Learn Data Science 🚀

My personal experiments and notes during my journey of learning Data Science are preserved in this repository.

About the Project

This project serves as a kind of journal documenting my adventure in exploring Data Science. Each notebook contains notes on specific topics along with experiments that test the validity of the concepts discussed. I have aimed to keep my notes as free from mathematical formulas and dry academic theory as possible, favoring more understandable explanations and simplified notation. If you'd like to use my notes for learning, I believe they will definitely be helpful to you.

The project aims to:

• Document my educational journey.
• Share examples and insights that may help other beginners.
• Experiment with various techniques and write my own library.

Contents

Jupyter Notebooks:

• 01-stochastic-thinking.ipynb
• 02-data-visualization.ipynb
• 03-random-walks.ipynb
• 04-monte-carlo-simulation.ipynb
• 05-distributions.ipynb
• 06-confidence-intervals.ipynb
• 07-sampling.ipynb
• 08-the-central-limit-theorem.ipynb
• 09-linear-regression.ipynb

Requirements

To run the notebooks, you need:

• Python 3.12+
• Installed libraries listed in the requirements.txt file.

Environment setup:

git clone https://github.com/DanielFaltynowski/learn-data-science.git
cd learn-data-science  
pip install -r requirements.txt  

How to Use?

1. Open the repository in your favorite Jupyter environment:

jupyter notebook

2. Browse and run the notebooks in any order.

3. Experiment with the code and customize it to your needs.

Topics Covered

The project includes:

• Basics of statistics.
• Model fitting methods.
• Introduction to machine learning.

Contact

Have questions or suggestions? Feel free to reach out to me:

• Email: faltynowskidaniel6@gmail.com
• LinkedIn: Daniel Faltynowski

References

1. John V. Guttag. Introduction to Computation and Programming Using Python With Application to Understanding Data. MIT Press. 2016

2. StatQuest Website