https://github.com/atrkhomeini/learn_data-analysis-with-python

Python packages are a powerful programming language for data analysis, enabling integration with web apps and statistics code in production databases. Despite their infancy, Python has improved significantly over the years, offering tools like NumPy, Pandas, and matplotlib for data manipulation and graphics creation.
https://github.com/atrkhomeini/learn_data-analysis-with-python

Last synced: 10 days ago
JSON representation

Python packages are a powerful programming language for data analysis, enabling integration with web apps and statistics code in production databases. Despite their infancy, Python has improved significantly over the years, offering tools like NumPy, Pandas, and matplotlib for data manipulation and graphics creation.

• Host: GitHub
• URL: https://github.com/atrkhomeini/learn_data-analysis-with-python
• Owner: atrkhomeini
• Created: 2023-08-26T20:04:33.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-12-18T17:15:14.000Z (about 1 year ago)
• Last Synced: 2024-12-08T01:13:13.847Z (2 months ago)
• Language: Jupyter Notebook
• Size: 5.24 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Data-Analysis-with-Python

Python packages are a powerful programming language for data analysis, enabling integration with web apps and statistics code in production databases. Despite their infancy, Python has improved significantly over the years, offering tools like NumPy, Pandas, and matplotlib for data manipulation and graphics creation.

Course Outline

INTRODUCTION TO PYTHON AND BASIC STATISTICS

Course Description

Python is an easy to learn, powerful programming language. You can use Python when your data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database. Being a full-fledged programming language, it’s a great tool to implement algorithms for production use.

While the infancy of Python packages for data analysis was an issue in the past, this has improved significantly over the years. In this course, you will learn about NumPy (Links to an external site.)Links to an external site.  (Links to an external site.)Links to an external site. / Pandas (data manipulation) for data analysis and matplotlib  (Links to an external site.)Links to an external site. (to make graphics). You will also learn about scikit-learn  (Links to an external site.)Links to an external site. for machine learning in future courses.

 

Seaborn for data visualization

Univariate analysis – Dist plot, count plot, Boxplot, Bar chart

Bivariate analysis – Pair plot, Reg plot, Joint plot, Point plot, Factor plot, Strip plot, Swarm plot

Case Study

Tools

Jupyter Notebook.