Udemy Course Analysis Project

Udemy Course Analysis Project

Overview
During my time at the Tech4Dev Women Techsters BootCamp, I had the opportunity to work on a comprehensive Udemy Course Analysis project. This experience significantly enhanced my skills in data transformation, analysis, and visualization using Power BI. I started with a dataset provided by my tutor and followed a structured process to extract meaningful insights that could guide strategic decision-making for course offerings.

Data Collection and Preparation

• Dataset Source: The dataset, provided in CSV format by my tutor, was uploaded to Power BI.

• Data Cleaning & Transformation:

  • Used Power Query Editor for data transformation.

  • Removed inconsistencies and duplicates.

  • Filtered rows to focus on relevant data.

  • Split columns by delimiter for better data structure.

  • Added a conditional column to enrich data analysis.

  • Reordered columns for clarity and changed data types as needed.

Data Analysis and Visualizations
After cleaning and preparing the data, I created the following visualizations and key insights:

Key Performance Indicators (KPIs):

1. Total Subscribers: 12 million

2. Total Courses: 3,672

3. Total Revenue: $244,000

4. Total Reviews: 575,000

5. Average Ratings: 0.61% (rating percentage)

Slicer:

• Filter by course type: Paid and Free courses.

Key Insights from Visualizations:

1. Subscribers by Subject:

   • Web development attracted the highest number of subscribers, totaling 798,000.

2. Subscribers by Course:

   • The course "Learn HTML5 Programming from Scratch" had the most subscribers, with 269,000.

3. Subscribers by Level:

   • Analyzed four levels (e.g., beginner, intermediate, advanced, all levels). The "All Levels" category had the most subscribers, reaching approximately 6 million.

4. Revenue by Level:

   • Courses in the "All Levels" category also generated the highest revenue, totaling $129,000.

5. Revenue by Subject:

   • The subject of Web Development brought in the highest revenue, contributing $93,000.

Slicer Insights:

• Applying the course type slicer revealed:

  • Free Courses: Attracted 4 million subscribers.

  • Paid Courses: Attracted 8 million subscribers.

Insights Revealed

1. Market Demand:

   • The high number of subscribers (12 million) indicates a significant demand for online courses, particularly in specific subject areas like web development. This suggests a thriving market for digital education.

2. Subject Popularity:

   • The dominance of web development in both subscriber count and revenue illustrates that this subject area is particularly appealing to learners, likely due to its relevance in today’s tech-driven economy. It may suggest a potential for more targeted courses in this area.

3. Course Performance:

   • The specific course "Learn HTML5 Programming from Scratch" having the highest subscriber count indicates that this course is well-optimized for attracting learners, possibly through effective marketing, engaging content, or favorable reviews. It may provide a benchmark for other courses.

4. User Engagement:

   • The ratio of free to paid subscribers (4 million free vs. 8 million paid) suggests a healthy conversion rate from free trials to paid courses. This insight could inform marketing strategies to increase conversion rates further by understanding what leads users to upgrade.

5. Diversity of Offerings:

   • The analysis of subscribers by course level (with All Levels having the most) suggests that learners appreciate courses that cater to a broad audience. This could indicate that creating courses that accommodate all levels of expertise might attract a wider audience.

6. Revenue Generation:

   • The highest revenue being generated from both the All Levels category and web development subjects highlights profitable areas for course development. This could guide future content creation and marketing strategies to maximize revenue.

Experience and Takeaways
This project was incredibly enriching, allowing me to:

• Sharpen my Power BI skills in data transformation and visualization.

• Improve my data storytelling capabilities.

• Enhance my ability to draw actionable insights from complex datasets.