This project presents a comprehensive analysis of Udemy's sales and course performance using Python and data visualization techniques. The goal is to derive actionable business insights from course metadata including revenues, enrollments, pricing, and category-level trends.
The dataset includes course-related metrics such as:
- Course categories (e.g., Web Development, Business Finance)
- Pricing details
- Enrollments
- Revenue over time
- Course levels (Beginner, Intermediate, Expert)
- Top Performing Categories
- Customer Insights - Course Level Distribution
- Price vs Enrolment Trends
- Subject-wise Revenue
- Course Creation Trends
- Revenue Forecasting
- Growth and Decline Reasons
- Strategic Recommendations
Udemy experienced a significant revenue increase from 2014 to 2016, followed by a sudden drop in 2017.
This project aims to understand the reasons behind this trend and suggest solutions to stabilize and grow revenue.
- 📈 High Demand: Web Development & Business Finance
- 🎯 Revenue Peak: 2016 (followed by a drop in 2017)
- 🪙 Low Prices attract more students
- 📚 Beginner Courses dominate, but there's scope to expand Intermediate & Expert content
- 🧩 Bundle strategy and seasonal marketing can help boost engagement
Mentioned in the PPT in 8th topic
- Python
- Pandas – data manipulation
- Matplotlib & Seaborn – visualizations
- Jupyter Notebook – for analysis
udemy_analysis_cleaned.ipynb– Cleaned Jupyter notebook with full analysis
udemy_cleaned.csv– Dataset used (cleaned version)
udemy_presentation.pdf– Visual summary of findings
README.md– Project documentation
- Analyzing trends in online education
- Understanding market performance by category and level
- Applying data visualization for strategic business decisions
For queries or collaboration, feel free to connect:
Dhrumil Patel
📧 Email: pdhrumil079@gmail.com
🌐 Linkedin: www.linkedin.com/in/dhrumil-pa