basics_series

This repository is about the essentials of data science. Although I am working on more advanced topics now, I wanted to go back to the basics in this repository. Because I believe strong fundamentals are quite important. In addition, I wanted to show my fundamental knowledge to my future employer and provide a clean summary for beginners. The repository has 3 parts: Python, libraries, and projects.

basics_series_python

This part is about the essentials of the Python programming language. It explains the fundamentals of a programming language in 5 sections.

1. Data Type and Structures
2. Flow Control Structures
3. Functional Programming
4. Object-Oriented Programming
5. Debugging

In this part, I was inspired by the Complete Python Bootcamp From Zero to Hero in Python course (https://www.udemy.com/course/complete-python-bootcamp/) and the documentatiton of the Python programming language.

basics_series_libraries

This part is about the essential Python libraries for data science. It includes 5 most common libraries for data analysis, data visualization, and machine learning.

1. numpy
2. pandas
3. matplotlib
4. seaborn
5. sckit-learn

In this part, I was inspired by the Python for Data Science and Machine Learning Bootcamp course (https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/) and the documentatiton of the libraries.

basics_series_projects

This part is about the essantial projects for data science. It includes 3 most common projects that focus on classification and regression.

1. Titanic
2. House Prices

In this part, I was inspired by Kaggle website (https://www.kaggle.com/).