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1. Introduction
1.1. Computer glossary
1.2. Your computer
1.3. AI in learning programmimng
1.4. Resources
2. Installation and setup
2.1. Software installation
2.2. Setting up Conda environments
2.3. Setting-up VS Code
2.4. Setting up Git
3. Terminal
3.1. Bash commands
3.2. Exercises
4. Getting started with Python and Git
4.1. About Python
4.2. Version control with Git
5. Python basics
5.1. Data types
5.2. Data typing
5.3. Python as a calculator
5.4. Sending data to Python
5.5. Understanding errors
5.6. Exercises
6. AI in programming
6.1. What is AI?
6.2. AI Prompting
6.3. A conversation with AI
7. Good coding practices
7.1. Understandable and robust code
7.2. PEP 8 style guidelines
8. Flow control and algorithmic thinking
8.1. Conditional statements
8.2. Loops
8.3. Algorithmic thinking
8.4. Pseudocode
8.5. Exercises
9. Packages and NumPy
9.1. Libraries, documentation, and NumPy
9.2. NumPy arrays
9.3. Extracting elements
9.4. Vectorization
9.5. Exercises
10. Functions and debugging
10.1. Functions in Python
10.2. Variable scope
10.3. Debugging in VS Code
10.4. Exercises
11. Data files
11.1. Working with data files
11.2. Files with NumPy
11.3. Files with pandas
11.4. Exercises
12. Data visualization
12.1. Data visualization in Python
12.2. Plotting with Matplotlib
12.3. Plotting with seaborn
12.4. Exercises
13. Biomolecular simulations
13.1. Graphical environment
13.2. Exercises
13.3. Wrapping up your journey
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