Python for Scientists
An introductory Python course for physics students — with interactive exercises, quizzes, and downloadable notebooks.
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Python for Beginners
From "Hello World" to Data Science. A structured journey into the world of Python.
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Lecture Materials
Click on any lecture to expand details. All materials available as PDF downloads.
Getting Started with Python Programming
- Programming Languages Overview
- Why Python? Key Features
- Python Environment Setup
- Hello World Program
Key Concepts: Interpreted vs Compiled languages, Interactive programming, OOP fundamentals
Understanding Python's Core Data Structures
- Variables & Naming Rules
- Dynamic Typing
- Strings: Indexing & Slicing
- Numbers (int, float, complex)
- Lists, Tuples, Dictionaries
- Sets & Booleans
Program Logic and Decision Making
- If / Elif / Else Statements
- For Loops
- While Loops
- range(), enumerate(), zip()
Modular Programming and Code Reusability
- Defining Functions (def)
- Parameters & Arguments
- Return Values
- *args and **kwargs
Working with Files and External Libraries
- File Operations (r, w, a)
- Context Managers (with)
- Importing Modules
- PIP Package Manager
Writing Robust Python Code
- Try / Except Blocks
- Finally Clause
- Raising Exceptions
- Debugging Techniques
Introduction to Scientific Python
NumPy
Numerical computing with arrays
Pandas
Data manipulation and analysis
Matplotlib
Data visualization
Advanced Python Concepts
- Classes and Objects
- Encapsulation
- Inheritance
- Polymorphism
- Mean, Median, Mode
- Variance & Std Dev
Tutorial Materials Solutions Included
Hands-on practice with solved examples. Run notebooks in Google Colab or Jupyter.
Variables, Data Types & Strings
Keywords, Dynamic Typing, String Methods, Lists, Dictionaries
Data Structures Deep Dive
Advanced Lists, Tuples, Dictionaries, Sets
Loops and Functions
Pattern Questions, While Loops, Continue/Break
Functions Deep Dive
*args, List Operations, Palindrome, Prime Numbers
Files, Modules & Data Science
Log Analyzer, Custom Modules, NumPy Arrays, Matplotlib
NumPy, Pandas & Matplotlib
2D Arrays, DataFrames, Publication-Quality Plots
Object-Oriented Programming
Classes, __init__, self, Inheritance, super()
Quick Access Buttons
Each tutorial has two action buttons:
Open in Colab
Opens the notebook in Google Colab. Important: Click File → Save a copy in Drive to save your work!
Download
Download the .ipynb file to run locally in Jupyter Notebook.
Learning Path
Follow this journey from beginner to advanced Python programming
Introduction to Python
Programming basics, Environment setup, Hello World
- Python Syntax & Comments
- Variables & Assignment
- Input & Output
Data Types & Strings
Variables, Numbers, Strings, Lists, Dictionaries
- String Slicing & Methods
- List operations (append, pop)
- Dictionary keys & values
Flow Control
If/Else, For Loops, While Loops
- Conditional Logic
- Iterating with Loops
- Break & Continue
Functions
Defining functions, Parameters, Return values
- Defining Functions (def)
- Arguments & Return Values
- Scope & Lifetime
File Handling & Modules
Read/Write files, Context managers, PIP
- Reading & Writing Files
- Context Managers (with)
- Importing Modules
Error Handling
Try/Except, Debugging, Custom exceptions
- Try, Except, Finally
- Debugging Strategies
- Raising Exceptions
Data Science Libraries
NumPy, Pandas, Matplotlib
- NumPy Arrays
- Pandas DataFrames
- Matplotlib Plotting
OOP & Statistics
Classes, Inheritance, Statistics
- Classes & Objects
- Inheritance & Polymorphism
- Basic Statistics
Recommended YouTube Courses
Sleep through the lectures? No worries. Binge-watch these top-rated courses and become a pro overnight. (Popcorn not included).
Prerequisites
- Python 3.x installed
- Google Colab account (recommended)
- Text editor (VSCode, PyCharm)
Recommended Resources
Tips for Success
- Try problems yourself before looking at solutions
- Experiment with code modifications
- Read error messages carefully
- Don't skip the fundamentals
🐍 Python Output Challenge
Can you predict the output of these code snippets?