Data Science and Artificial Intelligence for Teens

          Module 1: Fundamentals of Data Science and AI
Section 1: Introduction to Data Science
- Lesson 1: What Is Data Science?
  - Module 1, Section 1, Lesson 1: What Is Data Science?
- Lesson 2: Data Science in Everyday Life
  - Module 1, Section 1, Lesson 2: Data Science in Everyday Life

Section 2: Introduction to Artificial Intelligence
- Lesson 1: What Is Artificial Intelligence?
  - Module 1, Section 2, Lesson 1: What Is Artificial Intelligence?
- Lesson 2: The History and Impact of AI
  - Module 1, Section 2, Lesson 2: The History and Impact of AI

Section 3: Basic Concepts in Data Handling
- Lesson 1: Understanding Data Types
  - Module 1, Section 3, Lesson 1: Understanding Data Types
- Lesson 2: Collecting and Organizing Data
  - Module 1, Section 3, Lesson 2: Collecting and Organizing Data

Section 4: Tools and Technologies Overview
- Lesson 1: Overview of Python for Data Science
  - Module 1, Section 4, Lesson 1: Overview of Python for Data Science
- Lesson 2: Introduction to Essential Data Tools
  - Module 1, Section 4, Lesson 2: Introduction to Essential Data Tools

Section 5: Ethics and Responsibility in Data Science
- Lesson 1: Understanding Data Ethics
  - Module 1, Section 5, Lesson 1: Understanding Data Ethics
- Lesson 2: Responsible AI Practices
  - Module 1, Section 5, Lesson 2: Responsible AI Practices


Module 2: Python Programming for Data Science
Section 1: Python Basics
- Lesson 1: Setting Up the Python Environment
  - Module 2, Section 1, Lesson 1: Setting Up the Python Environment
- Lesson 2: Basic Python Syntax and Operations
  - Module 2, Section 1, Lesson 2: Basic Python Syntax and Operations

Section 2: Data Structures in Python
- Lesson 1: Understanding Lists, Tuples, and Dictionaries
  - Module 2, Section 2, Lesson 1: Understanding Lists, Tuples, and Dictionaries
- Lesson 2: Practical Uses of Python Data Structures
  - Module 2, Section 2, Lesson 2: Practical Uses of Python Data Structures

Section 3: Control Flow and Functions
- Lesson 1: Using If Statements and Loops in Python
  - Module 2, Section 3, Lesson 1: Using If Statements and Loops in Python
- Lesson 2: Defining and Using Functions
  - Module 2, Section 3, Lesson 2: Defining and Using Functions

Section 4: Basics of Libraries for Data Science
- Lesson 1: Introduction to NumPy
  - Module 2, Section 4, Lesson 1: Introduction to NumPy
- Lesson 2: Working with Pandas for Data Manipulation
  - Module 2, Section 4, Lesson 2: Working with Pandas for Data Manipulation

Section 5: Writing and Debugging Code
- Lesson 1: Writing Clear and Maintainable Code
  - Module 2, Section 5, Lesson 1: Writing Clear and Maintainable Code
- Lesson 2: Introduction to Debugging Techniques
  - Module 2, Section 5, Lesson 2: Introduction to Debugging Techniques


Module 3: Data Visualization and Analysis
Section 1: Data Visualization Principles
- Lesson 1: Why Visualize Data?
  - Module 3, Section 1, Lesson 1: Why Visualize Data?
- Lesson 2: Selecting the Appropriate Graphs and Charts
  - Module 3, Section 1, Lesson 2: Selecting the Appropriate Graphs and Charts

Section 2: Introduction to Visualization Tools
- Lesson 1: Getting Started with Matplotlib
  - Module 3, Section 2, Lesson 1: Getting Started with Matplotlib
- Lesson 2: An Introduction to Seaborn
  - Module 3, Section 2, Lesson 2: An Introduction to Seaborn

Section 3: Data Cleaning and Preparation
- Lesson 1: Understanding the Importance of Data Cleaning
  - Module 3, Section 3, Lesson 1: Understanding the Importance of Data Cleaning
- Lesson 2: Basic Data Preprocessing Techniques
  - Module 3, Section 3, Lesson 2: Basic Data Preprocessing Techniques

Section 4: Exploratory Data Analysis (EDA)
- Lesson 1: Identifying Trends and Patterns in Data
  - Module 3, Section 4, Lesson 1: Identifying Trends and Patterns in Data
- Lesson 2: Transforming and Summarizing Data
  - Module 3, Section 4, Lesson 2: Transforming and Summarizing Data

Section 5: Case Study in Data Visualization
- Lesson 1: Step-by-Step Data Visualization Process
  - Module 3, Section 5, Lesson 1: Step-by-Step Data Visualization Process
- Lesson 2: Interpreting Visualization Results
  - Module 3, Section 5, Lesson 2: Interpreting Visualization Results


Module 4: Introduction to Machine Learning
Section 1: Machine Learning Basics
- Lesson 1: What Is Machine Learning?
  - Module 4, Section 1, Lesson 1: What Is Machine Learning?
- Lesson 2: Overview of Different Types of Machine Learning
  - Module 4, Section 1, Lesson 2: Overview of Different Types of Machine Learning

Section 2: Preparing Data for Machine Learning
- Lesson 1: Understanding Data Splitting: Training versus Testing
  - Module 4, Section 2, Lesson 1: Understanding Data Splitting: Training versus Testing
- Lesson 2: Fundamentals of Feature Selection
  - Module 4, Section 2, Lesson 2: Fundamentals of Feature Selection

Section 3: Introduction to Supervised Learning
- Lesson 1: Basics of Regression Models
  - Module 4, Section 3, Lesson 1: Basics of Regression Models
- Lesson 2: Understanding Classification Methods
  - Module 4, Section 3, Lesson 2: Understanding Classification Methods

Section 4: Building a Simple Machine Learning Model
- Lesson 1: Creating a Basic Model with Python
  - Module 4, Section 4, Lesson 1: Creating a Basic Model with Python
- Lesson 2: Evaluating Model Performance
  - Module 4, Section 4, Lesson 2: Evaluating Model Performance

Section 5: Improving Machine Learning Models
- Lesson 1: Recognizing Common Errors in Model Building
  - Module 4, Section 5, Lesson 1: Recognizing Common Errors in Model Building
- Lesson 2: Introduction to Basic Model Tuning Techniques
  - Module 4, Section 5, Lesson 2: Introduction to Basic Model Tuning Techniques


Module 5: AI Projects and Ethical Considerations
Section 1: Planning AI Projects
- Lesson 1: Defining Project Goals and Objectives
  - Module 5, Section 1, Lesson 1: Defining Project Goals and Objectives
- Lesson 2: Identifying Key Data Requirements
  - Module 5, Section 1, Lesson 2: Identifying Key Data Requirements

Section 2: Exploring AI Tools and Platforms
- Lesson 1: Introduction to TensorFlow Basics
  - Module 5, Section 2, Lesson 1: Introduction to TensorFlow Basics
- Lesson 2: Overview of Open-Source AI Tools
  - Module 5, Section 2, Lesson 2: Overview of Open-Source AI Tools

Section 3: Mini Projects in Artificial Intelligence
- Lesson 1: Planning and Selecting a Mini Project
  - Module 5, Section 3, Lesson 1: Planning and Selecting a Mini Project
- Lesson 2: Initial Model Building for a Mini Project
  - Module 5, Section 3, Lesson 2: Initial Model Building for a Mini Project

Section 4: Presenting and Interpreting AI Results
- Lesson 1: Creating Effective Reports for AI Projects
  - Module 5, Section 4, Lesson 1: Creating Effective Reports for AI Projects
- Lesson 2: Communicating Data Findings Clearly
  - Module 5, Section 4, Lesson 2: Communicating Data Findings Clearly

Section 5: Ethical AI and Future Directions
- Lesson 1: Exploring Ethical Considerations in AI
  - Module 5, Section 5, Lesson 1: Exploring Ethical Considerations in AI
- Lesson 2: An Overview of Future Trends in AI and Data Science
  - Module 5, Section 5, Lesson 2: An Overview of Future Trends in AI and Data Science