There were approximately 18.58 million college students in the U.S. in 2022, with around 13.49 million enrolled in public colleges and a further 5.09 million students enrolled in private colleges. The figures are projected to remain relatively constant over the next few years.

What is the most expensive college in the U.S.? The overall number of higher education institutions in the U.S. totals around 4,000, and California is the state with the most. One important factor that students – and their parents – must consider before choosing a college is cost. With annual expenses totaling almost 78,000 U.S. dollars, Harvey Mudd College in California was the most expensive college for the 2021-2022 academic year. There are three major costs of college: tuition, room, and board. The difference in on-campus and off-campus accommodation costs is often negligible, but they can change greatly depending on the college town.

The differences between public and private colleges Public colleges, also called state colleges, are mostly funded by state governments. Private colleges, on the other hand, are not funded by the government but by private donors and endowments. Typically, private institutions are  much more expensive. Public colleges tend to offer different tuition fees for students based on whether they live in-state or out-of-state, while private colleges have the same tuition cost for every student.

This dataset includes the attendance rate for public school students PK-12 by district during the 2020-2021 school year. Attendance rates are provided for each district for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

In the academic year of 2023/24, around 21 million students were enrolled for undergraduate degrees in the United States. This was a slight increase from the previous year, when 20.6 million students were enrolled as undergraduates.

Data from the Ministry of Colleges and Universities' College Enrolment Statistical Reporting system. Provides aggregated key enrolment data for college students, such as: * Fall term headcount enrolment by campus, credential pursued and level of study * Fall term headcount enrolment by program and Classification of Instructional Program * Fall term headcount enrolment by student status in Canada and country of citizenship by institution * Fall term headcount enrolment by student demographics (e.g., gender, age, first language) To protect privacy, numbers are suppressed in categories with less than 10 students. ## Related * College enrolments - 1996 to 2011 * University enrolment * Enrolment by grade in secondary schools * School enrolment by gender * Second language course enrolment * Course enrolment in secondary schools * Enrolment by grade in elementary schools

This dataset includes the attendance rate for public school students PK-12 by student group and by district during the 2021-2022 school year. Student groups include: Students experiencing homelessness Students with disabilities Students who qualify for free/reduced lunch English learners All high needs students Non-high needs students Students by race/ethnicity (Hispanic/Latino of any race, Black or African American, White, All other races) Attendance rates are provided for each student group by district and for the state. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch. When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

This statistic shows the total number of students enrolled in postsecondary institutions in Canada from 2000 to 2021, distinguished by attendance type. In 2021, about 1.69 million full-time students were enrolled in postsecondary institutions in Canada.

Attendance rate for semester 1 in SA Government schools by school from 2014. Important notes: • Attendance rate = (number of days attending school / number of days enrolled) x 100. • Attendance rates are only calculated for full time students who were enrolled or left during Semester 1. • Both whole day and part day absences are counted. • Attendance data is not collected from schools 1717 Watarru Anangu School (non operational), 849 Open Access College, 810 Thebarton Senior College , 583 Marden Senior College, 1012 Northern Adelaide Senior College and 195 Youth Education Centre. • To protect the privacy of students, where a school has 5 or less Full Time Equivalent students enrolled, the attendance rate is suppressed for that school. • Attendance rates in 2020 are lower than anticipated due to Covid-19 lockdowns.

In 2029, the projected number of White high school students enrolled in four-year colleges in the United States was around *********, a decrease when compared to ********* in 2019. For Hispanic high school students, however, the projected number of those enrolled in college in 2029 was approximately *******, an increase from ******* in 2019.

Daily Attendance figures are accurate as of 4:00pm, but are not final as schools continue to submit data after we generate this preliminary report.

This dataset includes the attendance rate for public school students PK-12 by town during the 2020-2021 school year.

Attendance rates are provided for each town for the overall student population and for the high needs student population. Students who are considered high needs include students who are English language learners, who receive special education, or who qualify for free and reduced lunch.

When no attendance data is displayed in a cell, data have been suppressed to safeguard student confidentiality, or to ensure that statistics based on a very small sample size are not interpreted as equally representative as those based on a sufficiently larger sample size. For more information on CSDE data suppression policies, please visit http://edsight.ct.gov/relatedreports/BDCRE%20Data%20Suppression%20Rules.pdf.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset provides the number of students enrolled in private colleges and universities in Qatar, categorized by educational institution, nationality, and gender. The data includes institutions such as Education City Universities, Hamad Bin Khalifa University, and Lusail University. It allows for the analysis of student enrollment trends across different institutions, nationalities (Qatari and Non-Qatari), and genders. This dataset is useful for understanding the distribution of students in Qatar's higher education institutions, as well as the participation of male and female students within these institutions.

This statistic shows the share of 25 to 34 year olds in different states across the United States who were enrolled in college or other higher education programs as of 2010. In the District of Columbia, 18.9 percent of 25 to 34 year olds were enrolled in college in 2010.

Context: The University Attendance Sheet Dataset is a comprehensive collection of attendance records from various university courses. This dataset is valuable for analyzing student attendance patterns, studying the impact of attendance on academic performance, and exploring factors influencing student engagement. It provides a rich resource for researchers, educators, and students interested in understanding attendance dynamics within a university setting.

Content: The dataset includes the following information:

Student ID: A unique identifier for each student. Course ID: A unique identifier for each course. Date: The date of the attendance record. Attendance Status: Indicates whether the student was present, absent, or had an excused absence on a particular date. The dataset contains records from multiple academic semesters, covering a wide range of courses across different disciplines. By examining this dataset, researchers can investigate attendance trends across different courses, identify patterns related to student performance, and explore correlations between attendance and other academic variables.

Acknowledgements: We would like to express our gratitude to the university administration, faculty members, and students who contributed to the collection and organization of this dataset. Their cooperation and support have made this dataset possible, enabling valuable insights into student attendance dynamics.

Inspiration: The inspiration behind creating this dataset stems from the recognition of the significant role attendance plays in a student's academic journey. By making this dataset available on Kaggle, we hope to facilitate research and analysis on attendance patterns, identify interventions to improve student engagement, and provide educators with valuable insights to enhance their teaching strategies. We also encourage collaboration and exploration of the dataset to uncover new findings and generate knowledge that can benefit the education community as a whole.

By leveraging the University Attendance Sheet Dataset, we aspire to contribute to the ongoing efforts to improve student success and foster an environment that promotes active participation and learning within higher education institutions.

This dataset contains data on the number of students in schools and universities categorized by level of education (Pre-primary, Primary, etc.), type of education (Government, Private), and gender (Male, Female). The data provides insight into the enrollment trends across different education levels and types of schools in the region. This dataset is essential for analyzing gender and educational distribution within both government and private institutions.

Metric scores are not reported for n-sizes under 10. Per OSSE's policy, secondary suppression is applied to all student groups when a complementary group has an n-size under 10 or is top/bottom suppressed to prevent the calculation of suppressed data.

Data Source: DC Office of the State Superintendent of Education

Why This Matters

A growing number of jobs require college degrees and people with college degrees tend to have higher incomes.

Although bachelor’s degree attainment has increased across all racial and ethnic groups, inequities persist and factors such as family income, parental education level, and neighborhood segregation continue to act as barriers to college enrollment.

Racial disparities in educational attainment perpetuate other racial inequities including in employment opportunities, wages earned, occupations held, and overall well-being.

The District Response

The Office of the State Superintendent of Education (OSSE)’s Division of Postsecondary and Career Education (PCE) helps residents transition into postsecondary programs. They offer career guidance, help students find and apply to grants, and assist residents in obtaining adult literacy proficiency and GED credentials.

The Office of College and Career Readiness (CCR) promotes college access for public school students by offering academically rigorous programs, providing funding for SAT and ACT college entrance exams, and promoting FAFSA and college application completion.

The Tuition Assistance Program Initiative for TANF (TAPIT) provides financial assistance for TANF customers to pursue postsecondary degrees of college certificate programs. This can lower the financial barrier low-income residents face in pursuing higher education.

United States US: School Enrollment: Primary: % Net data was reported at 92.942 % in 2015. This records an increase from the previous number of 92.197 % for 2014. United States US: School Enrollment: Primary: % Net data is updated yearly, averaging 93.521 % from Dec 1975 (Median) to 2015, with 26 observations. The data reached an all-time high of 98.651 % in 1991 and a record low of 81.582 % in 1975. United States US: School Enrollment: Primary: % Net data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Kaggle Dataset Description

Title: School Enrollment, Primary (% Net)
Subtitle: Exploring global trends in access to primary education.

Detailed Description:
This dataset contains data on net primary school enrollment rates, sourced from the World Bank. It measures the proportion of children enrolled in primary education who belong to the official age group for that level, expressed as a percentage of the total population of that age group.

Key Highlights: - Annual data for countries worldwide.
- Metric: Net primary school enrollment (%).
- Use cases: Analyze trends, compare regional disparities, and study relationships with socio-economic factors like GDP, literacy, and gender equality.

4. Exploratory Data Analysis (EDA)

Notebook Ideas

1. Data Cleaning:

   • Handle missing or inconsistent data points.
   • Normalize data for comparison across regions.
   • Aggregate data by regions (e.g., high-income vs. low-income countries).

2. Visualizations:

   • Line Graph: Trends in net enrollment rates over time for selected countries.
   • Heatmap: Net enrollment rates by region and year.
   • Scatterplot: Correlation between net enrollment and GDP, literacy rates, or gender equality.
   • Bar Chart: Top and bottom countries by net enrollment for a specific year.

3. Descriptive Analysis:

   • Highlight regions with near-universal enrollment.
   • Identify countries with significant improvements or declines in enrollment rates.
   • Analyze trends in gender disparities (if available).

5. Predictive Analysis (Optional)

• Use time-series forecasting (e.g., ARIMA or Prophet) to predict future enrollment rates for specific regions or countries.
• Apply clustering algorithms to group countries with similar educational trends.

6. Kaggle Notebook

Create a Kaggle notebook with:
1. Data Cleaning: Show how missing or inconsistent values are handled.
2. EDA: Include visualizations like heatmaps, scatterplots, and line graphs.
3. Insights: Highlight findings such as regions with the highest net enrollment or disparities over time.
4. Optional Predictive Modeling: Use forecasting models to predict future enrollment trends.

7. Call to Action

For GitHub:

• Share the GitHub repository link on LinkedIn, Twitter, and relevant forums.
• Invite collaboration:
  • "Fork this repository and contribute by adding insights, analyses, or visualizations!"

GitHub Link: https://github.com/AmsalAli/Primary_School_Enrollment_Trends

For Kaggle:

• Encourage upvotes:
  • "If this dataset is helpful, please upvote to make it more visible to the Kaggle community!"
• Engage users with questions:
  • "Which countries have achieved universal primary school enrollment?"
  • "How does GDP or literacy impact primary school enrollment rates?"

Kaggle Link: https://www.kaggle.com/datasets/yourusername/primary-school-enrollment

8. LinkedIn Post

Post Title:
📚 Global Trends in Primary School Enrollment 🌍

Post Body:
Excited to share my latest dataset on net primary school enrollment rates, sourced from the World Bank. This dataset measures the proportion of children enrolled in primary education who belong to the official age group for that level, offering key insights into education access globally.

📂 Explore the Dataset:
- GitHub Repository: https://github.com/AmsalAli/Primary_School_Enrollment_Trends
- Kaggle Dataset: https://www.kaggle.com/datasets/yourusername/primary-school-enrollment

Why It Matters:

Education is fundamental to global development. This dataset is ideal for:
- Trend Analysis: Analyze primary school enrollment across countries and regions.
- Regional Comparisons: Explore disparities in education access.
- Correlations: Study relationships between enrollment rates, GDP, gender equality, and literacy.

📈 Get Involved:
- Use this dataset for analysis and visualizations.
- Share your insights and findings.
- Upvote on Kaggle if you find it useful to help others discover it!

❓ What trends or correlations do you see?
- Which countries have achieved near-universal primary school enrollment?
- What factors drive improvements in education access?

Let me know your thoughts, and feel free to share this resource with your network! 🌟

DataScience #Education #SchoolEnrollment #GlobalD...

The data here is from the report entitled Trends in Enrollment, Credit Attainment, and Remediation at Connecticut Public Universities and Community Colleges: Results from P20WIN for the High School Graduating Classes of 2010 through 2016.

The report answers three questions: 1. Enrollment: What percentage of the graduating class enrolled in a Connecticut public university or community college (UCONN, the four Connecticut State Universities, and 12 Connecticut community colleges) within 16 months of graduation? 2. Credit Attainment: What percentage of those who enrolled in a Connecticut public university or community college within 16 months of graduation earned at least one year’s worth of credits (24 or more) within two years of enrollment? 3. Remediation: What percentage of those who enrolled in one of the four Connecticut State Universities or one of the 12 community colleges within 16 months of graduation took a remedial course within two years of enrollment?

Notes on the data: CT Remed: % Enrolled in Remediation is a subset of the % Enrolled in 16 Months.

Predict students' dropout and academic success

Investigating the Impact of Social and Economic Factors

By [source]

About this dataset

This dataset provides a comprehensive view of students enrolled in various undergraduate degrees offered at a higher education institution. It includes demographic data, social-economic factors and academic performance information that can be used to analyze the possible predictors of student dropout and academic success. This dataset contains multiple disjoint databases consisting of relevant information available at the time of enrollment, such as application mode, marital status, course chosen and more. Additionally, this data can be used to estimate overall student performance at the end of each semester by assessing curricular units credited/enrolled/evaluated/approved as well as their respective grades. Finally, we have unemployment rate, inflation rate and GDP from the region which can help us further understand how economic factors play into student dropout rates or academic success outcomes. This powerful analysis tool will provide valuable insight into what motivates students to stay in school or abandon their studies for a wide range of disciplines such as agronomy, design, education nursing journalism management social service or technologies

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Featured Notebooks

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How to use the dataset

This dataset can be used to understand and predict student dropouts and academic outcomes. The data includes a variety of demographic, social-economic and academic performance factors related to the students enrolled in higher education institutions. The dataset provides valuable insights into the factors that affect student success and could be used to guide interventions and policies related to student retention.

Using this dataset, researchers can investigate two key questions: - which specific predictive factors are linked with student dropout or completion? - how do different features interact with each other? For example, researchers could explore if there any demographic characteristics (e.g., gender, age at enrollment etc.) or immersion conditions (e.g., unemployment rate in region) are associated with higher student success rates, as well as understand what implications poverty has for educational outcomes. By answering these questions, research insight is generated which can provide critical information for administrators on formulating strategies that promote successful degree completion among students from diverse backgrounds in their institutions.

In order to use this dataset effectively it is important that scientists familiarize themselves with all variables provided in the dataset including categorical (qualitative) variables such as gender or application mode; numerical variables such as number of curricular units at the beginning of semesters or age at enrollment; ordinal data measurement type variables such as marital status; studied trends over time such as inflation rate or GDP; frequency measurements variables like percentage of scholarship holders; etc.. Additionally scientists should make sure they aware off all potential bias included in the data prior running analysis–for example understanding if one population is underrepresented compared another -as this phenomenon could lead unexpected results if not taken into consideration while conducting research undertaken using this data set.. Finally it would be important for practitioners realize that this current Kaggle Dataset contains only one semester-worth information on each admission intake whereas additional studies conducted for a longer time period might be able provide more accurate results related selected topic area due further deterioration retention achievement coefficients obtained from those gradually accurate experiments unfolding different year-long admissions seasons

Research Ideas

• Prediction of Student Retention: This dataset can be used to develop predictive models that can identify student risk factors for dropout and take early interventions to improve student retention rate.
• Improved Academic Performance: By using this data, higher education institutions could better understand their students' academic progress and identify areas of improvement from both an individual and institutional perspective. This will enable them to develop targeted courses, activities, or initiatives that enhance academic performance more effectively and efficiently.
• Accessibility Assistance: Using the demographic information included in the dataset, institutions could develop s...