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https://github.com/naninsv/edtech-course-analytics-power-bi-dashboard

An interactive Power BI dashboard for analyzing online course trends, engagement, pricing strategies, and instructor performance. This project helps an EdTech startup optimize course offerings using Power BI, Power Query, and Excel for data-driven decisions
https://github.com/naninsv/edtech-course-analytics-power-bi-dashboard

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An interactive Power BI dashboard for analyzing online course trends, engagement, pricing strategies, and instructor performance. This project helps an EdTech startup optimize course offerings using Power BI, Power Query, and Excel for data-driven decisions

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README 

        
# EdTech Startup Power BI Dashboard - Course Insights

## Project Overview



![Project Presentation](Photo/Project%20Presentation.jpg)



## 📌 Project Overview

As a data analyst for an EdTech startup, this project aims to clean, analyze, and visualize data collected from various EdTech websites to uncover valuable insights regarding online courses. The focus is on category-wise analysis to identify areas for improvement and opportunities for growth in online education services.



The dashboard will help the company understand key trends in online education, optimize its offerings, and make data-driven decisions to enhance its course catalog.

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## 📊 Key Insights & Analysis



### 1️⃣ Course Type Distribution by Category & Sub-Category

- Analyzes the distribution of different course types across categories.

- Helps determine which course types to launch for maximum impact.

- Counts the number of courses by category and sub-category to uncover trends.



### 2️⃣ Average Number of Views per Category, Sub-Category & Language

- Identifies viewer engagement patterns based on course category, sub-category, and language.

- Guides strategic content development to maximize engagement.



### 3️⃣ Most Commonly Taught Skills by Category

- Identifies the most relevant skills currently in demand.

- Ensures course offerings align with job market trends.



### 4️⃣ Language Distribution of Courses

- Analyzes the distribution of different languages used for courses.

- Helps the company assess the variety of languages available.



### 5️⃣ Language Preferences by Category (Top 5 Categories Only)

- Determines the preferred language for each of the top five most popular categories.

- Enables better course accessibility and alignment with audience demand.



### 6️⃣ Impact of Subtitles on Course Views

- Examines whether subtitles influence the number of views.

- Helps optimize course accessibility and engagement.



### 7️⃣ Top 3 Instructors per Category & Sub-Category (Based on Ratings)

- Identifies top instructors based on student ratings.

- Highlights educators who consistently deliver high-quality content.

- Provides static visuals showcasing the best instructors.



### 8️⃣ Course Duration vs. Views (Engagement Analysis)

- Analyzes how course length affects engagement.

- Determines optimal course duration for viewer retention.

- Includes constraints:

  - Monthly schedule: Maximum 60 hours of content per month.

  - Flexible schedule: Maximum 200 hours of content.



### 9️⃣ Skill Variety Impact on Viewership (Category & Sub-Category Level)

- Investigates whether a diverse range of skills influences course views.

- Helps assess the effectiveness of broad vs. niche content strategies.



### 🔟 Course Price vs. Engagement Analysis

- Evaluates how course pricing affects student engagement and viewership.

- Helps determine optimal pricing strategies for maximizing revenue and enrollments.



### 1️⃣1️⃣ Seasonal Trends in Course Enrollment

- Identifies patterns in course enrollments based on time of year.

- Helps predict demand for specific courses during peak periods.



### 1️⃣2️⃣ Competitor Benchmarking & Market Trends

- Compares the company’s courses with competitors.

- Identifies emerging trends in online education.



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## 🛠️ Tools & Technologies Used

- **Power BI**: Data visualization and dashboard creation.

- **Power Query**: Data cleaning and transformation.

- **Excel**: Data loading and preprocessing.



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## 📂 Dataset Used

The dataset for this analysis is sourced from Kaggle: [Online Courses Dataset](https://www.kaggle.com/datasets/)



### Dataset Features:

- Course Title

- Category & Sub-Category

- Number of Views

- Course Duration

- Instructor Ratings

- Language

- Skills Covered

- Subtitle Availability

- Course Pricing

- Enrollment Date



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## 💡 Business Recommendations



📌 **Expand Course Offerings in High-Demand Categories**

- Focus on top-performing categories with high engagement and views.

- Increase courses in languages with strong viewer preference.



📌 **Collaborate with Top-Rated Instructors**

- Partner with educators who have consistently high ratings and engagement.



📌 **Optimize Course Duration for Maximum Engagement**

- Align course lengths with viewer preferences to enhance completion rates.



📌 **Offer Multi-Language Support & Subtitles**

- Improve accessibility for a global audience.

- Increase viewership by ensuring courses are available in preferred languages.



📌 **Align Skills with Job Market Demand**

- Regularly update course content based on trending industry skills.



📌 **Strategically Price Courses for Maximum Enrollment**

- Adjust course prices based on demand and engagement data.



📌 **Prepare for Seasonal Enrollment Spikes**

- Launch promotions and new courses during peak enrollment periods.



📌 **Stay Competitive with Market Research**

- Regularly benchmark against competitors to identify gaps and trends.



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## 🚀 How to Use This Project

1. **Download or Clone** the repository.

2. Open the **Power BI Dashboard (.pbix file)**.

3. Load the dataset into Power BI.

4. Use filters, slicers, and interactive visuals to explore insights.

5. Draw business conclusions based on key findings.



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## 🎯 Conclusion

This Power BI dashboard serves as a comprehensive analytical tool to help the EdTech startup strategically expand its online course offerings. By leveraging insights from course distribution, language preferences, instructor performance, and engagement patterns, the company can make data-driven decisions to enhance its content strategy and learner experience.



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