Udemy Courses Exploratory Data Analysis (EDA)

This project provides an Exploratory Data Analysis (EDA) of Udemy courses, utilizing various Python libraries such as NumPy, Pandas, Seaborn, Matplotlib, and Plotly Express. The analysis is conducted on the dataset containing Udemy course data, which includes insights into course categories, prices, reviews, and more.

Project Overview

The goal of this project is to analyze the Udemy courses dataset to uncover patterns, trends, and relationships in the data. Through this analysis, we aim to answer questions such as:

• Which categories have the most courses?
• What is the price distribution of Udemy courses?
• How are the courses rated based on student feedback?

Links to the Dataset and EDA Notebook

• EDA Notebook on Kaggle
• Udemy Courses Dataset on Kaggle

Libraries Used

1. NumPy

2. Pandas

3. Seaborn

4. Matplotlib (Pyplot)

5. Plotly Express

Key Insights from the Analysis

The following are some key insights derived from the Udemy courses dataset:

1. Course Category Distribution: The dataset reveals that certain categories, such as "Business" and "Development", have a significantly larger number of courses compared to others.
2. Price Range: The majority of Udemy courses fall under specific price ranges, with some interesting trends regarding free vs paid courses.
3. Student Enrollment: Popular categories and courses tend to attract more student enrollments and reviews.
4. Course Ratings: The distribution of course ratings helps to understand student satisfaction across different categories.

Prerequisites

Ensure you have the following Python libraries installed:

• numpy
• pandas
• seaborn
• matplotlib
• plotly