact with MIT experts, instructors, and peers in live synchronous sessions for a more comprehensive learning experience.

Access to rich supplementary resources provides additional materials and content for a more thorough educational journey.

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Course Syllabus

  • Getting Started: What is Python? Installing Python and Setting Up the Environment (Anaconda Jupyter VS Code)
  • Writing Your First Python Program
  • Python IDEs and Tools
  • Core Concepts: Variables Data Types and Type Conversion
  • Input/Output Operations
  • Basic Operators (Arithmetic Comparison Logical)
  • Strings and String Manipulations
  • Control Flow
  • If/Else Statements
  • Loops (For While)
  • Break
  • Continue
  • Pass
  • Basic Data Structures Lists Tuples Sets and Dictionaries
  • Indexing Slicing and Basic Operations
  • Iteration through Data Structures

  • Functions
  • Defining and Calling Functions
  • Arguments and Return Values
  • Lambda Functions and Higher-Order Functions
  • Recursion
  • Error and Exception Handling
  • Understanding Errors and Exceptions
  • Try
  • Except
  • Finally
  • Custom Exceptions
  • Working with Files
  • Reading and Writing Text/Binary Files
  • Context Managers (with statement)
  • File Handling
  • Use Cases
  • Python Libraries
  • Introduction to os sys math random
  • Working with Dates and Time (datetime)
  • Basics of pandas and numpy

  • Classes and Objects
  • Defining Classes and Objects
  • Instance Variables and Methods
  • Inheritance and Polymorphism
  • Single and Multiple Inheritance
  • Method Overriding and Super()
  • Encapsulation
  • Private and Protected Members
  • Property Decorators
  • Advanced OOP Concepts
  • Magic/Dunder Methods
  • Static and Class Methods

  • Advanced Data Structures
  • Stacks
  • Queues and Linked Lists (Using Python)
  • Introduction to Collections (deque Counter defaultdict)
  • Iterators and Generators
  • Creating and Using Iterators
  • Building Generators with yield
  • Generator Expressions
  • Decorators
  • Function and Class Decorators
  • Chaining Decorators
  • Concurrency
  • Multithreading with threading
  • Multiprocessing with multiprocessing
  • Async Programming with asyncio
  • Error Handling Best Practices
  • Raising Exceptions Effectively
  • Logging with the logging Module

  • Web Scraping
  • Using BeautifulSoup and requests
  • Automating Tasks with Scraped Data
  • Data Analysis and Visualization
  • Advanced pandas Operations
  • Visualizing Data with matplotlib and seaborn
  • Exploratory Data Analysis (EDA) Basics
  • Web Development
  • Introduction to Flask and Django Frameworks
  • Building a REST API
  • Automation
  • Automating Emails and File Organization
  • Using selenium for Browser Automation
  • Machine Learning Basics
  • Introduction to Machine Learning Libraries (scikit-learn)
  • Building a Basic Regression/Classification Model

  • Project Options (Choose One or More)
  • 1. Building a Personal Finance Tracker with Data Visualization
  • 2. Developing a Web Scraper for Live Stock Prices
  • 3. Automating Daily Tasks with Python Scripts

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Course Syllabus

Module 1: Introduction to Python (Beginners)
  • a:16:{i:0;s:109:"Getting Started: What is Python? Installing Python and Setting Up the Environment (Anaconda Jupyter VS Code)";i:1;s:33:"Writing Your First Python Program";i:2;s:21:"Python IDEs and Tools";i:3;s:55:"Core Concepts: Variables Data Types and Type Conversion";i:4;s:23:"Input/Output Operations";i:5;s:47:"Basic Operators (Arithmetic Comparison Logical)";i:6;s:32:"Strings and String Manipulations";i:7;s:12:"Control Flow";i:8;s:18:"If/Else Statements";i:9;s:17:"Loops (For While)";i:10;s:5:"Break";i:11;s:8:"Continue";i:12;s:4:"Pass";i:13;s:56:"Basic Data Structures Lists Tuples Sets and Dictionaries";i:14;s:38:"Indexing Slicing and Basic Operations";i:15;s:33:"Iteration through Data Structures";}
Module 2: Intermediate Python
  • a:20:{i:0;s:9:"Functions";i:1;s:30:"Defining and Calling Functions";i:2;s:27:"Arguments and Return Values";i:3;s:43:"Lambda Functions and Higher-Order Functions";i:4;s:9:"Recursion";i:5;s:28:"Error and Exception Handling";i:6;s:35:"Understanding Errors and Exceptions";i:7;s:3:"Try";i:8;s:6:"Except";i:9;s:7:"Finally";i:10;s:17:"Custom Exceptions";i:11;s:18:"Working with Files";i:12;s:37:"Reading and Writing Text/Binary Files";i:13;s:33:"Context Managers (with statement)";i:14;s:13:"File Handling";i:15;s:9:"Use Cases";i:16;s:16:"Python Libraries";i:17;s:35:"Introduction to os sys math random";i:18;s:38:"Working with Dates and Time (datetime)";i:19;s:26:"Basics of pandas and numpy";}
Module 3: Object-Oriented Programming (OOP)
  • a:12:{i:0;s:19:"Classes and Objects";i:1;s:28:"Defining Classes and Objects";i:2;s:30:"Instance Variables and Methods";i:3;s:28:"Inheritance and Polymorphism";i:4;s:31:"Single and Multiple Inheritance";i:5;s:29:"Method Overriding and Super()";i:6;s:13:"Encapsulation";i:7;s:29:"Private and Protected Members";i:8;s:19:"Property Decorators";i:9;s:21:"Advanced OOP Concepts";i:10;s:20:"Magic/Dunder Methods";i:11;s:24:"Static and Class Methods";}
Module 4: Advanced Python
  • a:18:{i:0;s:24:"Advanced Data Structures";i:1;s:6:"Stacks";i:2;s:38:"Queues and Linked Lists (Using Python)";i:3;s:55:"Introduction to Collections (deque Counter defaultdict)";i:4;s:24:"Iterators and Generators";i:5;s:28:"Creating and Using Iterators";i:6;s:30:"Building Generators with yield";i:7;s:21:"Generator Expressions";i:8;s:10:"Decorators";i:9;s:29:"Function and Class Decorators";i:10;s:19:"Chaining Decorators";i:11;s:11:"Concurrency";i:12;s:29:"Multithreading with threading";i:13;s:36:"Multiprocessing with multiprocessing";i:14;s:30:"Async Programming with asyncio";i:15;s:29:"Error Handling Best Practices";i:16;s:30:"Raising Exceptions Effectively";i:17;s:31:"Logging with the logging Module";}
Module 5: Applications and Real-World Projects
  • a:16:{i:0;s:12:"Web Scraping";i:1;s:32:"Using BeautifulSoup and requests";i:2;s:34:"Automating Tasks with Scraped Data";i:3;s:31:"Data Analysis and Visualization";i:4;s:26:"Advanced pandas Operations";i:5;s:44:"Visualizing Data with matplotlib and seaborn";i:6;s:38:"Exploratory Data Analysis (EDA) Basics";i:7;s:15:"Web Development";i:8;s:43:"Introduction to Flask and Django Frameworks";i:9;s:19:"Building a REST API";i:10;s:10:"Automation";i:11;s:39:"Automating Emails and File Organization";i:12;s:37:"Using selenium for Browser Automation";i:13;s:23:"Machine Learning Basics";i:14;s:57:"Introduction to Machine Learning Libraries (scikit-learn)";i:15;s:48:"Building a Basic Regression/Classification Model";}
Module 6: Capstone Projects
  • a:4:{i:0;s:36:"Project Options (Choose One or More)";i:1;s:62:"1. Building a Personal Finance Tracker with Data Visualization";i:2;s:49:"2. Developing a Web Scraper for Live Stock Prices";i:3;s:45:"3. Automating Daily Tasks with Python Scripts";}

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