Our Advanced Python Syllabus at Softlogic is designed to elevate your Python skills, offering an in-depth exploration of advanced concepts and techniques. This comprehensive syllabus covers topics such as object-oriented programming, data structures, algorithms, and Python libraries for data science and web development. Whether you’re looking to enhance your existing skills or prepare for specialized roles, our advanced Python course syllabus will guide you. Download our advanced python syllabus pdf for a detailed breakdown of each module and topic covered in the course.
Advanced Python Syllabus
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Advanced Python Syllabus Course Syllabus
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Advanced Python Fundamentals
- Advanced Data Types: Lists, Tuples, Sets, and Dictionaries
- Comprehensions: List, Dictionary, and Set Comprehensions
- Lambda Functions and Higher-Order Functions
- Iterators and Generators
- Decorators and Closures
Object-Oriented Programming (OOP) in Python
- Classes and Objects
- Inheritance and Polymorphism
- Encapsulation and Abstraction
- Magic Methods and Operator Overloading
- Abstract Classes and Interfaces
Exception Handling and File Management
- Advanced Exception Handling Techniques
- Custom Exception Handling
- File Handling and I/O Operations
- Working with JSON and CSV files
Data Structures and Algorithms
- Advanced Lists and Arrays
- Stacks, Queues, Linked Lists
- Trees, Graphs, and Hash Tables
- Sorting and Searching Algorithms
- Recursion and Dynamic Programming
Python Libraries for Data Science
- NumPy for Numerical Computing
- Pandas for Data Manipulation
- Matplotlib and Seaborn for Data Visualization
- SciPy for Scientific Computing
- Introduction to Data Cleaning and Preprocessing
Web Development with Python
- Introduction to Web Frameworks (Flask and Django)
- Building REST APIs with Flask
- Working with Databases (SQLAlchemy, SQLite)
- Templating Engines (Jinja2)
- User Authentication and Authorization
Machine Learning with Python
- Introduction to Machine Learning Concepts
- Supervised vs Unsupervised Learning
- Linear Regression, Logistic Regression, and Decision Trees
- Working with Scikit-learn
- Model Evaluation and Cross-Validation
Data Analysis and Visualization
- Advanced Pandas Techniques
- Data Wrangling and Transformation
- Visualizing Data with Matplotlib and Seaborn
- Time Series Analysis
- Introduction to Big Data and Hadoop
Working with APIs and Web Scraping
- RESTful APIs and HTTP Requests
- Working with APIs using Requests Library
- Web Scraping with BeautifulSoup and Scrapy
- Handling JSON and XML Data
Python for Automation and Scripting
- Automating Tasks with Python
- Web Scraping and Data Extraction
- Writing Python Scripts for System Administration
- Automating Web Testing with Selenium
Introduction to Deep Learning and TensorFlow
- Basics of Neural Networks and Deep Learning
- Introduction to TensorFlow
- Building and Training Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
Final Project and Case Studies
- Real-world Python Application Development
- Group Project: Web Application or Data Science Project
- Code Optimization and Refactoring
- Best Practices for Writing Clean and Maintainable Code
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Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
May 2025
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2 Hours Real Time Interactive Technical Training
1 Hour Aptitude
1 Hour Communication & Soft Skills
(Suitable for Fresh Jobseekers / Non IT to IT transition)
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May 2025
Week ends
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Online/Offline
4 Hours Real Time Interactive Technical Training
(Suitable for working IT Professionals)
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