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Python Training

60 Days (1 Hour 30 Minutes Daily)
★ Featured

Overview

This Python Training program is designed to help learners master the fundamentals and advanced concepts of Python programming. Covering everything from core syntax and OOPs to data structures, regular expressions, file handling, and frameworks like Django, the course is suitable for beginners and professionals alike. It provides hands-on exposure to Python for data analytics, machine learning, and web development.

Who Can Attend

Course Content

Core Python and Language Fundamentals

  • Introduction to programming languages and scripting
  • Translators: Compiler and Interpreter
  • Programming paradigms and scripting types
  • History and features of Python
  • Python installation, setup, and IDEs (Spyder, Jupyter, PyCharm)

Python Basics and Syntax

  • Keywords, identifiers, and data types
  • Structure of a Python program
  • Running Python scripts in interactive and scripting modes
  • Variables, operators, and expressions
  • Input/output operations and command-line arguments

Control Statements and Loops

  • Conditional statements: if, if-else, elif, nested-if
  • Loops: for, while, nested loops
  • Branching statements: break, continue, pass, return
  • Ternary and membership operators
  • Case studies on decision-making logic

Data Structures and Collections

  • Overview and importance of Python data structures
  • Strings, lists, tuples, sets, and dictionaries
  • Indexing, slicing, and comprehension techniques
  • Mutable vs Immutable data types
  • Case studies on real-world data operations

Functions and Lambda Expressions

  • Defining and calling functions
  • Function arguments: default, positional, keyword, variable
  • Recursion and anonymous functions (lambda)
  • map(), filter(), reduce() functions
  • Decorators, generators, and closures

Modules, Packages, and PIP

  • Creating and importing modules
  • Predefined vs user-defined modules
  • Organizing code into packages
  • Installing and managing libraries using PIP
  • Modular programming and reusable code practices

Object-Oriented Programming (OOPs)

  • Classes, objects, and constructors
  • Instance, class, and static methods
  • Encapsulation, abstraction, inheritance, and polymorphism
  • Operator and method overloading
  • Inner classes, abstract classes, and MRO concepts

Exception Handling and Regular Expressions

  • Syntax vs runtime errors
  • Try-except-finally blocks and custom exceptions
  • Using raise keyword for exceptions
  • Regular expressions and the re module
  • Data extraction using regex: emails, URLs, phone numbers

File and Directory Handling

  • Working with files: open, read, write, append
  • CSV, XML, and JSON parsing
  • Pickle module for object serialization
  • Working with OS and shutil modules
  • Automating file operations

Logging, DateTime, and OS Operations

  • Python logging levels and configuration
  • Creating custom loggers
  • Date and time manipulation using datetime and calendar
  • OS operations: file handling, renaming, system commands
  • Practical scripting for system automation

Multithreading and Multiprocessing

  • Difference between multitasking and multithreading
  • Threading module and thread lifecycle
  • Synchronization and locks
  • Multiprocessing overview
  • Case studies on parallel task execution

Database Connectivity (PDBC)

  • Introduction to DBMS and Python connectors
  • Connecting Python to MySQL and Oracle
  • Executing queries and managing transactions
  • Working with cursors and dynamic queries
  • Practical database case studies

Network Programming

  • Socket programming basics
  • Creating client-server applications
  • Server socket methods and connections
  • Implementing simple chat and data exchange programs
  • Practical networking examples

GUI Programming with Tkinter and Turtle

  • Introduction to GUI development in Python
  • Tkinter widgets: label, entry, button, combo, radio
  • Layout management and event handling
  • Turtle graphics and drawing operations
  • Case studies using GUI components

Data Analytics and Machine Learning Modules

  • Introduction to NumPy, SciPy, and Pandas
  • Arrays, matrices, and data manipulation
  • Data visualization using Matplotlib
  • Introduction to Machine Learning and Data Science
  • Overview of frameworks like Django

NumPy and Pandas in Depth

  • NumPy arrays, indexing, slicing, and broadcasting
  • Mathematical and statistical operations
  • Pandas Series and DataFrames creation and manipulation
  • Data merging, grouping, and visualization
  • Handling missing data and time series in Pandas

Django Framework Overview

  • Introduction to Django and MVT architecture
  • Difference between MVC and MVT
  • Setting up Django environment
  • Creating models, views, and templates
  • Developing web applications using Django