https://github.com/karishmagupta05/udemy-course-analysis

This project analyzes Udemy courses using Exploratory Data Analysis (EDA) techniques to uncover insights about course trends, pricing, subscriber counts, and popularity. By leveraging Python, Pandas, and data visualization libraries, we extract meaningful information from the dataset.
https://github.com/karishmagupta05/udemy-course-analysis

data-analysis data-visualization eda jupiter-notebook pandas python

Last synced: 3 months ago
JSON representation

This project analyzes Udemy courses using Exploratory Data Analysis (EDA) techniques to uncover insights about course trends, pricing, subscriber counts, and popularity. By leveraging Python, Pandas, and data visualization libraries, we extract meaningful information from the dataset.

• Host: GitHub
• URL: https://github.com/karishmagupta05/udemy-course-analysis
• Owner: Karishmagupta05
• Created: 2025-02-21T04:49:25.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-21T04:57:35.000Z (over 1 year ago)
• Last Synced: 2025-10-08T01:53:30.758Z (9 months ago)
• Topics: data-analysis, data-visualization, eda, jupiter-notebook, pandas, python
• Language: Jupyter Notebook
• Homepage:
• Size: 266 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Udemy Course Analysis

## Project Overview

This project analyzes Udemy courses using Exploratory Data Analysis (EDA) techniques to uncover insights about course trends, pricing, subscriber counts, and popularity. By leveraging Python, Pandas, and data visualization libraries, we extract meaningful information from the dataset.

## Features & Analysis Covered

1. **Display Top 10 Rows of The Dataset**  

2. **Check Last 5 Rows of The Dataset**  

3. **Find Shape of Our Dataset (Number of Rows and Columns)**  

4. **Dataset Information**  

   - Total number of rows and columns  

   - Datatypes of each column  

   - Memory usage  

5. **Check for Missing Values**  

   - Identify and handle null values.

6. **Check for Duplicate Data and Drop Them**  

   - Ensure data integrity by removing duplicate records.

7. **Find the Number of Courses Per Subject**  

8. **Analyze Course Levels**  

   - Identify which course levels (Beginner, Intermediate, Advanced) are offered.

9. **Display Count of Paid and Free Courses**  

   - Compare the availability of free and paid courses.

10. **Which Course Type (Free or Paid) Has More Lectures?**  

    - Determine if free or paid courses provide more content.

11. **Which Course Type (Free or Paid) Has More Subscribers?**  

    - Analyze subscriber preferences for free vs. paid courses.

12. **Which Level Has the Highest Number of Subscribers?**  

13. **Find the Most Popular Course Title**  

    - Discover the most subscribed Udemy course.

14. **Display the 10 Most Popular Courses Based on Subscribers**  

    - Showcase the top-performing courses.

15. **Find the Course with the Highest Number of Reviews**  

    - Identify the course receiving maximum feedback.

16. **Does Price Affect the Number of Reviews?**  

    - Analyze the relationship between pricing and user reviews.

17. **Find the Total Number of Python Courses**  

    - Explore the demand for Python-related courses.

18. **Display the 10 Most Popular Python Courses**  

    - Rank Python courses based on popularity.

19. **Find the Year with the Highest Number of Courses Posted**  

    - Identify growth trends in Udemy course listings.

20. **Category-Wise Count of Posted Subjects (Year-Wise)**  

## Technologies Used

- **Programming Language:** Python  

- **Libraries:** Pandas, NumPy, Matplotlib, Seaborn  

- **Environment:** Jupyter Notebook  

## Dataset

**Source:** https://www.kaggle.com/datasets/andrewmvd/udemy-courses