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Python Programming

Python Programming
  • Fee: 3000
  • Timing: 2.5 Hours Per Day
  • Duration: 3 Months

COURSE DETAILS

The Python Programming course offered at NIMACT is having the latest curriculum related to general purpose programming, automation and application development, with the view to preparing developers for catering to the IT, Data, AI, Research and Software Industry in India. The curriculum and the study materials are revised and updated every six months for the inclusion of the latest Python versions, libraries, coding practices and AI assisted learning methods. Accordingly assistance and advice are sought on a continuing basis from the industry for knowing and assessing their requirements.

Python succeeded because it removed the noise from programming. Other languages force a beginner to fight with brackets, semicolons, type declarations and long boilerplate before he can print a single line. Python removes all of that and lets the student concentrate on the actual thinking - what the problem is and how to solve it step by step. This is why Python is now the first programming language in most school and college syllabi worldwide, and why a beginner builds working programs within days rather than months.

The second reason for its dominance is reach. Python is the standard language of Artificial Intelligence and Machine Learning. It is the standard language of data analysis and data science. It is used for web backends with Django and Flask, for automation of daily office work, for testing, for scientific research, for image processing and for cloud scripting. One language opens all these doors, and almost every new technology released today provides a Python library first. For a student, this means the time invested in Python keeps paying returns in whichever direction he later chooses.

This course is careful about one thing in particular - teaching Python properly, not just quickly. Many self taught learners write Python that works but is not Python. They use loops where a comprehension belongs, they never understand mutability, and they get confused the first time a list changes unexpectedly inside a function. So here the language is taught with its own logic - data structures and their behaviour, references and mutability, comprehensions, functions as objects, decorators and generators. A student who learns this properly writes clean, professional code that a company can accept.

NIMACT teaches every programming language on the model of AI > Programming Language (Coding). The first stage is AI. Tools like ChatGPT, Claude AI, Gemini and Copilot are used to explain a concept such as mutability, decorator, generator or class inheritance in simple language, to read a traceback and explain which line actually caused the failure, to compare two approaches to the same problem, and to give extra practice questions. The second stage is the coding itself. Every program, every class and every project is typed, run and understood by the student with his own hands. This discipline is enforced most strictly in the Python course, for a specific reason - AI tools write Python better than any other language, so the temptation to simply accept the output is highest here. A student who does that will pass a practice exercise and fail in front of a client, because the day the code breaks, he will have no idea what it was doing.

So many opportunities after this course. IT companies, startups, analytics firms, research institutes, fintech companies, automation teams and product companies recruit Python developers, and the job profile will be Python Developer, Backend Developer, Data Analyst Trainee, Automation Executive, Software Developer Trainee, Machine Learning Trainee, Testing Automation Executive etc. Python is also strong for self employment - office automation scripts, data cleaning work, report generation tools and small business utilities are regularly paid freelance work.

Who Should Attend: Students of BCA, MCA, B.Tech, Polytechnic, Diploma and school level who have Python in their syllabus. Complete beginners who want the easiest and most useful first language. Office and MIS professionals who want to automate repetitive work. Students planning to enter Data Analysis, Machine Learning, Artificial Intelligence or Django web development, as well.

ELIGIBILITY CRITERIA: Open to all. There is no minimum qualification for this course. Basic computer handling is helpful, but the course starts from the very first step.

Mode: Hybrid (Offline + Online + Live Doubt Session)

SYLLABUS:
To get a better idea about the course structure, let us go through a list of important subjects present in this program. Note - Only the important points have been mentioned.

  • Programming Concept, ICT & AI
  • Artificial Intelligence (AI) as a Learning Support - ChatGPT, Claude AI, Gemini, Copilot, Grok, DeepSeek, Perplexity
  • AI for Concept Explanation, Traceback Reading, Code Reading and Practice Questions
  • Introduction to Python
  • History, Versions and Features of Python
  • Why Python is the Most Popular Language Today
  • Python vs C, C++, Java and JavaScript
  • Where Python is Used - AI, Data, Web, Automation, Research
  • Interpreter, Bytecode and Python Execution Process
  • Setup and Tools
  • Python Installation and PATH Setting
  • IDLE, VS Code, PyCharm and Jupyter Notebook
  • Google Colab and Online Compilers
  • Running a Script, Interactive Mode and Command Line
  • pip, Package Installation and Virtual Environment
  • Project Folder Structure
  • Python Basics
  • Statement, Indentation Rule and Comment
  • Variable, Assignment and Naming Convention
  • Dynamic Typing and Memory Reference
  • Data Types - int, float, complex, bool, str, NoneType
  • Type Conversion and Type Checking
  • Constant Convention and Multiple Assignment
  • Input with input Function
  • Output with print, sep, end and formatting
  • f-string, format Method and String Formatting
  • Operators
  • Arithmetic, Assignment and Comparison Operators
  • Logical, Identity and Membership Operators
  • Bitwise Operators
  • Walrus Operator
  • Operator Precedence and Associativity
  • Control Statements
  • if, elif, else and Nested Condition
  • match Statement
  • Conditional Expression
  • Loops
  • for Loop with range and Sequence
  • while Loop
  • break, continue and else with Loop
  • Nested Loop and Pattern Programs
  • enumerate and zip in Loops
  • Strings
  • String Creation, Indexing and Slicing
  • String Immutability
  • String Methods - upper, lower, strip, split, join, replace, find, count
  • startswith, endswith, isdigit, isalpha
  • String Formatting and Escape Characters
  • Multiline String and Raw String
  • Data Structures
  • List - Creation, Indexing, Slicing and Modification
  • List Methods - append, insert, extend, remove, pop, sort, reverse
  • List Copy, Shallow Copy and Deep Copy
  • Tuple - Creation, Immutability and Unpacking
  • Set - Creation, Set Operations and Methods
  • Dictionary - Key Value Pair, Access, Update, Delete
  • Dictionary Methods - keys, values, items, get, update, pop
  • Nested List and Nested Dictionary
  • Mutability vs Immutability - The Most Important Concept
  • Comprehensions
  • List Comprehension with Condition
  • Dictionary Comprehension and Set Comprehension
  • Nested Comprehension
  • Generator Expression
  • Project
  • Functions
  • Function Definition, Call and Return
  • Positional, Keyword and Default Arguments
  • Variable Length Arguments - args and kwargs
  • Return Multiple Values
  • Docstring and Function Documentation
  • Local, Global and Nonlocal Scope
  • global and nonlocal Keywords
  • Recursion and Recursive Problems
  • Lambda Function
  • map, filter and reduce
  • Function as an Object and First Class Function
  • Closure
  • Decorator - Concept, Creation and Practical Use
  • Generator and yield Keyword
  • Iterator and Iterable Protocol
  • Modules and Packages
  • Module Concept, import and from import
  • Aliasing and dir Function
  • Creating a Custom Module
  • Package, init.py and Module Search Path
  • Standard Library Overview
  • math, random, datetime, time, calendar
  • os, sys, shutil and pathlib
  • json, csv and collections
  • statistics and itertools
  • pip, requirements.txt and Virtual Environment Management
  • File Handling
  • File Open Modes and File Object
  • Read, Readline, Readlines and Write
  • Append Mode and File Pointer
  • with Statement and Context Manager
  • CSV File Read and Write
  • JSON File Read and Write
  • Excel File Handling with openpyxl
  • Working with Folders and Bulk File Processing
  • Binary File and Pickle Module
  • Exception Handling
  • Error vs Exception
  • Common Exceptions - ValueError, TypeError, IndexError, KeyError, ZeroDivisionError
  • try, except, else, finally
  • Multiple except and Exception Object
  • raise and Custom Exception Class
  • Reading a Traceback Correctly
  • Assertion and Debugging Practice
  • Object Oriented Programming
  • OOP Concept and Why It Is Needed
  • Class, Object and Attribute
  • init Constructor and self Parameter
  • Instance Variable vs Class Variable
  • Instance Method, Class Method and Static Method
  • Encapsulation, Private and Protected Convention
  • Inheritance - Single, Multiple, Multilevel
  • Method Overriding and super Function
  • Polymorphism and Duck Typing
  • Abstraction and Abstract Base Class
  • Magic Methods - str, repr, len, eq, add
  • Operator Overloading
  • Property, Getter and Setter
  • Data Class
  • Composition vs Inheritance
  • Regular Expression
  • re Module and Pattern Syntax
  • match, search, findall, sub and split
  • Group, Quantifier and Character Class
  • Practical Validation Patterns
  • Database Connectivity
  • SQLite with Python
  • MySQL and PostgreSQL Connection
  • Create, Insert, Select, Update, Delete from Python
  • Parameterized Query and Safe Data Handling
  • Reading Database Data into a Report
  • Working with the Internet
  • requests Module - GET and POST
  • JSON API Handling and Response Parsing
  • API Authentication Basics
  • Web Scraping with BeautifulSoup - Introduction
  • Downloading Files and Handling URLs
  • Introduction to Popular Libraries
  • NumPy - Array and Numeric Operations
  • Pandas - Series, DataFrame and Data Handling
  • Matplotlib - Basic Charts
  • Tkinter - Simple GUI Window and Widgets
  • Introduction to Django and Flask
  • Introduction to Automation with Python
  • Testing and Code Quality
  • Debugging with Breakpoint and Print Tracing
  • Unit Testing with unittest and pytest Basics
  • PEP 8 Style Guide and Clean Code Rules
  • Type Hints and Readable Code
  • Code Reading and Output Prediction Practice
  • Programming Coding Practice
  • Daily Coding Exercises and Logic Building Questions
  • Interview Questions on Data Structures, Mutability and OOP
  • Practical Programs
  • Calculator and Unit Converter
  • Student Result and Marksheet Program
  • Employee Payroll and Attendance Program
  • Shop Billing and Inventory Program
  • Bank Account Management Program
  • Bulk File Rename and Folder Organizer
  • Excel Report Generator
  • Contact Book with Database
  • Quiz Program and Number Guessing Game
  • Multi Platform and Career
  • Python on Windows, Linux and Online Compilers
  • Git and GitHub Basics for Python Projects
  • Converting a Script to an EXE File
  • Path Ahead - Data Analysis, Machine Learning, Django and Automation
  • Portfolio, Resume and Interview Preparation
  • Emerging Technologies - Python in AI, Data Science and Cloud
  • Project Work
  • Internship