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10 Reason to Learn Python
According to Stack Overflow and GitHub, Python has seen year-over-year growth in developer adoption — making it a future-proof choice for your career
Python is a top requirement for roles like:
- Data Scientist
- Machine Learning Engineer
- AI Developer
- Python Developer
- Automation Tester
- Web Developer
- DevOps Engineer
Python developers earn excellent salaries — in India, entry-level roles start at ₹4–6 LPA, while experienced professionals in AI/Data Science can earn ₹20–50+ LPA. In the U.S., average salaries are $100,000+ per year
There are over 1 million job openings for Python-related skills worldwide across industries — including fintech, healthcare, education, e-commerce, and more.
Over 70% of Data Science and AI projects use Python, thanks to powerful libraries like Pandas, NumPy, TensorFlow, and Scikit-learn.
Python has a simple syntax, readable like English — so even non-tech learners and school students can pick it up quickly and grow into complex applications.
From web apps (with Django or Flask) to games (using Pygame) or automating Excel reports — Python lets you build real-world tools without learning multiple languages.
Python has tons of free learning platforms, tutorials, and open-source projects. Plus, its community is one of the largest and most supportive in the world.
Top companies like Google, Meta, Amazon, Spotify, Netflix, NASA, and Microsoft rely on Python for backend systems, data analysis, automation, and AI.
Python skills can earn you $20–$100/hour on platforms like Upwork and Fiverr, especially in areas like web scraping, automation, and data analysis.
PLACEMENT RECORD
We’re proud of our alumni who now work at top companies. Although we keep their names private, we’re happy to share details if you request.
Syllabus Basics
- Overview of different types of programming languages
- What constitutes a programming language?
- Classifications: Low-level, high-level, scripting, and markup
- Role of translators in coding (compiler vs interpreter)
- Explanation of scripting languages with examples
- Comparing scripting and programming languages
- Procedural vs Object-Oriented Programming styles
- Concept of programming paradigms
- What is Python and why is it widely used?
- Evolution and timeline of Python as a language
- Key characteristics: Interpreted, dynamic, object-oriented, modular, embeddable, extensible
- Uses of Python in diverse industries and real-time environments
- Python as a general-purpose tool
- A few constraints and limitations Python developers should know
- What is PSF and its role in Python development
- Major implementations and distributions of Python (CPython, Jython, etc.)
- Applications of Python in domains like AI, automation, web, etc.
- Real-time adoption of Python in industries
- Key differences across versions: Python 2.x vs 3.x, 3.7 vs 3.8
- Software development lifecycle in Python environments
- Python tools and software distributions
- How to download and install Python on different platforms (Windows, Linux, Unix, Mac)
- Overview of online interpreters and offline development tools
- IDEs widely used in the industry: Jupyter Notebook, Spyder, PyCharm, Rodeo, VS Code, ATOM, PyDev, etc.
- Comparison between editors and full IDEs
- Using Python in online coding environments
- Overview of Python’s language structure and grammar
- Alternative Python implementations and interpreter flavors
- Working with:
- Keywords
- Identifiers
- Constants & literals
- In-depth overview of Python’s built-in data types
- How Python compares with Java on syntax and functionality
- Python code writing modes: Interactive shell vs Script mode
- Understanding Python’s basic programming elements
- Program structure, indentation rules, and execution flow
- Writing and executing your first Python script
- File extensions (.py, .pyw)
- Setting environment variables and path configurations
- Running Python code manually or via an IDE
- Behind-the-scenes view of how the Python interpreter processes code
- What is bytecode in Python?
- Debugging Python code with built-in tools
- Understanding the Python execution lifecycle
- Initializing variables in Python
- Difference between local and global variables
- Using the global keyword to access or modify global values
- Understanding data types like bytes, bytearray
- Formatting strings using format() and f-strings
- Modules: math, random, secrets for practical operations
- Reading input from users
- Displaying output using the print() function
- Command-line input arguments
- Converting data types using:
- int(), float(), complex()
- str(), chr(), ord()
- Arithmetic operations
- Relational and comparison operators
- Assignment techniques
- Logical and bitwise operations
- Bit shifting: left shift, right shift
- Membership (in, not in) and Identity (is, is not) operators
- Ternary operators for inline conditionals
- Operator precedence and associativity
- Difference between is and ==
- Conditional logic with if, if-else, elif
- Nested conditions and best practices
- Iterative loops: for, while, nested loops
- Loop control:
- break, continue, pass, return
- Practical examples and use cases
🔹 Overview:
- What are data structures and why are they important?
- Real-world applications of Python collections
- Categories: Sequential and Non-sequential
- Sequential: Strings, Lists, Tuples, Range
- Non-sequential: Sets, Frozen Sets, Dictionaries
🔹 Strings:
- Defining strings and working with characters
- Access using index and iterators
- Slicing and modifying strings
- String operators and built-in methods
- Immutability concept in strings
- Use of string functions in practice
🔹 Lists:
- Introduction and advantages of lists
- Creating and manipulating lists in various ways
- List comprehension
- Slicing, indexing, nested lists
- List methods and mutability
- Understanding copy types: deep, shallow, and hard copy
- Working with zip() and unzipping logic
🔹 Tuples:
- Creating tuples and using them in programs
- Tuple methods and immutability
- Accessing tuples using slicing/indexing
- How tuple differs from list
- Use cases with nested structures
🔹 Sets:
- Defining sets and their properties
- Operations and methods: union, intersection, difference
- Comparison: list vs set
- Frozen sets and how they differ
- Case-based use examples
🔹 Dictionaries:
- Dictionary as a key-value store
- Comparing dicts with lists and sets
- Accessing, updating, and deleting key-value pairs
- Dictionary methods and functions
- Dictionary comprehension
- Introduction to Python’s hashing mechanism
- Why functions? Benefits of modular code
- Syntax and rules for defining functions
- Calling functions and returning values
- Types of function usage:
- No arguments, no return
- With arguments, with or without return
- Recursive functions
- Argument types:
- Default
- Positional
- Keyword
- Variable-length
- Special keywords like pass
- Lambda (anonymous) functions
- Functional programming tools: map(), filter(), reduce()
- Nested functions, closure behavior
- nonlocal and global scope
- Advanced features:
- Decorators
- Generators
- Iterators
- Monkey patching for dynamic updates
Syllabus Intermediate
🔹 Working with Modules
- Importance of breaking programs into modules
- Understanding what a module is in Python
- Classifying modules: built-in vs user-defined
- Creating your own custom modules
- Function-based modules
- Class-based modules
- Ways to use modules in a program:
- import
- from … import
- Renaming modules with aliasing
- Built-in properties of modules
🔹 Organizing with Packages
- How to structure larger Python projects into packages
- Package types: built-in and custom
- Difference between a folder and a package
- Understanding .py files in packaging
- Techniques to import and use packages
🔹 Package Management with PIP
- What is PIP and why it’s needed
- How to install and upgrade PIP
- Installing third-party Python libraries
- Removing/uninstalling installed packages
🔹 Basics of OOP
- Comparing procedural and object-oriented styles
- Principles of OOP:
- Encapsulation
- Abstraction (Data Hiding)
- Inheritance
- Polymorphism
- How to define a class and create objects
- Types of variables:
- Instance-level
- Class-level
- Types of methods:
- Instance methods
- Class methods
- Static methods
- How constructors work and how to initialize objects
- Using the self and cls reference keywords
- Access modifiers in Python:
- Public
- Protected (_)
- Private (__)
🔹 Working with Properties
- Creating getter and setter functions
- Using @property and property()
- Creating object attributes dynamically
🔹 Polymorphism in Action
- Understanding method and constructor overriding
- Concepts of method and constructor overloading
- Operator overloading with special methods
- Reusing code using composition and aggregation
- Inheritance types:
- Single
- Multilevel
- Multiple
- Hierarchical
- Hybrid
- Diamond problem resolution using MRO
- Role of constructors in inheritance
- super() and accessing parent class
- Runtime polymorphism and its application
🔹 Advanced OOP Concepts
- Duck Typing
- Abstract base classes and concrete methods
- Difference between abstraction and encapsulation
- Introduction to inner classes:
- Writing inner classes
- Accessing class-level and object-level data
- Local and complex inner classes
- Hands-on case studies for each concept
- What are exceptions and why they occur
- Difference between syntax errors and runtime errors
- Common exception types:
- AttributeError, ValueError, IndexError, TypeError, etc.
- Writing exception-safe code using:
- try-except blocks
- try with multiple except
- Single except for multiple exceptions
- finally block and its guarantee of execution
- try-except-finally combinations
- Use of raise for manual exception generation
- Creating user-defined exceptions
- Real-time exception handling examples
- What are regular expressions and how they’re used
- Differences between strings and regex patterns
- Functions in Python’s re module:
- match(), search(), split(), findall()
- compile(), sub(), subn()
- Using special characters, operators, and tokens
- Pattern matching for:
- Phone numbers
- Email IDs
- Passwords
- URLs
- Vehicle numbers
- Case study examples for data extraction using regex
- Opening and working with files in different modes
- Reading, writing, and appending data to files
- Counting lines, reading line-by-line
- CSV file operations:
- Creating, reading, and writing CSV data
- Object serialization with pickle module
- XML and JSON parsing
- Introduction to file-based logging
- Understanding the purpose of logging
- Log levels and their meanings
- Setting up logging in overwrite and append modes
- Logging timestamps and structured messages
- Capturing exceptions into log files
- Building a custom logger with advanced features
- Using Python’s datetime and date objects
- Formatting dates and working with time deltas
- Calendar generation and viewing:
- Text calendar
- HTML calendar
- Introduction to os module
- Running shell commands from Python
- File and folder operations:
- Create, delete, rename, move
- Automating system operations like shutdown/restart
- Accessing file system metadata
- Executing terminal commands programmatically
- Overview of multitasking and threading in Python
- Using threading module to create threads
- Thread creation via subclassing and callable objects
- Thread lifecycle and states
- Difference between calling run() and start()
- Thread control methods: sleep(), join()
- Using Lock objects for synchronization:
- acquire() and release()
- Real-time examples: single-threaded vs multi-threaded apps
- What is garbage collection and why it matters
- Manual vs automatic memory cleanup
- Handling self-referencing objects
- Python’s gc module overview
- Using collect() and threshold() functions
- Implementing cleanup strategies via case studies
Syllabus Advanced
- Introduction to database management systems (DBMS)
- Differences between file-based systems and DBMS
- Setting up database connectivity (MySQL, Oracle)
- Installing and using the mysql.connector module
- Establishing a connection using connect()
- Oracle DB integration via cx_Oracle
- Cursor operations: creating and managing cursors
- Executing SQL commands using:
- execute()
- executemany()
- Fetching results:
- fetchone(), fetchmany(), fetchall()
- Working with static vs dynamic SQL queries
- Transaction control and data commit/rollbac
- Real-time project-based case studies
- Overview of networking basics and socket concepts
- Introduction to the socket module
- Server-side socket operations
- Connecting clients to servers using sockets
- Building a basic client-server communication program
- Writing the server logic
- Implementing the client logic
- Practical hands-on networking example
- What is GUI programming in Python?
- Working with the tkinter module
- Creating a basic GUI app using Tk class
- Adding widgets:
- Labels
- Entry boxes
- Buttons
- Combo boxes
- Radio buttons
- Layout management in Tkinter
- Handling events triggered by user interaction
- Customizing widget properties
- Mini GUI application projects
Turtle
- Introduction to Turtle graphics for creative drawing
- Using Turtle for visual Python logic and design
NumPy (Numerical Python)
- Introduction to NumPy and its capabilities
- Environment setup and array creation
- Data types in NumPy
- Array attributes: shape, size, dimension, memory
- Array creation techniques:
- empty(), zeros(), ones(), arange(), linspace()
- Indexing and slicing arrays
- Advanced indexing: integer and boolean
- Broadcasting and its behavior
- Array manipulation: reshape, flatten, transpose
- Stacking and splitting arrays
- Mathematical and statistical functions:
- Trigonometric, rounding, power, mean, median, std dev, variance
- Searching, sorting, and filtering arrays
- Matrix operations with matlib
- Linear algebra: dot product, determinant, solving equations
- Visualization using Matplotlib:
- Plotting sine wave, bars, histograms
Pandas (Data Handling Library)
- Introduction to Pandas for structured data
- Data Structures:
- Series
- DataFrame
- Panel
- Creating data from:
- Lists
- Dictionaries
- ndarrays
- Scalars
- Accessing data via label, position (loc, iloc)
- DataFrame operations:
- Column selection, addition, deletion
- Row operations: selection, append, drop
- Basic data operations and statistics
- Function application: table-wise, row-wise, element-wise
- Reindexing, renaming, and iteration over DataFrames
- Sorting and filtering data
- Working with text-based data
- Configuring Pandas options
- Aggregations: sum(), mean(), count(), etc.
- Handling missing values:
- Replacing, dropping, filling (NaN)
- Grouping and grouping operations
- Data merging, joining, and concatenation
- Date handling:
- Date/time parsing, timedelta, date operations
- Categorical data management
- Data visualization:
- Bar, histogram, box, scatter, pie charts
- Input/Output:
- Reading/writing CSV, Excel
- Sparse data handling and caveats
- Comparing Pandas vs SQL operations
- What is Machine Learning?
- Different categories of ML:
- Supervised
- Unsupervised
- Reinforcement Learning
- Role of Python in machine learning pipelines
- Real-world relevance of data science with Python
- Overview of Django as a web framework
- What is a web framework and its need?
- Difference between MVC and MVT architecture
- Understanding Django’s MVT design pattern
- How Django simplifies Python web development
- Real-world Django project use cases
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The introductory classes are free. You only pay for advanced modules or project training. Fees are affordable and structured per module. [Download the brochure] or [Contact us] for the latest pricing.
No prior programming experience is required. Our course starts from the basics and gradually moves to advanced concepts.
Python opens doors to careers in:
Software Development
Web Development
Data Science
Machine Learning
Automation Testing
DevOps & more
Yes. We focus on project-based learning. You’ll build real-time projects to understand practical implementation.
Absolutely! We provide doubt-clearing sessions and post-class support via chat, calls, and email.
Yes, a course completion certificate will be provided, which is helpful for your resume and job interviews.
You’ll learn to work with:
Python (latest version)
Jupyter Notebook
VS Code / PyCharm
GitHub for version control
- All you need is a computer with internet access. We’ll guide you through installing free tools like Python, Jupyter Notebook, VS Code, and other libraries. No paid software is required.
Yes, we specialize in helping businesses find and hire the right talent. Our HR and recruitment services are tailored to meet your company’s specific hiring needs. Contact us to learn how we can support your hiring process.
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