Python
In-depth exploratory research is carried out by experts to design Python course content at ICLP. Our Python Syllabus is developed by expert-level programming professionals to make sure of providing the best anytime-anywhere training environment for the aspirants. Python course details provide you with valuable and super clear information about all the Python programming concepts and also help you become a notable Python developer in the future. Below you will find the complete Python course details.
Course Highlights
Skills You'll Gain
- Learn Python basics and syntax
- Work with sequences and file operations
- Understand Object-Oriented Programming (OOP)
- Handle exceptions and modules
- Build data analysis and web scraping tools
- Develop REST APIs using Python frameworks
- Automate tasks and workflows with Python scripting
- Implement data visualization with popular libraries
Our Graduates Work At
Course Curriculum
- Need for programming
- Advantages of programming
- Overview of Python
- Organizations using Python
- Python Applications in various domains
- Accepting user input and eval function
- Files input/output functions
- Lists, Tuples, Strings manipulation
- Sets and set operations
- Python dictionary
- Concept of Object Orientation
- Attributes and Methods
- Classes and Objects
- Methods and Constructors
- Inheritance, Abstraction, and Polymorphism
- User-defined functions
- Function parameters
- Lambda functions
- Built-in functions
- Working with modules and handling exceptions
- Introduction to Pandas
- Data structures in Pandas
- Importing and exporting files
- Data cleaning and exploration
- Line plots, Bar plots, Histograms
- Pie charts, Scatter plots, Boxplots
- Customizing visualizations
- Saving plots
- Beautiful Soup library
- Scrapy and Requests library
- Image editing using OpenCV
- Face detection and motion detection
- Basics of database management
- Python MySQL
- CRUD operations
- MongoDB integration
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Why Learn Python?
- Easy-to-learn syntax ideal for beginners
- Versatile applications from web development to data science
- Extensive standard library and third-party packages
- Strong community support and documentation
- Excellent for rapid prototyping and development
- Cross-platform compatibility
- High demand in AI and machine learning fields
- Great for automation and scripting tasks
- Integration capabilities with other languages
- Strong corporate backing (Google, Facebook, etc.)
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Reviews
Average rating: 4.8/5 (1,000+ learners)
Python FAQs
Find answers to common questions about this course
Python is a versatile language used for web development (Django, Flask), data science, machine learning, automation, scripting, and more due to its simple syntax and powerful libraries.
Yes, Python is often recommended as a first language because of its readable syntax and gentle learning curve while still being powerful for professional use.
Most learners can grasp Python fundamentals in 4-8 weeks with consistent practice. The simple syntax allows quick progress compared to other languages.
Python 3 introduced print() as a function, improved integer division, Unicode support, and many other enhancements. Python 2 is no longer supported as of 2020.
Start with foundational libraries like NumPy, pandas for data, requests for web, matplotlib for visualization, then explore Django/Flask for web development.
Yes, but specializing in a Python-related field (data science, web dev, automation) along with relevant frameworks increases job prospects significantly.
Popular choices include PyCharm (full-featured IDE), VS Code (lightweight), and Jupyter Notebooks (for data science). Beginners can start with IDLE.
Python's data science stack (pandas, NumPy, scikit-learn) makes it ideal for data analysis, visualization, and machine learning, dominating this field.
Decorators are functions that modify the behavior of other functions, allowing you to wrap existing functionality without permanent modification.
Python is more versatile (beyond just statistics) and generally better for production systems, while R has stronger statistical packages. Many learn both.
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