Python Course in Bangalore

4.8 (19,031 reviews)
  • Comprehensive Python training covering basic to advanced concepts and more...
  • Hands-on labs and real-world projects
  • 1.5-months of Intensive Training and LIVE Project mentoring
  • Unlimited access to Python Cloud Lab for practice
  • Real-Time Projects
  • Duration: 1.5 Months
  • BootCamp
  • Resume Building Session
  • Interview Preparation
  • Mock Interview
  • Placement Assistance
Enquire Now
Enquiry code drafter code drafter

Key Features

20+ tools

Access to over 20 Python tools and services.

laptop

Highly engaging physical sessions led by Python certified experts.

measurement instrument

Multiple industry-aligned capstone projects.

file check icon

Speed Building Techniques and Mock tests.

user with search icon

Job + Personality oriented comprehensive programs.

ideas

Tips & tricks on how to use Python tools for efficient and optimized programming.

Course
Advantages!

Technical expertise

Get an in-depth understanding of the core Python concepts such as data structures, web development, data science, and more.

Individual growth

Along with technical skills, we focus on your core development, such as personality, confidence, communication, aptitude, and logic building.

Non-tech Training

Learn essential skills like interview preparation, team collaboration, and how to excel in a corporate environment.

Hands-on experience

Our Python course provides hands-on experience with all the trending Python tools and technologies used by developers.

Skills we will be Infusing!

Technical Skills

Interpersonal Skills

Tool-Pool

Python

Python is a versatile language used for web development, data analysis, artificial intelligence, and scientific computing.

NumPy

NumPy is a fundamental package for scientific computing with Python.

Pandas

Pandas is a powerful data analysis and manipulation library for Python.

Django

Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design.

Flask

Flask is a micro web framework for Python based on Werkzeug and Jinja2.

TensorFlow

TensorFlow is an end-to-end open-source platform for machine learning.

Outcomes of the program?

A girl who is typing on her laptop.
Successful Python Developer

Soon after completing this program, you will be a successful Python Developer in a reputable IT Company with a very decent salary package as per the IT Industry standards.

Job positions

You will acquire the necessary skills and expertise to excel in the industry and be qualified for various job positions.

Succeed in job interviews

Our program includes a soft skills development component that will enhance your abilities to succeed in job interviews and showcase your strengths.

Live Projects

Through timely assessments and project-based training, you will gain proficiency in handling multiple live projects.

Job Selection

The Interview & GD training aspect of the program will equip you with the knowledge to succeed in job selection and perform well in any company.

Teamwork and Interaction

You will be proficient in teamwork and client interaction, making these qualities less challenging for you.

Two people are discussing business.

Syllabus For Python Course in Bangalore

  • 1. Overview of Python
    • What is Python?
    • History of Python
    • Features of Python
    • Python Applications
  • 2. Installation and Setup
    • Downloading and Installing Python
    • Setting Up Python Environment
    • Integrated Development Environments (IDEs)
  • 3. Writing Your First Python Program
    • Understanding the Python Syntax
    • Running Python Programs
    • Interactive Mode vs Script Mode
  • 1. Python Variables
    • Declaring and Assigning Values
    • Variable Naming Rules
    • Understanding Data Types
  • 2. Python Data Types
    • Numbers
    • Strings
    • Lists
    • Tuples
    • Dictionaries
    • Sets
  • 3. Basic Operators
    • Arithmetic Operators
    • Comparison Operators
    • Assignment Operators
    • Logical Operators
    • Bitwise Operators
  • 4. Control Flow
    • if, elif, else Statements
    • Loops: for and while
    • break, continue, pass
  • 5. Functions
    • Defining Functions
    • Function Arguments
    • Returning Values
    • Lambda Functions
  • 6. Modules and Packages
    • Importing Modules
    • Standard Library Modules
    • Creating Your Own Modules
  • 7. Exception Handling
    • Understanding Exceptions
    • try, except, finally
    • Raising Exceptions
    • Creating Custom Exceptions
  • 8. File Handling
    • Reading and Writing Files
    • Working with File Paths
    • Handling File Exceptions
  • 1. Introduction to OOP
    • Understanding OOP Concepts
    • Classes and Objects
    • Attributes and Methods
  • 2. Inheritance
    • Understanding Inheritance
    • Single and Multiple Inheritance
    • Overriding Methods
  • 3. Polymorphism
    • Polymorphism in Python
    • Method Overloading
    • Method Overriding
  • 4. Encapsulation and Abstraction
    • Data Hiding
    • Getters and Setters
    • Abstract Classes and Methods
  • 5. Magic Methods and Operator Overloading
    • Understanding Magic Methods
    • Overloading Operators
    • Customizing Object Behavior
  • 1. Iterators and Generators
    • Understanding Iterators
    • Creating Generators with yield
    • Using Generator Expressions
  • 2. Decorators
    • Understanding Decorators
    • Function Decorators
    • Class Decorators
  • 3. Context Managers
    • Using with Statements
    • Creating Custom Context Managers
  • 4. Regular Expressions
    • Introduction to Regular Expressions
    • Pattern Matching
    • Using re Module
  • 5. Multithreading and Multiprocessing
    • Understanding Threads
    • Creating and Managing Threads
    • Introduction to Multiprocessing
  • 6. Working with Databases
    • Introduction to SQL
    • Using SQLite with Python
    • Performing CRUD Operations
  • 7. Working with APIs
    • Understanding APIs
    • Using REST APIs with Python
    • Handling JSON Data
  • 1. Introduction to Data Science
    • What is Data Science?
    • Python’s Role in Data Science
    • Popular Python Libraries for Data Science
  • 2. NumPy
    • Understanding Arrays
    • Array

      Eligibility Criteria

      • To start with this Python program, you must have a basic knowledge and understanding of programming.

      • Anybody can learn Python, even if the students pass in art, commerce or computer science.

      • You must pass our basic admission test to enroll in this Python program.

      Python is a high-level, interpreted programming language known for its simplicity and readability. It is widely used for web development, data analysis, artificial intelligence, scientific computing, and more.

      Learning Python offers several benefits including versatility in various domains, high demand in the job market, ease of learning for beginners, extensive libraries and frameworks, and strong community support.

      Python has a rich ecosystem of frameworks and libraries including Django, Flask, NumPy, Pandas, TensorFlow, Keras, PyTorch, and many more.

      Python developers have a wide range of job opportunities in fields such as web development, data science, artificial intelligence, machine learning, automation, and scientific computing.

      The Python program is open to individuals without a technical background, but having fundamental knowledge in programming can be helpful and advantageous.

    • 2. NumPy
      • Understanding Arrays
      • Array Creation and Operations
      • Indexing and Slicing
      • Broadcasting
      • Mathematical Operations on Arrays
      • Working with Multi-Dimensional Arrays
      • Advanced Array Manipulation
    • 3. Pandas
      • Introduction to Pandas
      • DataFrames and Series
      • Data Cleaning and Preparation
      • Handling Missing Data
      • Data Aggregation and Grouping
      • Data Merging and Joining
      • Working with Dates and Times
      • Visualization with Pandas
    • 4. Matplotlib and Seaborn
      • Introduction to Data Visualization
      • Plotting with Matplotlib
      • Creating Line, Bar, and Pie Charts
      • Advanced Plots: Histograms, Scatterplots, etc.
      • Customizing Plots
      • Introduction to Seaborn
      • Statistical Plots with Seaborn
      • Customizing Seaborn Plots
    • 5. Data Wrangling with Pandas
      • Combining and Merging DataFrames
      • Reshaping DataFrames: Pivot, Melt
      • Handling Duplicates
      • Working with Time Series Data
      • Data Sampling and Permutation
    • 6. Introduction to Machine Learning with Scikit-Learn
      • Overview of Machine Learning
      • Supervised vs Unsupervised Learning
      • Data Preprocessing for Machine Learning
      • Building Machine Learning Models
      • Model Evaluation and Tuning
      • Using Scikit-Learn for Machine Learning
  • 1. Introduction to Web Development
    • Understanding the Web
    • Frontend vs Backend
    • Python’s Role in Web Development
  • 2. Working with Flask
    • Introduction to Flask
    • Setting Up a Flask Project
    • Creating Routes and Views
    • Handling Forms and User Input
    • Working with Templates
    • Database Integration with Flask
  • 3. Working with Django
    • Introduction to Django
    • Setting Up a Django Project
    • Understanding Django’s MVT Architecture
    • Creating and Managing Django Models
    • Working with Django Admin
    • Handling Forms and Views in Django
    • Template Rendering in Django
    • Django ORM and Database Management
  • 4. RESTful APIs with Flask and Django
    • Introduction to RESTful Services
    • Building REST APIs with Flask
    • Building REST APIs with Django REST Framework
    • Authentication and Authorization in APIs
    • Consuming REST APIs with Python
  • 5. Web Scraping with Python
    • Introduction to Web Scraping
    • Using BeautifulSoup for Web Scraping
    • Handling Web Scraping Challenges
    • Working with Scrapy for Advanced Scraping
    • Storing Scraped Data
  • 6. Deploying Python Web Applications
    • Introduction to Deployment
    • Deploying Flask Applications on Heroku
    • Deploying Django Applications on AWS
    • Configuring Web Servers for Python Applications
    • Monitoring and Scaling Web Applications
  • 1. Introduction to Automation with Python
    • Why Automate with Python?
    • Types of Automation
  • 2. Automating File and Folder Operations
    • Working with OS Module
    • Batch Renaming Files
    • Automating File Downloads
  • 3. Automating Web Browsing with Selenium
    • Introduction to Selenium
    • Setting Up WebDriver
    • Automating Web Tasks with Selenium
    • Handling Web Elements
    • Working with Forms and Inputs
  • 4. Automating Emails and Notifications
    • Sending Emails with Python
    • Using smtplib and email Modules
    • Automating Email Sending
    • Setting Up Email Notifications
  • 5. Automating Data Processing Tasks
    • Automating Excel and CSV Operations
    • Working with OpenPyXL and Pandas
    • Automating Data Parsing and Analysis
  • 1. Introduction to Networking with Python
    • Understanding Networking Concepts
    • Python Networking Libraries
  • 2. Socket Programming
    • Introduction to Sockets
    • Creating TCP and UDP Clients and Servers
    • Sending and Receiving Data
    • Handling Multiple Connections
  • 3. Working with Network Protocols
    • Understanding HTTP, FTP, and SMTP Protocols
    • Automating Network Tasks with Python
    • Building a Simple HTTP Server
  • 4. Network Security and Automation
    • Introduction to Network Security
    • Using Python for Network Scanning
    • Automating Security Audits
    • Using Python for Penetration Testing
  • 1. Introduction to AI with Python
    • What is Artificial Intelligence?
    • Python’s Role in AI Development
    • Overview of AI Libraries
  • 2. Machine Learning with Python
    • Introduction to Machine Learning
    • Supervised vs Unsupervised Learning
    • Implementing ML Algorithms with Scikit-Learn
  • 3. Natural Language Processing (NLP) with Python
    • Introduction to NLP
    • Text Processing with NLTK and SpaCy
    • Building a Simple Chatbot
  • 4. Deep Learning with Python
    • Understanding Deep Learning Concepts
    • Using TensorFlow and Keras
    • Building Neural Networks
    • Training and Evaluating Models
  • 5. Computer Vision with Python
    • Introduction to Computer Vision
    • Image Processing with OpenCV
    • Building Image Recognition Models
  • 6. AI in Real-World Applications
    • Case Studies of AI Applications
    • Deploying AI Models
    • Ethical Considerations in AI
  • 1. Setting Up Your Project
    • Defining Project Scope
    • Setting Up Version Control with Git
    • Creating a Project Structure
  • 2. Developing the Project
    • Writing Reusable Code
    • Testing and Debugging
    • Documentation and Code Comments
  • 3. Finalizing the Project
    • Project Deployment
    • Continuous Integration and Delivery
    • Monitoring and Maintenance
  • 4. Presenting Your Project
    • Creating a Project Report
    • Preparing a Project Presentation
    • Showcasing Your Work