Please do upvote if you are downloading this dataset

This data is scrapped from UDISE School Search and** don't forget to upvote **🙏 if you found it usefull . It contains all the schools in India (approx 16 lakhs) with their location, current status and etc. One can also download the whole database of schools from UDISE but it does not contain the school name for that reason I scrapped it from the above-mentioned link. Contains all primary, secondary, and higher secondary schools. This data is helpful if you are building Resume Parser If this data helps you don't forget to upvote then I can share all the data that I scrapped like Universities, Colleges, and Degrees with their streams.

Keywords for searchable All Indian Schools List data Primary Schools in India data Higher Secondary Schools in India Secondary Schools listed in India Indian Schools database. Resume data parser cv data Indian Education data

A database containing information on all open schools within the education sector

This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2022-23 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to K-12 public schools that were open in October 2022 to coincide with the official 2022-23 student enrollment counts collected on Fall Census Day in 2022 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website. Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

This dataset includes all identifiable DCPS public elementary schools, middle schools, education campuses, high schools, and special education schools, as well as learning centers. This dataset does not include private or charter schools. School locations were identified from a database from the District of Columbia Public Schools, Office of Facilities Management.

Database of NCAA D1, NCAA D2, NCAA D3, and NAIA colleges.

Features: * Name: Name of college * Type: type of college (public or private) * Location: location of school (city, state) * Conference: school conference * Division: schools division * URL : schools website

Directory of Department of Education High Schools in 2019

This data is collected for the purposes of providing families and students with information about NYC DOE high schools for the purposes of admissions. Each record represents one high school.

Directory of Department of Education Schools in 2017.

This dataset contains the official listing of all public educational organizations in Connecticut. Data elements include district name, school name, organization type, organization code, address, open date, interdistrict magnet status and grades offered.

Included data are collected by the CT State Department of Education (CSDE) through the Directory Manager (DM) portal in accordance with Connecticut General Statute (C.G.S.) 10-4. This critical information is used by other data collection systems and for state and federal reporting.

For more information regarding DM, please visit http://www.csde.state.ct.us/public/directorymanager/default.asp

This point datalayer shows the locations of schools in Massachusetts. Schools appearing in this layer are those attended by students in pre-kindergarten through high school. Categories of schools include public, private, charter, collaborative programs, and approved special education. This data was originally developed by the Massachusetts Department of Environmental Protection’s (DEP) GIS Program based on database information provided by the Massachusetts Department of Education (DOE). The update published on April 17th, 2009 was based on listings MassGIS obtained from the DOE as of February 9th, 2009. The layer is stored in ArcSDE and distributed as SCHOOLS_PT. Only schools located in Massachusetts are included in this layer. The DOE also provides a listing of out-of-state schools open to Massachusetts' residents, particularly for those with special learning requirements. Please see http://profiles.doe.mass.edu/outofstate.asp for details. Updated September 2018.

Public, Private, and Postsecondary school data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain enrollment data.

This layer serves as the authoritative geographic data source for California's K-12 public school locations during the 2023-24 academic year. Schools are mapped as point locations and assigned coordinates based on the physical address of the school facility. The school records are enriched with additional demographic and performance variables from the California Department of Education's data collections. These data elements can be visualized and examined geographically to uncover patterns, solve problems and inform education policy decisions.The schools in this file represent a subset of all records contained in the CDE's public school directory database. This subset is restricted to K-12 public schools that were open in October 2023 to coincide with the official 2023-24 student enrollment counts collected on Fall Census Day in 2023 (first Wednesday in October). This layer also excludes nonpublic nonsectarian schools and district office schools.The CDE's California School Directory provides school location other basic school characteristics found in the layer's attribute table. The school enrollment, demographic and program data are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website. Schools are assigned X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy. Most schools are mapped to the school structure or centroid of the school property parcel and are individually verified using aerial imagery or assessor's parcels databases. Schools are assigned various geographic area values based on their mapped locations including state and federal legislative district identifiers and National Center for Education Statistics (NCES) locale codes.

Contact information for certified public school districts in the State of Oklahoma.

DC public schools. This dataset contains points representing public schools. It was created for the D.C. public schools and later added to the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO). This dataset includes all identifiable DCPS public elementary, middle, education campus's, senior high, and special education schools as well as learning centers. Does not include private or charter schools. School locations were identified from a database from the DC Public Schools, Office of Facilities Management. Current for the 2017-18 school year.

Layer includes school name and address, County-District-School code (CDS) from the California Department of Education (CDE), and county and district (public only) in which each school is located. Other variables include TYPE (public/private), subtype, grade span, and 10 years of employment and enrollment numbers where available (2012/2013 school year to 2021-2022). New in this version is the notation in the STATUS field for schools that are primarily or exclusively virtual. One school - Walt Tyler Elementary in El Dorado County - burned in the 2021 Caldor Fire. CDE lists the school as "active" with employees and students accounted for at the physical location, so it is listed the same here.Unlike previous versions, this database does not include schools that have closed. Closed schools (such as we had them to this version) are available by request, but users should keep in mind that some campuses have hosted multiple schools over the years this database has been produced. There could and sometimes are multiple closed schools on a given campus. All attempts have been made to include all K-12 schools in this database, but especially with private schools, which are not held to the same reporting standards as public schools, some may have been missed.*12/11/23 Update: Added Title by year designation and SACOG Environmental Justice Boundary

India Number of Schools: Secondary School data was reported at 252,176.000 Unit in 2015. This records an increase from the previous number of 244,653.000 Unit for 2014. India Number of Schools: Secondary School data is updated yearly, averaging 114,629.000 Unit from Sep 1950 (Median) to 2015, with 34 observations. The data reached an all-time high of 252,176.000 Unit in 2015 and a record low of 7,416.000 Unit in 1950. India Number of Schools: Secondary School data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDC001: Number of Schools: Secondary School.

The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes: (a) the names of schools (b) staffing levels, including full-time and part-time teaching and non-teaching staff (c) student enrolments, including the number of boys and girls (d) school financial information, including Commonwealth government, state government, and private funding (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'

Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.

The database was constructed directly from the other included files by running the following command at a command-line prompt: sqlite3 school-data-2016-1.db < school-data-2016-1.sql Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.

Briefly, the data are organized as follows. (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this. (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'. (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line). (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'. (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages. (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it. (7) Geocoding of the schools is not available in this edition. (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy. (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.

Database is provided by ASL Marketing and covers the United States of America. With ASL Marketing Reaching GenZ has never been easier. Current high school student data customized by: Class year Date of Birth Gender GPA Geo Household Income Ethnicity Hobbies College-bound Interests College Intent Email

These data are collected to inform families applying to Pre-K for All of the programs available. This is a spreadsheet version of the exact data points printed in the borough-level directories and the online PDFs. Each record represents a school participating in Pre-K for All. The information for each school is collected by the Department of Early Childhood Education (Department of Education). The "Who Got Offers" section of the spreadsheet is calculated by the Office of Student Enrollment (Department of Education) based on results from Round 1 of the Fall 2017 admissions process. This spreadsheet is simply a different representation of the same material produced in the printed and widely distributed Pre-K directories. This spreadsheet should not be used to identify current programs, as the directory was printed in December 2017 and schools are subject to change. For the most updated list of Pre-K for All schools, use the UPK Sites Directory compiled by the Department of Early Childhood Education. Disclaimer: The following columns were added to this directory to meet the Geo-spatial Standards of Local Law 108 of 2015 • Postcode / Zip code • Latitude • Longitude • Community Board • Council District • Census tract • BIN • BBL • NTA

This dataset contains student achievement data for two Portuguese high schools. The data was collected using school reports and questionnaires, and includes student grades, demographics, social, parent, and school-related features.

Two datasets are provided regarding performance in two distinct subjects: Mathematics and Portuguese language. I have cleaned the original datasets so that they are easier to read and use.

Attributes for both student_math_cleaned.csv (Math course) and student_portuguese_cleaned.csv (Portuguese language course) datasets:

1. school - student's school (binary: "GP" - Gabriel Pereira or "MS" - Mousinho da Silveira)
2. sex - student's sex (binary: "F" - female or "M" - male)
3. age - student's age (numeric: from 15 to 22)
4. address_type - student's home address type (binary: "Urban" or "Rural")
5. family_size - family size (binary: "Less or equal to 3" or "Greater than 3")
6. parent_status - parent's cohabitation status (binary: "Living together" or "Apart")
7. mother_education - mother's education (ordinal: "none", "primary education (4th grade)", "5th to 9th grade", "secondary education" or "higher education")
8. father_education - father's education (ordinal: "none", "primary education (4th grade)", "5th to 9th grade", "secondary education" or "higher education")
9. mother_job - mother's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
10. father_job - father's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
11. reason - reason to choose this school (nominal: close to "home", school "reputation", "course" preference or "other")
12. guardian - student's guardian (nominal: "mother", "father" or "other")
13. travel_time - home to school travel time (ordinal: "<15 min.", "15 to 30 min.", "30 min. to 1 hour", or 4 - ">1 hour")
14. study_time - weekly study time (ordinal: 1 - "<2 hours", "2 to 5 hours", "5 to 10 hours", or ">10 hours")
15. class_failures - number of past class failures (numeric: n if 1<=n<3, else 4)
16. school_support - extra educational support (binary: yes or no)
17. family_support - family educational support (binary: yes or no)
18. extra_paid_classes - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
19. activities - extra-curricular activities (binary: yes or no)
20. nursery - attended nursery school (binary: yes or no)
21. higher_ed - wants to take higher education (binary: yes or no)
22. internet - Internet access at home (binary: yes or no)
23. romantic_relationship - with a romantic relationship (binary: yes or no)
24. family_relationship - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
25. free_time - free time after school (numeric: from 1 - very low to 5 - very high)
26. social - going out with friends (numeric: from 1 - very low to 5 - very high)
27. weekday_alcohol - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
28. weekend_alcohol - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
29. health - current health status (numeric: from 1 - very bad to 5 - very good)
30. absences - number of school absences (numeric: from 0 to 93)

These grades are related with the course subject, Math or Portuguese:

1. grade_1 - first period grade (numeric: from 0 to 20)
2. grade_2 - second period grade (numeric: from 0 to 20)
3. final_grade - final grade (numeric: from 0 to 20, output target)

Important note: the target attribute final_grade has a strong correlation with attributes grade_2 and grade_1. This occurs because final_grade is the final year grade (issued at the 3rd period), while grade_1 and grade_2 correspond to the 1st and 2nd period grades. It is more difficult to predict final_grade without grade_2 and grade_1, but these predictions will be much more useful.

Additional note: there are 382 students that belong to both datasets, though the ID's do not match. These students can be identified by searching for identical attributes that characterize each student.

Please include this citation if you plan to use this database: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.