Python for Data Science - 3 Hour Beginner Course

Course Overview

This comprehensive course is designed to prepare complete beginners for data science work in Python. The course consists of 7 Jupyter notebooks that progressively build the skills needed to understand and work with advanced data science notebooks.

Duration: 3 hours (180 minutes)
Prerequisites: None - designed for complete beginners
Goal: Prepare students to understand and execute data science notebooks using NumPy, matplotlib, and machine learning concepts

Course Structure

Module 1: Python Fundamentals (45 minutes)

• Notebook 1: Python Basics (20 minutes) - Variables, data types, basic operations
• Notebook 2: Control Structures (25 minutes) - if/else, loops, basic error handling

Module 2: Data Structures and Operations (50 minutes)

• Notebook 3: Lists and Data Structures (25 minutes) - Lists, indexing, slicing, tuples
• Notebook 4: Dictionaries and Advanced Operations (25 minutes) - Dictionaries, nested structures

Module 2.5: Pandas Preview (15 minutes)

• Notebook 4.5: Pandas Preview (15 minutes) - Quick introduction to pandas basics

Module 3: Functions and Code Organization (35 minutes)

• Notebook 5: Functions and Modules (20 minutes) - Function definition, parameters, modules
• Break (15 minutes)

Module 4: Data Science Libraries (50 minutes)

• Notebook 6: NumPy Fundamentals (25 minutes) - Arrays, operations, matrix math
• Notebook 7: Matplotlib Basics (25 minutes) - Plotting, visualization, customization

Capstone Project: Weather Data Analysis (45-60 minutes)

• Notebook 8: Capstone Project - Apply all learned skills to real-world data analysis

Learning Objectives

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

1. Write basic Python code using variables, data types, and control structures
2. Work with Python data structures including lists, dictionaries, and nested structures
3. Use NumPy for numerical computations and array operations
4. Create visualizations using matplotlib
5. Understand and execute code from data science notebooks
6. Import and use Python modules and packages

Key Concepts Covered

Essential for Advanced Notebooks:

• ✅ List slicing (X[0:3]) - Used extensively in ML notebooks
• ✅ NumPy arrays and operations (.dot(), .T, broadcasting)
• ✅ Matplotlib plotting (plt.plot(), plt.xlabel(), etc.)
• ✅ Import statements (import numpy as np, import matplotlib.pyplot as plt)
• ✅ For loops and iteration over data
• ✅ String formatting (% operator, f-strings)
• ✅ Basic mathematical operations and statistical concepts
• ✅ Function calls and method usage

Getting Started

Prerequisites

• Jupyter Notebook or JupyterLab installed
• Python 3.7+ with the following packages:
  • numpy
  • matplotlib

Installation

If you don't have the required packages, install them using:

pip install numpy matplotlib jupyter

Running the Course

1. Download all notebook files to a single directory
2. Open Jupyter Notebook in that directory: jupyter notebook
3. Start with 01_python_basics.ipynb and work through in order
4. Execute each cell by pressing Shift+Enter
5. Complete the practice exercises in each notebook

Course Files

1. 01_python_basics.ipynb - Variables, data types, basic operations
2. 02_control_structures.ipynb - Conditional statements, loops, error handling
3. 03_lists_data_structures.ipynb - Lists, indexing, slicing, tuples
4. 04_dictionaries_advanced.ipynb - Dictionaries, nested data structures
5. 05_functions_modules.ipynb - Functions, parameters, modules, imports
6. 06_numpy_fundamentals.ipynb - NumPy arrays, operations, linear algebra
7. 07_matplotlib_basics.ipynb - Plotting, visualization, customization

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

Assessment and Practice

Each notebook includes:

• Hands-on exercises with real data science scenarios
• Progressive difficulty building on previous concepts
• Real examples that mirror advanced ML notebooks
• Practice problems for reinforcement

Next Steps After Completion

Students will be ready to:

1. Understand advanced notebooks with machine learning algorithms
2. Work with pandas for data manipulation
3. Use scikit-learn for machine learning
4. Explore real datasets and perform analysis
5. Build their own 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

Course Validation

This course has been designed to optimally prepare students for advanced data science notebooks by:

• ✅ Analyzing actual ML notebooks to identify required concepts
• ✅ Progressive skill building from basics to advanced topics
• ✅ Hands-on practice with realistic data science scenarios
• ✅ Comprehensive coverage of essential Python and data science tools
• ✅ Practical examples that mirror real-world usage patterns

Students completing this course will have the foundational knowledge needed to understand and work with machine learning algorithms, linear regression, decision trees, and neural networks as implemented in Python.

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Happy Learning! 🐍📊🚀

New Course Features (2025 Update)

🎯 Interactive Learning Elements

• Mini-Challenges: Hands-on exercises at the end of each notebook
• Self-Assessment Checklists: Help students verify their understanding
• Try-It-Yourself Sections: Interactive code challenges throughout

🔧 Error Handling and Troubleshooting

• Common Error Examples: Real errors students will encounter with solutions
• Debugging Tips: Practical strategies for fixing code issues
• Input Validation: Best practices for handling different data types

📊 Enhanced Data Science Context

• Real-World Examples: Practical scenarios from actual data science work
• ML Notebook Patterns: Code patterns students will see in machine learning
• Best Practices: Professional data science coding standards

📚 Additional Resources

• Python Data Science Cheat Sheet: Quick reference for common operations
• Capstone Project: Comprehensive weather data analysis project
• Assessment Tools: Self-evaluation and progress tracking

🎓 Progressive Skill Building

Each notebook now includes:

• Clear learning objectives
• Real-world context and applications
• Progressive difficulty with proper scaffolding
• Assessment and validation opportunities
• Next steps and connections to advanced topics