Python for Data Science
Welcome to the Python for Data Science repository! This repository contains various Jupyter notebooks and datasets designed to help you learn and apply Python concepts in the context of data science.
Contents
Notebooks
Arithmetic Operations.ipynb: Basic arithmetic operations in Python.
Control Structures.ipynb: Introduction to control structures such as if-else statements and switch-case.
Datatypes.ipynb: Overview of different data types in Python.
Dictionary.ipynb: Working with dictionaries in Python.
Exception Handling.ipynb: Techniques for handling errors and exceptions in Python.
File Handling.ipynb: Methods for reading from and writing to files.
Functions.ipynb: Defining and using functions in Python.
Iterators & Generators.ipynb: Understanding iterators and generators.
Lists.ipynb: Working with lists and list operations.
Loops & Iteration.ipynb: Concepts of loops and iteration in Python.
Matplotlib-TL.ipynb: Introduction to data visualization with Matplotlib.
Matplotlib.ipynb: Advanced usage of Matplotlib for data visualization.
Normal_Distribution_+_CLT.ipynb: Analysis of normal distribution and the Central Limit Theorem.
NumPy.ipynb: Introduction to NumPy for numerical computations.
OOPs in Python.ipynb: Object-oriented programming concepts in Python.
Pandas.ipynb: Data manipulation and analysis with Pandas.
Seaborn-TL.ipynb: Introduction to data visualization with Seaborn.
Seaborn.ipynb: Advanced visualization techniques using Seaborn.
Sets.ipynb: Operations and usage of sets in Python.
String Operations.ipynb: String manipulation and operations.
Tuples.ipynb: Working with tuples in Python.
Variables & Keywords.ipynb: Understanding variables and keywords in Python.
map, reduce & filter.ipynb: Using map, reduce, and filter functions for data processing.
Dataset
Churn_Modelling.csv: Dataset used for modeling customer churn.
How to Use
Clone or Download the Repository: Clone this repository using Git or download it as a ZIP file.
Set Up Your Environment: Ensure you have Jupyter Notebook installed. You can install it via Anaconda or pip.
Open Jupyter Notebook: Navigate to the directory containing the notebooks and open them using Jupyter Notebook.
Explore and Execute Notebooks: Review the notebooks and run the cells to understand the concepts and code.
Requirements
Python 3.x
Jupyter Notebook
Required Libraries: NumPy, Pandas, Matplotlib, Seaborn (install via pip install numpy pandas matplotlib seaborn)