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.

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

About Dataset

The dataset you provided, titled "Report Card Enrollment 2023-24 School Year," appears to be a comprehensive collection of information regarding student enrollment and demographics within educational institutions for the specified academic year. Here are some observations about the dataset:

1. Granularity: The dataset seems to be quite granular, providing detailed information not only about overall student enrollment but also about various demographic categories such as gender, race/ethnicity, English language learners, students with disabilities, and socioeconomic status.

2. Demographic Diversity: It captures the diversity of the student population by including counts for various racial/ethnic groups, as well as categories such as gender X, indicating a recognition of diverse gender identities.

3. Socioeconomic Indicators: The dataset includes indicators of socioeconomic status such as students in foster care, homeless students, and those from low-income families, which can provide insights into equity and access issues within the educational system.

4. Special Education and Gifted Programs: It tracks the enrollment of students with disabilities and those identified as highly capable, which are important metrics for evaluating the inclusivity and effectiveness of special education and gifted programs.

5. Geographical Context: The dataset includes information about the county, educational service district, and school district, providing a geographical context for the enrollment data.

6. Temporal Aspect: The "DataAsOf" column indicates that the data has a temporal aspect, suggesting that it may be periodically updated to reflect changes in enrollment and demographics throughout the academic year.

**columns : ** SchoolYear: Indicates the academic year for which the data is reported, in this case, it's 2023-24.

OrganizationLevel: Specifies the level of educational organization, which could be school, district, or any other relevant level within the educational system.

County: Refers to the county where the educational organization is located.

ESDName: Stands for Educational Service District Name, which represents the intermediate level of educational administration in some states.

ESDOrganizationID: ID assigned to the Educational Service District.

DistrictCode: Code assigned to the district within the educational system.

DistrictName: Name of the school district.

DistrictOrganizationId: ID assigned to the district organization.

SchoolCode: Code assigned to the school within the district.

SchoolName: Name of the school.

SchoolOrganizationID: ID assigned to the school organization.

CurrentSchoolType: Indicates the current type of the school, such as elementary, middle, or high school.

GradeLevel: Specifies the grade level(s) served by the school.

All Students: Total number of enrolled students in the school.

Female: Number of female students enrolled.

Gender X: Number of students who identify as gender X, indicating a non-binary or non-conforming gender identity.

Male: Number of male students enrolled.

American Indian/ Alaskan Native: Number of students identifying as American Indian or Alaskan Native.

Asian: Number of students identifying as Asian.

Black/ African American: Number of students identifying as Black or African American.

Hispanic/ Latino of any race(s): Number of students identifying as Hispanic or Latino of any race.

Native Hawaiian/ Other Pacific Islander: Number of students identifying as Native Hawaiian or other Pacific Islander.

Two or More Races: Number of students identifying as belonging to two or more races.

White: Number of students identifying as White.

English Language Learners: Number of students who are learning English as a second language.

Foster Care: Number of students in foster care.

Highly Capable: Number of students identified as highly capable or gifted.

Homeless: Number of students experiencing homelessness.

Low-Income: Number of students from low-income families.

Migrant: Number of students from migrant families.

Military Parent: Number of students with parents serving in the military.

Mobile: Number of students who frequently change residences.

Section 504: Number of students covered under Section 504 of the Rehabilitation Act, which provides accommodations for students with disabilities.

Students with Disabilities: Number of students with disabilities.

Non-English Language Learners: Number of students who are not learning English as a second language.

Non-Foster Care: Number of students who are not in foster care.

Non-Highly Capable: Number of students who are not identified as highly capable or gifted.

Non-Homeless: Number of students wh...

Objectives of the analysis

This case study looks at college attending plan. In this case, the government's education department would like to know how many students want to go to college, but high school students may not be willing to share such private information with the education department. So the government needs to predict whether high school students are willing to go to college, and then give instructions to public universities and labor departments. Whether high school students go to college is mainly influenced by themselves and their parents. One way to perform this analysis is to use student data available to the education department. The data in this case study came from a city government that wanted us to be able to use this desensitized student data to make predictions about whether high school students were willing to go to college.

The purpose of this study is to use the basic information of high school students to predict whether they plan to go to college, in particular, which factors can better distinguish high school students who are willing to go to college from those who are not. That could help colleges plan their admissions ahead of time and help the government predict the number of high-school students who will need jobs during graduation season. In data mining terms, we are concerned with classification problems. Therefore, classification algorithms, especially the decision tree algorithm you learned about, can be used for analysis.

Description of the data

Here we will give a brief introduction to the dataset. The data sent to us by the government is in csv format, containing numbers and strings. Students' names are desensitized and replaced by StudentID to prevent information leakage. According to the government official who spoke with us, the data was taken from the actual enrollment data of the city last year, and the last column Plan indicates whether the high school student finally made plans to go to college. The explanatory variables we were able to use included the gender of the student, the IQ of the student, the income of the parents and whether the parents encouraged the child to go to college. Government officials tell us that in their long-term observation, these four factors are the most important factors in whether high school students plan to go to college. The data given to us by government officials is extensive, but analyzing the entire data set is expensive and time-consuming, so we have taken a partial sample for you to analyze. You can get up to 8,000 samples, which is enough to help you train a stable decision tree. The table below details the explanatory variables you can use:

This dataset contains college enrollment information for the state of Michigan. College enrollment was defined as the number of public high school students who graduated in 2017, who enrolled in a college or university. This dataset includes enrollment in two-year and four-year institutions of higher education.

CollegeScorecard US College Graduation and Opportunity Data

Exploring Student Success and Outcomes

By Noah Rippner [source]

About this dataset

This dataset provides an in-depth look at the data elements for the US College CollegeScorecard Graduation and Opportunity Project Use Case. It contains information on the variables used to create a comprehensive report, including Year, dev-category, developer-friendly name, VARIABLE NAME, API data type, label, VALUE, LABEL , SCORECARD? Y/N , SOURCE and NOTES. The data is provided by the U.S Department of Education and allows parents, students and policymakers to take meaningful action to improve outcomes. This dataset contains more than enough information to allow people like Maria - a 25 year old recent US Army veteran who wants a degree in Management Systems and Information Technology -to distinguish between her school options; access services; find affordable housing near high-quality schools which are located in safe neighborhoods that have access to transport links as well as employment opportunities nearby. This highly useful dataset provides detailed analysis of all this criteria so that users can make an informed decision about which school is best for them!

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How to use the dataset

This dataset contains data related to college students, including their college graduation rates, access to opportunity indicators such as geographic mobility and career readiness, and other important indicators of the overall learning experience in the United States. This guide will show you how to use this dataset to make meaningful conclusions about high education in America.

First, you will need to be familiar with the different fields included in this CollegeScorecard’s US College Graduation and Opportunity Data set. Each record is comprised of several data elements which are defined by concise labels on the left side of each observation row. These include labels such as Name of Data Element, Year, dev-category (i.e., developmental category), Variable Name, API data type (i.e., type information for programmatic interface), Label (i.e., descriptive content labeling for visual reporting), Value , Label (i.e., descriptive value labeling for visual reporting). SCORECARD? Y/N indicates whether or not a field pertains to U.S Department of Education’s College Scorecard program and SOURCE indicates where the source of the variable can be found among other minor details about that variable are found within Notes column attributed beneath each row entry for further analysis or comparison between elements captured across observations

Now that you understand the components associated within each element or label related within Observation Rows identified beside each header label let’s go over some key steps you can take when working with this particular dataset:

• Utilize year specific filters on specified fields if needed — e.g.; Year = 2020 & API Data Type = Character

• Look up any ‘NCalPlaceHolder” values if applicable — these are placeholders often stating values have been absolved fromScorecards display versioning due conflicting formatting requirements across standard conditions being met or may state these details have still yet been updated recently so upon assessment wait patiently until returns minor changes via API interface incorporate latest returned results statements inventory configuration options relevant against budgetary cycle limits established positions

• Pivot data points into more custom tabular structured outputs tapering down complex unstructured RAW sources into more digestible Medium Level datasets consumed often via PowerBI / Tableau compatible Snapshots expanding upon Delimited text exports baseline formats provided formerly

• Explore correlations between education metrics our third parties documents generated frequently such values indicative educational adherence effects ROI growth potential looking beyond Campus Panoramic recognition metrics often supported outside Social Medial Primary

Research Ideas

• Creating an interactive dashboard to compare school performance in terms of safety, entrepreneurship and other criteria.
• Using the data to create a heat map visualization that shows which cities are most conducive to a successful educational experience for students like Maria.
• Gathering information about average course costs at different universities and mapping them relative to US unemployment rates indicates which states might offer the best value for money when it comes to higher education expenses

Ack...

Full Description The variable examined is graduation status after four years of high school. Early and summer graduates are considered graduates after four years. The "other" rate includes students who dropped out of high school, enrolled in a GED program, transferred to post-secondary education, or have unknown status. Special education students in school after four years but subsequently graduated are not included in the "still enrolled" rate due to Individuals with Disabilities Education Act (IDEA) restrictions. The subgroups reported are gender, race/ethnicity, English language learners, special education students, and students eligible for free or reduced-price meals (FRPM). The data replace the rate of students enrolled in 12th grade in September who graduated the following June. Connecticut State Department of Education (SDE) collects data longitudinally by four-year cohorts. SDE reports and CTdata.org carries graduation rates of four-year cohorts annually.

This dataset contains the total annual FTE and unduplicated headcount enrollment for undergraduate and graduate students in credit-bearing courses at public community colleges and state universities in Massachusetts since 2014. Data are disaggregated by fiscal year, segment, institution, and student attributes such as enrollment level, residency, age, race/ethnicity, and gender.

This dataset is 1 of 2 datasets that is also published in the interactive Annual Enrollment dashboard on the Department of Higher Education Data Center:

1) Public Postsecondary Annual Enrollment: Detail 2) Public Postsecondary Annual Enrollment: Summary

Related datasets: 1) Public Postsecondary Fall Enrollment 2) Public Postsecondary Fall Enrollment by Race and Gender

Notes: - Data appear as reported to the Massachusetts Department of Higher Education. - Annual enrollment refers to a 12 month enrollment period over one fiscal year (July 1 through June 30). - Figures published by DHE may differ slightly from figures published by other institutions and organizations due to differences in timing of publication, data definitions, and calculation logic. - Data for the University of Massachusetts are not included due to unique reporting requirements. See Fall Enrollment for HEIRS data on UMass enrollment. -The most common measure of enrollment is headcount of enrolled students. Annual headcount enrollment is unduplicated, meaning any individual student is only counted once per institution and fiscal year, even if they are enrolled in multiple terms. - Enrollment can also be measured as full-time equivalent (FTE) students, a calculation based on the sum of credits carried by all enrolled students. In a fiscal year, 30 undergraduate credits = 1 undergraduate FTE, and 24 graduate credits = 1 graduate FTE at a state university. - For precise calculations and aggregations, use the FTE_RAW column. The FTE column is for display only.

This dataset contains college enrollment information, by census block group, for the state of Michigan. College enrollment was defined as the number of public high school students who graduated in 2017, who enrolled in a college or university within 12 months of their high school graduation. This dataset includes enrollment in two-year and four-year institutions of higher education.Click here for metadata (descriptions of the fields).

About this dataset

Context

The dataset tabulates the College Corner population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of College Corner. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 198 (60.55% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the College Corner population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in College Corner is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the College Corner is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for College Corner Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the College Place population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of College Place. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 5,690 (57.90% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the College Place population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in College Place is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the College Place is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for College Place Population by Age. You can refer the same here

New York City school level College Board SAT results for the graduating seniors of 2010. Records contain 2010 College-bound seniors mean SAT scores. Records with 5 or fewer students are suppressed (marked ‘s’). College-bound seniors are those students that complete the SAT Questionnaire when they register for the SAT and identify that they will graduate from high school in a specific year. For example, the 2010 college-bound seniors are those students that self-reported they would graduate in 2010. Students are not required to complete the SAT Questionnaire in order to register for the SAT. Students who do not indicate which year they will graduate from high school will not be included in any college-bound senior report. Students are linked to schools by identifying which school they attend when registering for a College Board exam. A student is only included in a school’s report if he/she self-reports being enrolled at that school. Data collected and processed by the College Board.

About this dataset

Context

The dataset tabulates the North College Hill population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of North College Hill. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 - 64 years with a poulation of 5,757 (59.94% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the North College Hill population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in North College Hill is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the North College Hill is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for North College Hill Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the College Springs population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of College Springs. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 141 (64.98% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the College Springs population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in College Springs is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the College Springs is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for College Springs Population by Age. You can refer the same here

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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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...

About this dataset

Context

The dataset tabulates the College Park population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of College Park. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 - 64 years with a poulation of 9,174 (65.38% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the College Park population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in College Park is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the College Park is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for College Park Population by Age. You can refer the same here

The District Analysis and Review Tools (DARTs) offer snapshots of district and school performance, allowing users to easily track select data elements over time, and make sound, meaningful comparisons to the state or to "comparable" organizations.

This dataset is a long file that contains multiple rows for each school and district, with rows for different years, different student groups, and a wide range of indicators.

This dataset contains the same data that is also published on our DART Detail: Success After High School Online Dashboard

Below is a list of indicators that are included within the dataset. Note: "Student progression from high school through second year of postsecondary education" and "Student progression from high school through postsecondary degree completion" are available for download in this companion dataset. These two indicators are separate from the main DART: Success After High School download since the data are in a different format.

List of Indicators

Context

• Stability rate (enrolled all year)
• Student Enrollment

• 4-year cohort graduation rate
• 5-year cohort graduation rate
• 9th to 10th grade promotion rate (first-time 9th graders only)
• Annual dropout rate
• Chronically absent rate (% of students absent 10% or more each year)
• Student attendance rate
• Students absent 10 or more days each year
• Students suspended out-of-school at least once

• Average student growth percentiles (SGP) in ELA
• Average student growth percentiles (SGP) in mathematics
• Grade 10 students meeting or exceeding expectations in ELA
• Grade 10 students meeting or exceeding expectations in mathematics
• Jr / Sr AP test takers scoring 3 or above
• Jr / Sr enrolled in one or more AP / IB courses
• Jr / Sr who took AP courses and participated in one or more AP tests
• SAT average score - Mathematics
• SAT average score - reading
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - All subjects
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Art
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - English
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Foreign Language
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - History/Social Science
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Mathematics
• Test takers scoring 3 or above on the Advanced Placement (AP) by category - Science

• College students persistently enrolled in postsecondary education for the first two years
• Grade 12 students who completed FAFSA
• High school graduates enrolled in 2-year postsecondary education
• High school graduates enrolled in 4-year postsecondary education
• Students enrolled in postsecondary education in the immediate fall after high school graduation
• Student progression from high school through postsecondary degree completion
• Student progression from high school through second year of postsecondary education

• 12th graders passing a full year of mathematics coursework
• 12th graders passing a full year of science and technology/engineering coursework
• 9th graders completing and passing all courses
• High school graduates who completed MassCore

This dataset contains college enrollment information, by ZIP, for the state of Michigan. College enrollment was defined as the number of public high school students who graduated in 2017, who enrolled in a college or university within 12 months of their high school graduation. This dataset includes enrollment in two-year and four-year institutions of higher education.

This dataset contains college enrollment information, by County Subdivison, for the state of Michigan. College enrollment was defined as the number of public high school students who graduated in 2017, who enrolled in a college or university within 12 months of their high school graduation. This dataset includes enrollment in two-year and four-year institutions of higher education.

Maryland has 200+ higher education facilities located throughout the entire State. Maryland boasts a highly educated workforce with 300,000+ graduates from higher education institutions every year. Higher education opportunities range from two year, public and private institutions, four year, public and private institutions and regional education centers. Collectively, Maryland's higher education facilities offer every kind of educational experience, whether for the traditional college students or for students who have already begun a career and are working to learn new skills. Maryland's economic diversity and educational vitality is what makes it one of the best states in the nation in which to live, learn, work and raise a family.