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Courses
  • Python for Data Analytics
    • Python for Data Analytics Course
    • Python Basics
      • Overview of the Python Basics Module
      • Lesson 1 - Programming in Python
      • Lesson 2 - Variables and Data Types
      • Lesson 3 - For Loops and Iteration
      • Lesson 4 - Working with Lists
      • Lesson 5 - Conditional Statements
      • Lesson 6 - Python Dictionaries
      • Lesson 7 - Advanced Dictionaries and Frequency Tables
      • Lesson 8 - Python Functions
      • Lesson 9 - Python Functions: Arguments, Parameters, and Debugging
      • Lesson 10 - Working with Files
      • Lesson 11 - Exception Handling
      • Lesson 12 - List Comprehension
      • Lesson 13 - Lambda Functions
      • Lesson 14 - Filter and Map Functions
    • Python Advanced
      • Overview of the Python Advanced Module
      • Lesson 1 - Introduction to Object-Oriented Programming
      • Lesson 2 - Class Methods, Properties, and Encapsulation
      • Lesson 3 - Inheritance and Polymorphism
      • Lesson 4 - Special Methods and Python's Data Model
      • Lesson 5 - Advanced Function Concepts
      • Lesson 6 - Decorators and Metaprogramming
      • Lesson 7 - Iterators and the Iterator Protocol
      • Lesson 8 - Generators and Memory-Efficient Processing
      • Lesson 9 - Context Managers and Resource Management
      • Lesson 10 - Regular Expressions for Text Processing
      • Lesson 11 - Advanced Collections and Data Structures
      • Lesson 12 - Working with Dates, Times, and Timezones
      • Lesson 13 - Modules and Packages
      • Lesson 14 - Virtual Environments and Dependency Management
    • NumPy Fundamentals
      • Overview of the NumPy Fundamentals Module
      • Lesson 1 - NumPy Essentials and 1D Arrays
      • Lesson 2 - 2D Arrays and Working with CSV Data
      • Lesson 3 - Selecting and Slicing Data
      • Lesson 4 - Vector Operations and Calculations
      • Lesson 5 - Boolean Indexing and Data Filtering
      • Lesson 6 - Modifying Data and Assignment
    • Pandas Data Analysis
      • Pandas Data Analysis Module Overview
      • Lesson 1 - Introduction to Pandas and Series
      • Lesson 2 - DataFrames and Reading Data
      • Lesson 3 - Selecting Data with .loc[]
      • Lesson 4 - Selecting Data with .iloc[]
      • Lesson 5 - Series Operations and Value Counts
      • Lesson 6 - DateTime Fundamentals
      • Lesson 7 - Boolean Filtering in Pandas
      • Lesson 8 - Sorting and Ranking
      • Lesson 9 - Adding and Modifying Columns
      • Lesson 10 - Apply, Map, and Transform Functions
      • Lesson 11 - Handling Missing Data
      • Lesson 12 - Data Type Conversion and Cleaning
      • Lesson 13 - Removing Duplicates and Handling Outliers
      • Lesson 14 - Working with String Data
      • Lesson 15 - GroupBy and Aggregation
      • Lesson 16 - Pivot Tables
      • Lesson 17 - Concatenating DataFrames
      • Lesson 18 - Merging and Joining DataFrames
      • Lesson 19 - MultiIndex and Hierarchical Data
      • Lesson 20 - Window Functions and Rolling Operations
      • Lesson 21 - Final Project: Real-World Data Analysis
    • Data Visualization with Python
      • Data Visualization with Python - Course Overview
      • Lesson 1 - Understanding Graphs and Coordinates
      • Lesson 2 - Introduction to Matplotlib
      • Lesson 3 - Customizing Plots
      • Lesson 4 - Multiple Lines and Series
      • Lesson 5 - Scatter Plots Basics
      • Lesson 6 - Creating Scatter Plots
      • Lesson 7 - Understanding Correlation
      • Lesson 8 - Comparing Correlations
      • Lesson 9 - Understanding Distributions
      • Lesson 10 - Creating Bar Plots
      • Lesson 11 - Creating Histograms
      • Lesson 12 - Comparing Distributions
      • Lesson 13 - Pandas Plot Method
      • Lesson 14 - Creating Subplots
      • Lesson 15 - Grid Charts
      • Lesson 16 - Final Project - Traffic Analysis
  • Software Engineering Fundamentals
    • Course Overview
    • Lesson 1: Introduction to Software Engineering
    • Lesson 2: Implementing SDLC in Real-World Projects
    • Lesson 3: Software Development Methodologies
    • Lesson 4: Software Design and Architecture
    • Lesson 5: Design Patterns
    • Lesson 6: Object-Oriented Programming
    • Lesson 7: Clean Code and Best Practices
    • Lesson 8: Version Control with Git
    • Lesson 9: Software Testing
    • Lesson 10: Behaviour-Driven Development
    • Lesson 11: Gherkin Language
    • Lesson 12: CI/CD and DevOps
    • Lesson 13: Security Best Practices
  • Free Data Science & Analytics Courses
  • Tutorials
    • Installing Python on macOS
    • Installing Python on Windows
    • How to Set Up Visual Studio Code
    • Install PostgreSQL on macOS
    • Install PostgreSQL on Windows
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Software Engineering Fundamentals

Learn essential software engineering fundamentals including SOLID principles, design patterns, SDLC methodologies, and industry best practices.

Course Overview →
Lesson 1: Introduction to Software Engineering →
Lesson 10: Behaviour-Driven Development →
Lesson 11: Gherkin Language →
Lesson 12: CI/CD and DevOps →
Lesson 13: Security Best Practices →
Lesson 2: Implementing SDLC in Real-World Projects →
Lesson 3: Software Development Methodologies →
Lesson 4: Software Design and Architecture →
Lesson 5: Design Patterns →
Lesson 6: Object-Oriented Programming →
Lesson 7: Clean Code and Best Practices →
Lesson 8: Version Control With Git →
Lesson 9: Software Testing →
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