Intro to Data Mining: Python Exercises
A collection of Python exercises covering basic to advanced concepts including data types, control structures, functions, and object-oriented programming.
Overview
This notebook contains a comprehensive collection of Python exercises designed for the Introduction to Data Mining course. It covers fundamental Python concepts that are essential for data analysis and manipulation.
Topics Covered
- Basic Python Syntax: Variables, data types, and operators
- Control Structures: Conditional statements and loops
- Functions: Defining and using functions for code reusability
- Data Structures: Lists, tuples, dictionaries, and sets
- Object-Oriented Programming: Classes and objects basics
Learning Objectives
By completing these exercises, you’ll gain proficiency in:
- Writing clean and efficient Python code
- Understanding Python’s core data structures
- Implementing functions for modular programming
- Working with basic object-oriented concepts
Skills Developed
These exercises build the foundation for more advanced data mining concepts covered in subsequent assignments.
- Problem-solving with Python
- Code organization and best practices
- Debugging and testing strategies
- Reading and understanding Python documentation
01Intro to Data Mining: Pandas & Numpy Exercises
Data manipulation exercises using Pandas and NumPy libraries for data analysis and numerical computing.
[Python][Pandas][NumPy]
02Intro to Data Mining: Scikit-Learn Exercises
Machine learning exercises using Scikit-Learn library covering classification, regression, and model evaluation techniques.
[Python][Machine Learning][Scikit-Learn]
03Intro to Data Mining: Final Project
Clustering wine based on their chemical properties using unsupervised learning techniques and comprehensive cluster analysis.
[Clustering][Unsupervised Learning][Data Mining]