Learn Python: A Course Designed Specifically for Data Science and AI

From "What's Python?" to analyzing real datasets in just 3 hours

Course Overview

This comprehensive course bridges the gap between complete programming beginners and functional data science practitioners. Unlike typical Python courses that teach theoretical concepts, every lesson directly prepares you for real data science work.

Duration: 3 hours (180 minutes)
Prerequisites: None - designed for complete beginners
Goal: Master the foundational Python skills needed to understand and execute advanced data science notebooks

What Makes This Course Different?

🎯 Focused on Data Science

• Every concept connects directly to real data science workflows
• Learn list slicing (X[0:3]) used in virtually every ML notebook
• Master NumPy operations that power machine learning algorithms
• Practice string formatting for data analysis reports

**📊 Real-World Context **

• Calculate financial interest instead of printing "Hello, World!"
• Analyze test scores and weather data
• Work with realistic datasets and scenarios
• Build projects that mirror actual data science work

🏗️ Progressive Skill Building

• Each notebook builds on the previous one
• Concepts are introduced when you need them
• No overwhelming theory dumps
• Solid foundation that won't crumble with advanced topics

Course Structure

Module 1: Python Fundamentals (45 minutes)

• Notebook 1: Python Basics (20 minutes) - Master variables, data types, and operations through practical examples like calculating investment returns and formatting data analysis reports
• Notebook 2: Control Structures (25 minutes) - Learn to make decisions and repeat operations with real scenarios like temperature analysis and data quality checking

Module 2: Data Structures and Operations (50 minutes)

• Notebook 3: Lists and Data Structures (25 minutes) - Master the list operations you'll use in every data science project, from indexing to slicing to nested structures
• Notebook 4: Dictionaries and Advanced Operations (25 minutes) - Work with key-value structures that form the backbone of data manipulation and API interactions

Module 3: Pandas Introduction (15 minutes)

• Notebook 5: Pandas Preview (15 minutes) - Get a sneak peek at the most important data science library without overwhelming complexity

Module 4: Functions and Code Organization (35 minutes)

• Notebook 6: Functions and Modules (20 minutes) - Learn to write clean, reusable code that you can maintain and scale
• Break (15 minutes)

Module 5: Data Science Libraries (50 minutes)

• Notebook 7: NumPy Fundamentals (25 minutes) - Master the numerical computing library that powers everything from simple statistics to complex machine learning algorithms
• Notebook 8: Matplotlib Basics (25 minutes) - Create visualizations that turn raw data into compelling insights and actionable intelligence

Capstone Project: Real-World Application (45-60 minutes)

• Notebook 9: Weather Data Analysis - Put it all together in a comprehensive project analyzing real weather data from multiple cities

Learning Objectives

By the end of this course, students will be able to:

Core Python Skills

1. Write clean, professional Python code using variables, data types, and control structures
2. Master data structures including lists, dictionaries, and nested structures with confidence
3. Understand and debug common Python errors with systematic approaches

Data Science Fundamentals

4. Use NumPy for numerical computations and array operations that power ML algorithms
5. Create professional visualizations using matplotlib for data storytelling
6. Work with pandas DataFrames for data manipulation and analysis

Real-World Application

7. Read and understand advanced data science notebooks and ML code
8. Apply Python skills to solve realistic data science problems
9. Think like a data scientist with proper problem-solving approaches

Getting Started

Prerequisites

Absolutely none. We start from "What is a variable?" and build from there. Perfect for:

• Business professionals who want to make data-driven decisions
• Researchers looking to analyze data more effectively
• Students preparing for a career in tech
• Anyone curious about the power of data science

Time Investment

• Core Learning: 3 hours of focused study
• Practice & Mastery: Additional 2-3 hours working through exercises
• Total Value: A solid foundation for years of data science growth

What You'll Need

• A computer with internet access
• The desire to learn and experiment
• Patience with yourself (every expert was once a beginner)

Quick Setup (Recommended)

The easiest way to get started is using the provided setup script:

# Clone or download this repository
cd Data-Science-AI-Python-Course

# Run the setup script (macOS/Linux)
./setup.sh

# Or manually run the commands:
# python3 -m venv venv
# source venv/bin/activate
# pip install -r requirements.txt

This will:

• Create a virtual environment (venv/)
• Install all required packages
• Set up a Jupyter kernel specifically for this course

Manual Setup

If you prefer to set up manually:

Prerequisites

• Python 3.7+ installed on your system
• pip package manager

Installation

1. Create a virtual environment:

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install required packages:

pip install -r requirements.txt

3. Install Jupyter kernel:

python -m ipykernel install --user --name=data-science-course --display-name="Python (Data Science Course)"

Running the Course

1. Activate your virtual environment:

source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Start Jupyter Notebook:

jupyter notebook

3. In Jupyter, make sure to select the "Python (Data Science Course)" kernel
4. Start with 01_python_basics.ipynb and work through in order
5. Execute each cell by pressing Shift+Enter
6. Complete the practice exercises in each notebook

Deactivating the Environment

When you're done working, deactivate the virtual environment:

deactivate

Course Files

1. 01_python_basics.ipynb - Variables, data types, basic operations with real financial calculations
2. 02_control_structures.ipynb - Conditional statements, loops, error handling with temperature analysis
3. 03_lists_data_structures.ipynb - Lists, indexing, slicing, tuples for data manipulation
4. 04_dictionaries_advanced.ipynb - Dictionaries, nested data structures for API-like data
5. 05_pandas_preview.ipynb - Your first taste of the data science ecosystem
6. 06_functions_modules.ipynb - Functions, parameters, modules for clean, reusable code
7. 07_numpy_fundamentals.ipynb - NumPy arrays, operations, the foundation of ML
8. 08_matplotlib_basics.ipynb - Professional data visualization and storytelling
9. 09_capstone_project.ipynb - Comprehensive weather data analysis - everything together

Teaching Notes

For Instructors:

• Each notebook includes detailed explanations and examples
• Practice exercises are provided throughout
• Notebooks build progressively - don't skip ahead
• Encourage students to experiment with the code
• Allow extra time for students who need it

Pacing Guidelines:

• Beginners: May need 4-5 hours total
• Some programming experience: 3 hours as designed
• Quick learners: May finish in 2.5 hours

Common Issues:

• Import errors: Ensure numpy and matplotlib are installed
• Jupyter issues: Make sure Jupyter is properly installed and running
• Syntax errors: Emphasize proper indentation in Python

Next Steps After Completion

Students will be ready to:

1. Understand advanced notebooks with machine learning algorithms
2. Work with pandas for data manipulation and cleaning
3. Use scikit-learn for machine learning without syntax confusion
4. Explore real datasets and perform meaningful analysis
5. Build their own data science projects with confidence
6. Read and contribute to open-source data science projects

Troubleshooting

Common Issues:

Jupyter won't start:

pip install --upgrade jupyter
jupyter notebook

Import errors:

pip install numpy matplotlib

Plots not showing:

• Make sure %matplotlib inline is executed
• Try restarting the Jupyter kernel

Code not working:

• Check for proper indentation
• Ensure all cells are executed in order
• Restart kernel and run all cells if needed

Support and Resources

Additional Learning:

• Official Python Tutorial
• NumPy Documentation
• Matplotlib Tutorials

Practice Datasets:

• Kaggle Learn
• UCI Machine Learning Repository
• Seaborn Built-in Datasets

Take the Next Step

⭐ Star this repo if you find it helpful
🍴 Fork it to customize for your own learning
💬 Share your progress with the community
📝 Contribute improvements and suggestions

---

Remember: Every expert was once a beginner. The only difference is they started.

What will you build with your data science skills?

---