Python Tutorial for Beginners – Complete Course with AI Tools

Learn Python programming from scratch with this modern course that integrates AI tools to accelerate your learning. Whether you’re a scientist, engineer, or just curious about coding, this course provides a solid foundation in Python fundamentals, NumPy, and Matplotlib.

Course Materials

Download Links:
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– Conda Cheat Sheet

What You’ll Learn

Chapter 1: Basics & Installation
– Setting up your Python environment
– Installing JupyterLab & Anaconda (or modern alternatives)
– Understanding AI tools integration
– Jupyter Notebooks vs Python Scripts

Chapter 2: Variables and Data Types
– Strings and f-strings
– Integers and basic math
– Data structures: Tuples, Lists, Dictionaries
– Understanding cell state in notebooks

Chapter 3: Conditional Statements
– If/elif/else statements
– Boolean variables and logical operators
– Comparison operators
– Multiple conditions

Chapter 4: Functions
– Defining and calling functions
– Multiple return values
– Variable scope
– Error handling and debugging

Chapter 5: Loops
– While loops
– For loops
– Iteration patterns
– Common loop pitfalls

Chapter 6: Keyboard Shortcuts
– Essential shortcuts for faster coding
– Jupyter-specific commands

Chapter 7: Virtual Environments
– Why use virtual environments?
– Managing environments with conda
– Creating and sharing YAML files
– Troubleshooting environment issues

Chapter 8: NumPy Basics
– Arrays and array operations
– Mathematical functions
– Array manipulation

Chapter 9: Matplotlib Visualization
– Line plots and scatter plots
– Histograms
– Contour plots and pie charts
– Customizing your plots

Chapter 10: FAQ
– How to think like a programmer
– Choosing the right AI tools
– Common Python libraries for data analysis
– Improving your skills further

Modern Learning Approach

AI Tool Integration:
– ChatGPT for code explanation
– Claude for problem-solving
– Using AI tools to accelerate learning
– Focus on understanding, not just copying

The key is using AI tools to help you learn faster, not to replace learning. When you get stuck, use AI to understand why, then apply that knowledge yourself.

Practical Tips:
– Start with small, manageable code cells
– Work incrementally – test as you go
– Use AI tools to accelerate learning, not skip it
– Focus on understanding core concepts

Common Beginner Pitfalls

Challenges You’ll Face:
– Understanding error messages
– Thinking like a programmer
– Managing virtual environments
– Debugging effectively

Best Practices:
– Keep code cells small and focused
– Test code frequently
– Use meaningful variable names
– Avoid Python keywords when naming variables
– Document your code with comments

About the Setup (Important Update)

This course was filmed using JupyterLab Desktop and Anaconda. Since then:

– JupyterLab Desktop is discontinued
– Anaconda now requires paid licenses for most institutions

For beginners in 2025, I now recommend:

– Package Manager: Miniforge (free, works exactly like Anaconda)
– IDE: PyCharm Community Edition (zero configuration needed)

Coming Soon: I’m working on an updated version of this course where Chapter 1 (installation) will be replaced with current setup instructions for Miniforge and PyCharm/VS Code. The rest of the course remains unchanged – the Python fundamentals are the same regardless of which IDE you use.

In the meantime, you can follow along with the exercises using any modern Python setup. Chapters 2-10 teach Python fundamentals that work identically in any environment.

Next Steps

To get the most out of this course:

1. Download the course materials above
2. Set up your Python environment (see updated recommendations)
3. Follow along with the video
4. Practice with the exercises
5. Use AI tools for support, not replacement
6. Join the community for additional help

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Ready to dive deeper? Check out our advanced courses at Training Scientists for more specialized content in scientific computing and data analysis.