Back to Courses
Algorithm and Programming (Python)
Comprehensive programming fundamentals and algorithm design using Python with emphasis on problem-solving and software development practices
14 Sessions
0% Complete
Course Progress0%
14
Total Sessions
42
Materials
28 hrs
Estimated Time
Course Sessions
Learning Objectives:
- Set up professional Python development environment with IDE configuration
- Master Python syntax rules, indentation, and code structure conventions
- Understand and implement all basic data types: integers, floats, strings, booleans
- Apply type hints and annotations for better code documentation
- Use input/output functions effectively with proper error handling
- Follow PEP 8 style guidelines and Python coding standards
Available Materials:
Python Installation and Setup Guide (25 pages)
Complete Syntax Reference and Style Guide
Data Type Reference with Examples
PEP 8 Style Guide and Best Practices
50 Beginner Programming Exercises
IDE Configuration and Debugging Setup
Code Quality Tools (pylint, black, mypy)
Python Documentation and Help System Guide
Learning Objectives:
- Master all Python operators: arithmetic, comparison, logical, bitwise, assignment
- Understand operator precedence and associativity rules
- Implement complex boolean expressions with proper logic
- Design and implement conditional statements with if-elif-else structures
- Apply short-circuit evaluation and ternary operators effectively
- Handle edge cases and input validation in conditional logic
Available Materials:
Complete Operator Reference and Precedence Table
Boolean Logic and Expression Evaluation Guide
Conditional Statement Patterns and Examples
40 Logic and Control Flow Exercises
Input Validation and Error Handling Examples
Code Debugging and Testing Strategies
Logic Problem Solving Worksheets
Real-world Conditional Logic Applications
Learning Objectives:
- Master for and while loop constructs with proper initialization and termination
- Implement nested loop algorithms for multi-dimensional problem solving
- Use break, continue, and else clauses in loops effectively
- Understand and apply iterator protocol and iterable objects
- Design efficient loop algorithms with optimal time complexity
- Apply loop patterns for data processing and algorithm implementation
Available Materials:
Complete Loop Reference and Patterns Guide (35 pages)
Nested Loop Algorithm Collection
Iterator and Iterable Object Implementation
60 Loop Programming Challenges
Algorithm Efficiency and Loop Optimization
Data Processing with Loop Patterns
Common Loop Pitfalls and Solutions
Advanced Iteration Techniques
Learning Objectives:
- Define functions with positional, keyword, default, and variable arguments
- Understand local, global, and nonlocal scope rules with practical applications
- Implement lambda functions and higher-order functions
- Apply function decorators for code enhancement and reusability
- Master recursive functions with base cases and optimization techniques
- Design modular code architecture using function composition
Available Materials:
Complete Function Programming Guide (45 pages)
Parameter Types and Advanced Function Signatures
Scope and Namespace Detailed Analysis
Lambda Functions and Functional Programming Examples
Decorator Patterns and Implementation
Recursion Theory and Practice with Examples
50 Function Design Challenges
Code Architecture and Modularity Principles
Learning Objectives:
- Master all list methods and operations with time complexity analysis
- Implement advanced slicing techniques and negative indexing
- Design efficient list comprehensions and generator expressions
- Understand memory management and list performance characteristics
- Apply tuple packing and unpacking for multiple assignment
- Implement custom sequence types with special methods
Available Materials:
Complete List and Tuple Reference (40 pages)
List Method Time Complexity Analysis
Advanced Slicing and Indexing Techniques
List Comprehension and Generator Expression Guide
Memory Management and Performance Optimization
70 List and Tuple Programming Problems
Custom Sequence Implementation Examples
Data Structure Performance Comparison
Learning Objectives:
- Master dictionary operations, methods, and hash table principles
- Implement set operations: union, intersection, difference, symmetric difference
- Understand hash functions, collision resolution, and dictionary performance
- Apply dictionary and set comprehensions for data transformation
- Design efficient data lookup and storage systems
- Implement advanced data structures using dictionaries and sets
Available Materials:
Dictionary and Set Complete Reference (40 pages)
Hash Table Theory and Implementation
Set Mathematics and Operations Guide
Dictionary and Set Comprehension Patterns
60 Hash-based Data Structure Problems
Performance Analysis of Hash Operations
Advanced Data Manipulation Techniques
Real-world Data Processing Applications
Learning Objectives:
- Master all string methods and formatting techniques including f-strings
- Understand Unicode, encoding, and international text processing
- Implement regular expressions for pattern matching and text validation
- Apply string algorithms for searching, parsing, and transformation
- Design text processing pipelines for data cleaning and analysis
- Optimize string operations for performance and memory efficiency
Available Materials:
Complete String Processing Guide (50 pages)
Unicode and Text Encoding Reference
Regular Expression Tutorial and Reference
String Algorithm Implementation Collection
Text Processing and Data Cleaning Examples
50 String Manipulation Challenges
Performance Optimization for String Operations
Natural Language Processing Basics
Learning Objectives:
- Master file reading and writing operations with proper exception handling
- Process CSV, JSON, and XML data formats programmatically
- Implement data serialization using pickle and other formats
- Handle binary files and data streaming efficiently
- Connect to databases and perform basic SQL operations
- Design robust data persistence and retrieval systems
Available Materials:
File I/O and Data Formats Guide (45 pages)
CSV and JSON Processing Tutorial
Data Serialization and Persistence Methods
Binary File Operations and Protocols
Database Connection and SQL Integration
40 Data Processing Projects
Error Handling and Data Validation
Large File Processing Optimization
Learning Objectives:
- Design and implement classes with proper encapsulation principles
- Master instance and class attributes, methods, and properties
- Apply access control using naming conventions and property decorators
- Implement special methods (__init__, __str__, __repr__, etc.)
- Design object relationships and collaborative object systems
- Apply SOLID principles in object-oriented design
Available Materials:
Object-Oriented Programming Complete Guide (60 pages)
Class Design and Implementation Patterns
Encapsulation and Access Control Techniques
Special Methods and Magic Methods Reference
SOLID Principles and Design Patterns
50 OOP Design and Implementation Problems
Object Relationship and Collaboration Examples
Code Architecture and Design Best Practices
Learning Objectives:
- Implement single and multiple inheritance with method resolution order
- Master method overriding and super() function usage
- Apply polymorphism and duck typing for flexible code design
- Use abstract base classes and interfaces for contract definition
- Implement common design patterns: Factory, Observer, Strategy
- Design extensible and maintainable object-oriented systems
Available Materials:
Advanced OOP Concepts Guide (55 pages)
Inheritance and MRO Detailed Analysis
Polymorphism and Duck Typing Examples
Abstract Classes and Interface Design
Design Patterns Implementation Collection
45 Advanced OOP Challenges
System Architecture and Design Principles
Code Refactoring and Pattern Application
Learning Objectives:
- Master try-except-finally blocks with proper exception hierarchy
- Create custom exception classes for domain-specific error handling
- Apply debugging techniques using debugger and logging
- Implement unit testing using unittest and pytest frameworks
- Use code quality tools: linters, formatters, and static analyzers
- Design robust error handling and recovery strategies
Available Materials:
Exception Handling and Debugging Guide (40 pages)
Custom Exception Design and Implementation
Debugging Tools and Techniques Tutorial
Unit Testing and Test-Driven Development
Code Quality Tools Configuration and Usage
35 Debugging and Error Handling Exercises
Logging and Monitoring Implementation
Professional Development Workflow Setup
Learning Objectives:
- Master Big O, Omega, and Theta notation with mathematical rigor
- Analyze time and space complexity of algorithms and data structures
- Apply amortized analysis for average-case complexity evaluation
- Compare and evaluate different algorithmic approaches
- Understand NP-completeness and computational complexity classes
- Design experiments to measure and validate algorithmic performance
Available Materials:
Algorithm Analysis Theory and Practice (50 pages)
Big O Notation and Asymptotic Analysis Guide
Complexity Analysis Examples and Case Studies
Amortized Analysis Techniques
NP-Completeness and Complexity Classes
40 Algorithm Analysis Problems
Performance Measurement and Benchmarking
Complexity Theory Applications
Learning Objectives:
- Implement and analyze linear and binary search algorithms
- Master sorting algorithms: bubble, selection, insertion, merge, quick, heap sort
- Understand algorithm stability, adaptability, and space requirements
- Apply divide-and-conquer and dynamic programming strategies
- Implement graph algorithms: DFS, BFS, and shortest path algorithms
- Design and optimize algorithms for specific problem domains
Available Materials:
Fundamental Algorithms Complete Reference (70 pages)
Sorting Algorithm Implementation and Analysis
Searching Techniques and Optimization
Divide-and-Conquer Algorithm Collection
Dynamic Programming Examples and Applications
Graph Algorithm Implementation Guide
60 Algorithm Implementation Challenges
Algorithm Visualization and Animation Tools
Learning Objectives:
- Plan and execute a substantial software development project
- Apply software engineering best practices and design methodologies
- Implement comprehensive testing and documentation strategies
- Use version control and collaboration tools effectively
- Present technical solutions with clear documentation and demonstration
- Reflect on learning progress and identify areas for continued development
Available Materials:
Project Planning and Management Guide (30 pages)
Software Engineering Best Practices
Documentation and Technical Writing Templates
Version Control and Collaboration Workflow
Code Review and Quality Assurance Checklist
Portfolio Development and Presentation Guidelines
Professional Development Resources
Continuing Education and Learning Pathways