This dataset provides information on 100 engineering colleges in India, including key metrics such as rankings, ratings, fees, salaries, entrance exams, and other amenities. Here are the main features included:

1. College Ranking and Rating: Indicates the overall position and quality rating of each college.
2. Fees: Tuition fees for the course, ranging from ₹20,840 to ₹26.32 Lakh.
3. Average Salary: The average salary packages offered to graduates, ranging from ₹3.83 Lakh to ₹30.18 Lakh.
4. Entrance Exam: The exam required for admission, such as JEE Advanced, JEE Main, WBJEE, etc.
5. State: Location of the college.
6. Placement Percentage: The percentage of students placed in jobs after graduation, ranging from 58% to 92%.
7. Number of Companies Visited: The count of companies that visited the campus for recruitment.
8. Quality of Food, Hostel, and Safety Details: Ratings on the quality of food, hostel facilities, and safety on campus.

This dataset is useful for students and parents looking to compare and choose the best engineering colleges based on various factors.

Indian Students Abroad

Country-wise Statistics

By Harish Kumar Garg [source]

About this dataset

This dataset is about the number of Indian students studying abroad in different countries and the detailed information about different nations where Indian students are present. The data has been complied from the Ministry Of External Affairs to answer a question from the Member of Parliament regarding how many students from India are studying in foreign countries and which country. This dataset includes two fields, Country Name and Number of Indians Studying Abroad as of Mar 2017, giving a unique opportunity to track student mobility across various nations around the world. With this valuable data about student mobility, we can gain insights into how educational opportunities for Indian students have increased over time as well as look at trends in international education throughout different regions. From comparison among countries with similar academic opportunities to tracking regional popularity among study destinations, this dataset provides important context for studying student migration patterns. We invite everyone to explore this data further and use it to draw meaningful conclusions!

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How to use the dataset

How to use this dataset?

The data has two columns – Country Name and Number of Indians studying there as of March 2017. It also includes a third column, Percentage, which gives an indication about the proportion of Indian students enrolled in each country relative to total number enrolled abroad globally.

To get started with your exploration, you can visualize the data against various parameters like geographical region or language speaking as it may provide more clarity about motives/reasons behind student’s choice. You can also group countries on basis of research opportunities available, cost consideration etc.,to understand deeper into all aspects that motivate Indians to explore further studies outside India.

Additionally you can use this dataset for benchmarking purpose with other regional / international peer groups or aggregate regional / global reports with aim towards making better decisions or policies aiming greater outreach & support while targeting foreign universities/colleges for educational promotion activities that highlights engaging elements aimed at attracting more potential students from India aspiring higher international education experience abroad!

Research Ideas

• Using this dataset, educational institutions in India can set up international exchange programs with universities in other countries to facilitate and support Indian students studying abroad.

• Higher Education Institutions can also understand the current trend of Indian students sourcing for opportunities to study abroad and use this data to build specialized short-term courses in collaboration with universities from different countries that cater to the needs of students who are interested in moving abroad permanently or even temporarily for higher studies.

• Policy makers could use this data to assess the current trends and develop policies that aim at incentivizing international exposure among young professionals by commissioning fellowships or scholarships with an aim of exposing them to different problem sets around the world thereby making their profile more attractive while they look for better job opportunities globally

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: final_data.csv | Column name | Description | |:--------------------------|:-------------------------------------------------------------------------------------------------------------------------------| | Country | Name of the country where Indian students are studying. (String) | | No of Indian Students | Number of Indian students studying in the country. (Integer) | | Percentage | Percentage of Indian students studying in the country compared to the total number of Indian students studying abroad. (Float) |

Acknowledgements

If you use this dataset in your research, please credit ...

Time series data for the statistic Government expenditure on tertiary education as % of GDP (%) and country India. Indicator Definition:Total general (local, regional and central) government expenditure on tertiary education (current, capital, and transfers), expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. Divide total government expenditure for a given level of education (ex. primary, secondary, or all levels combined) by the GDP, and multiply by 100. A higher percentage of GDP spent on education shows a higher government priority for education, but also a higher capacity of the government to raise revenues for public spending, in relation to the size of the country's economy. When interpreting this indicator however, one should keep in mind in some countries, the private sector and/or households may fund a higher proportion of total funding for education, thus making government expenditure appear lower than in other countries. Limitations: In some instances data on total public expenditure on education refers only to the Ministry of Education, excluding other ministries which may also spend a part of their budget on educational activities. For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesco.org/Education/The indicator "Government expenditure on tertiary education as % of GDP (%)" stands at 1.10 as of 12/31/2013, the lowest value since 12/31/2009. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -11.84 percent compared to the value the year prior.The 1 year change in percent is -11.84.The 3 year change in percent is -9.99.The 10 year change in percent is 51.10.The Serie's long term average value is 0.944. It's latest available value, on 12/31/2013, is 16.21 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2005, to it's latest available value, on 12/31/2013, is +75.89%.The Serie's change in percent from it's maximum value, on 12/31/2011, to it's latest available value, on 12/31/2013, is -16.69%.

This dataset provides a comprehensive and realistic representation of the college admission landscape in India. It includes information about students from 26 different Indian states, spanning diverse academic backgrounds and career aspirations. The dataset contains a wide range of variables such as demographics, preferred streams, entrance exams (JEE, NEET, CET), Class 12 board performance, extracurricular involvement, admission probability, and final decision.

Each record captures a student's journey through the admission process. Streams like Engineering, Medical, Pharmacy, Architecture, Management, Law, Arts, Commerce, Science, Agriculture, Computer Applications, and Nursing are included, providing broad applicability across multiple educational domains. Entrance examination assignment follows real Indian patterns — Engineering students may take JEE or CET, Medical students appear for NEET, while several other streams may take CET or no exam depending on the common practice.

The dataset is synthetically generated but modeled to resemble real patterns observed in Indian admissions. For example: High board percentages increase admission likelihood Higher entrance exam scores add significant weight Extracurricular achievements contribute moderately Scholarship eligibility is based on strong academic or extracurricular performance

The admission probability is calculated through a weighted formula using academic performance, entrance exam score relevance, and extracurricular involvement. A final admission decision is then generated based on this probability. The dataset also includes scholarship eligibility to help represent merit-based incentives that many Indian institutions offer.

This dataset is suitable for a wide range of users, including educators, researchers, policymakers, data analysts, career counselors, and anyone exploring the structure and patterns of Indian admission systems. It does not require any domain-specific background and can be used for general insights, visualization, institutional planning, statistical observations, or educational studies.

Since the dataset is fully synthetic, it does not include any real student data, avoiding privacy concerns while providing a highly realistic structure.

Sources

No external datasets or third-party databases were used. All observations were generated programmatically using:

Publicly known exam scoring ranges (JEE, NEET, CET) Common academic streams offered in Indian colleges General demographic patterns (age, categories, states) Typical Class 12 percentage distributions

All values are simulated and do not originate from any real-world student records.

Dataset Description – NIRF 2025: Top 100 Engineering Colleges in India 📘 Overview

This dataset contains the Top 100 Engineering Institutions in India as ranked by the National Institutional Ranking Framework (NIRF) 2025, under the Engineering category. It includes each institution’s scores across multiple performance parameters such as Teaching, Research, Graduation Outcomes, and Perception, which collectively determine their national ranking.

🏛️ Source

Data has been compiled from the official NIRF India website: 🔗 https://www.nirfindia.org

All information belongs to the Ministry of Education, Government of India, under NIRF’s public ranking framework.

📊 About the Data

Each row represents one institution, with detailed scores for different evaluation parameters used by NIRF. The dataset focuses exclusively on the Top 100 Engineering Institutions for the year 2025.

Column Description College Name Name of the engineering institution. SS Student Strength – Total number of students including Ph.D. students (weighted score). FSR Faculty–Student Ratio – Weighted measure of faculty count per student. FQE Faculty Qualification & Experience – Score based on qualifications and experience of faculty members. FRU Financial Resources and Utilization – Measures efficient use of financial resources. PU Publications – Research publication count and quality. QP Quality of Publications – Citation and impact-based quality measures. IPR Intellectual Property Rights – Patents published or granted. FPPP Footprint of Projects and Professional Practice – Consultancy and project funding from industry and other sources. GPH Graduation Outcomes – Higher Studies – Percentage of graduates pursuing higher education. GUE Graduation Outcomes – Employment – Percentage of students placed in jobs. MS Median Salary – Median salary of placed students. GPHD Graduation Outcomes – PhD Students – Number of PhD students graduating. Rank NIRF Rank (1 = best). 🎯 Use Cases

This dataset is useful for:

Analyzing trends in institutional performance.

Building regression or ranking prediction models.

Visualizing correlations between NIRF metrics (e.g., research output vs. rank).

Educational or academic benchmarking.

The following data set is information obtained about counties in the United States from 2010 through 2019 through the United States Census Bureau. Information described in the data includes the age distributions, the education levels, employment statistics, ethnicity percents, houseold information, income, and other miscellneous statistics. (Values are denoted as -1, if the data is not available)

Data Dictionary

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Key	List of...	Comment	Example Value
County	String	County name	"Abbeville County"
State	String	State name	"SC"
Age.Percent 65 and Older	Float	Estimated percentage of population whose ages are equal or greater than 65 years old are produced for the United States states and counties as well as for the Commonwealth of Puerto Rico and its municipios (county-equivalents for Puerto Rico).	22.4
Age.Percent Under 18 Years	Float	Estimated percentage of population whose ages are under 18 years old are produced for the United States states and counties as well as for the Commonwealth of Puerto Rico and its municipios (county-equivalents for Puerto Rico).	19.8
Age.Percent Under 5 Years	Float	Estimated percentage of population whose ages are under 5 years old are produced for the United States states and counties as well as for the Commonwealth of Puerto Rico and its municipios (county-equivalents for Puerto Rico).	4.7
Education.Bachelor's Degree or Higher	Float	Percentage for the people who attended college but did not receive a degree and people who received an associate's bachelor's master's or professional or doctorate degree. These data include only persons 25 years old and over. The percentages are obtained by dividing the counts of graduates by the total number of persons 25 years old and over. Tha data is collected from 2015 to 2019.	15.6
Education.High School or Higher	Float	Percentage of people whose highest degree was a high school diploma or its equivalent people who attended college but did not receive a degree and people who received an associate's bachelor's master's or professional or doctorate degree. These data include only persons 25 years old and over. The percentages are obtained by dividing the counts of graduates by the total number of persons 25 years old and over. Tha data is collected from 2015 to 2019	81.7
Employment.Nonemployer Establishments	Integer	An establishment is a single physical location at which business is conducted or where services or industrial operations are performed. It is not necessarily identical with a company or enterprise which may consist of one establishment or more. The data was collected from 2018.	1416
Ethnicities.American Indian and Alaska Native Alone	Float	Estimated percentage of population having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment. This category includes people who indicate their race as "American Indian or Alaska Native" or report entries such as Navajo Blackfeet Inupiat Yup'ik or Central American Indian groups or South American Indian groups.	0.3
Ethnicities.Asian Alone	Float	Estimated percentage of population having origins in any of the original peoples of the Far East Southeast Asia or the Indian subcontinent including for example Cambodia China India Japan Korea Malaysia Pakistan the Philippine Islands Thailand and Vietnam. This includes people who reported detailed Asian responses such as: "Asian Indian " "Chinese " "Filipino " "Korean " "Japanese " "Vietnamese " and "Other Asian" or provide other detailed Asian responses.	0.4
Ethnicities.Black Alone	Float	Estimated percentage of population having origins in any of the Black racial groups of Africa. It includes people who indicate their race as "Black or African American " or report entries such as African American Kenyan Nigerian or Haitian.	27.6
Ethnicities.Hispanic or Latino	Float