Udemy Course Dataset Analysis

Dataset Overview

• Content: Diverse Udemy course data, emphasizing topic, price, content duration, and course level attributes.
• Preparation: Comprehensive data cleaning and preprocessing, including handling missing values and summarizing key statistics.

Key Insights

• Topic Distribution: Bar and pie charts show the distribution of courses across different topics.
• Paid vs. Free Courses: Comparison of subscriber counts and other key metrics between paid and free courses.
• Content Analysis: Examination of courses with the longest and shortest content durations and lecture counts, exploring trends and patterns.
• Course Level: Analysis of characteristics across different course levels, providing insights into structure and depth.
• Publication Trends: Review of course release trends over the years to understand the evolution of online education.

Visualizations and Findings

• Bar & Pie Charts: Show distribution of courses by topic and level.
• Scatter Plots: Explore relationships among content duration, lecture count, and subscriber numbers.
• Line Charts: Depict temporal trends of course attributes.
• Heatmaps: Reveal correlations among various course metrics.

Tools and Techniques

• Python Libraries: Pandas, Matplotlib, and Seaborn for data manipulation and visualization.
• Technical Approach: Data aggregation, trend analysis, and correlation analysis to extract actionable insights and visualize complex patterns.