Data Visualization Project For Angelhack Monthly coding challange

This project involves the visualization of the dataset containing the rankings of universities around the world. The aim is to explore the data and derive insights about the performance of universities based on various factors.

Dataset

The dataset used for this project is available at THE World University Rankings 2011-2023 . It contains information about the rankings of universities from 2011 to 2021, based on factors such as teaching score, research score, citations per faculty, and more.

Data story

The following visualizations have been created as part of this project:

• Data covered over years from 2011-2021

  • General observations
  • Universites in the map

• DataStory of 2011 data

  • Co-relation between all the columns.
  • Average overall scores of universities by location
  • Teaching score vs Overall score
  • Research score vs Overall score
  • Top universities

• Data story of location and universities.

  • Location vs Overall Score
  • Number of universities by location
  • Industry income vs Location

Thought process

We explored the world university rankings dataset and realized the potential for uncovering insights about the global higher education landscape.

To start, we conducted a preliminary analysis of the dataset and noticed some interesting trends, such as the high rank of the universities in Europe and the USA. We decided to focus on these trends and used a line plot to visualize the average scores_teaching and scores_research of universities located in these regions, over a period of 10 years from 2011 to 2021.

To investigate the relationships between different variables in the dataset, we will create a correlation matrix and included it in our story.

In addition to the correlation matrix, we will also include a map visualization that shows the location of universities in different regions across the world for the year 2011.

Our main goal with this data story is to provide insights into the trends and rankings of various universities across the world.

Tools and Technologies

The project is implemented using Python and various libraries such as,

• pandas
• matplotlib
• folium
• Seaborn.

The tools we have used are,

• PowerBI
• Canva

The data is preprocessed, analyzed, and visualized using these tools and technologies.

Data Story

Datastory.mp4

You can access our report from here.

Dashboard

Conclusion

Through the visualizations, we can conclude that there is a significant variation in the performance of universities across the world. Some regions consistently perform better than others, while some universities have shown a consistent improvement in their rankings over the years.

• 2011 Data story.

  • Overall score or ranking is depends on the parameters teaching score, research score

• Locations and ranking

  • Location does impact the rankings but we can't say that confirmly because we don't have all the university data in 2011.
  • Location may impact the industry income

Acknowledgments

[Thank You angelhack for this challange.]

Feel free to modify this outline and add any additional information that you feel is necessary. Good luck with your project!