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electricsheepafrica/africa-egypt-capmas-education-and-training-in-public-and-private-training-institutio-6e92a0e2

Education and Training in Public and Private Training Institutions | Africa (CAPMAS Egypt Open Data) 16,874 rows - 1 Africa country/area - 2012-2023 - 123 indicators - Engineered by Electric Sheep Africa TL;DR This dataset contains 16,874 rows from CAPMAS Egypt Open Data, covering Education and Training in Public and Private Training Institutions. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-egypt-capmas-education-and-training-in-public-and-private-training-institutio-6e92a0e2.

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Education and Training in Public and Private Training Institutions | Africa (CAPMAS Egypt Open Data)

16,874 rows - 1 Africa country/area - 2012-2023 - 123 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 16,874 rows from CAPMAS Egypt Open Data, covering Education and Training in Public and Private Training Institutions. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Education datasets help analysts study access, participation, learning systems, infrastructure, and outcomes across places and periods.

This dataset covers Education and Training in Public and Private Training Institutions from CAPMAS Egypt Open Data. Use the source and schema sections below to confirm definitions, units, and collection methodology before sensitive analytical use.

How To Read This Dataset

  • —One row means: one indicator observation for one geography, time period, and optional source dimensions.
  • —Primary geography column: source metadata (EGY).
  • —Best time column: year.
  • —Time coverage basis: year.
  • —Recommended join keys: year, indicator_id, plus source-specific dimension columns.

Coverage

DimensionValue
Rows16,874
Countries/areas1
First period2012
Last period2023
Indicators123
Columns12
Source formatPARQUET

Geographic Coverage

Top areas shown below, sorted by row count when available:

AreaRowsFirst yearLast yearName
EGY16,87420122023Egypt

Indicators, Variables, Or Resource Contents

  • —326 - The numerical distribution of the total number of graduates from training institutions in formal education in the public sector according to specialization.(Number)
  • —503 - The numerical distribution of the total number of students in educational training institutions in the government sector by gender.(Number)
  • —390 - Numerical distribution of the total number of graduates from training institutions in formal education in the public sector by governorate.(Number)
  • —419 - Distribution of the total number of trainers in educational training institutions in the public sector by gender(Number)
  • —447 - The numerical distribution of the total number of teachers in educational training institutions in the government sector by gender.(Number)
  • —341 - The numerical distribution of the total number of students in training institutions for formal education in the government sector according to specialization.(Number)
  • —441 - The numerical distribution of the total number of students in educational training institutions in the government sector by governorate.(Number)
  • —292 - The numerical distribution of the total number of training institutions for formal education in the government sector according to specialization.(Number)
  • —291 - Numerical distribution of the total number of educational training institutions in the government sector by governorate(Number)
  • —489 - Numerical distribution of graduates in schools within training institutions in the public sector according to the type of education (formal / informal)(Number)
  • —490 - The numerical distribution of graduates from institutes in training institutions in the public sector by governorate according to the type of education (formal / non-formal)(Number)
  • —393 - Numerical distribution of graduates from non-formal education training institutions in the public sector by governorate(Number)
  • —510 - The numerical distribution of graduates from non-formal education training institutions in the public sector by gender.(Number)
  • —407 - The numerical distribution of graduates from training centers in educational institutions (curricular / non-curricular) in the public sector by governorate.(Number)
  • —488 - Numerical distribution of graduates from training centers in educational institutions (formal / informal) in the public sector according to the type of education.(Number)
  • —405 - The numerical distribution of graduates in educational training institutions (formal / non-formal) in the government sector by governorate.(Number)
  • —398 - The numerical distribution of graduates in training institutions for formal education affiliated with the Ministry of Social Solidarity in the private sector by governorate.(Number)
  • —494 - Distribution of the total number of graduates from training institutions in formal education in the public sector by gender.(Number)
  • —459 - Numerical distribution of graduates in training institutions for formal education affiliated with the Ministry of Social Solidarity in the private sector by gender.(Number)
  • —363 - The numerical distribution of graduates from institutes in training institutions for education (curricular / non-curricular) in the public sector according to the governorate.(Number)
  • —... 103 more indicators

Schema

ColumnTypeDescriptionExample
filter_idstringSource column from the original resource.60
filter_arstringSource column from the original resource.الإجمالي
filter_enstringSource column from the original resource.Total
valuedoubleNumeric observation value.20337.0
yearint64Observation year.2015
quarternullSource column from the original resource.``
monthnullSource column from the original resource.``
indicator_idstringStable source or Electric Sheep Africa indicator identifier.326
indicator_namestringHuman-readable indicator name.The numerical distribution of the total number of graduates from trai...
indicator_name_arstringSource column from the original resource.التوزيع العددى لإجمالي عدد الخريجين بالمؤسسات التدريبية للتعليم المنه...
unitstringMeasurement unit, when supplied by the source.Number
periodicitystringSource column from the original resource.Annually

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-egypt-capmas-education-and-training-in-public-and-private-training-institutio-6e92a0e2")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

python
print(df.info())
print(df.head())

Filter By Geography

python
# This dataset is scoped to Egypt in source metadata.
sample = df.copy()

Time-Series Pattern

python
if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

python
if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

  • —Canonical time field: year.
  • —Missing values are preserved rather than silently imputed.
  • —Column names are standardized for machine use; source meanings are preserved where known.
  • —Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

Transformations Applied

  • —Converted the source table to Parquet for efficient analytics and ML workflows.
  • —Added or preserved source provenance columns where available.
  • —Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
  • —Preserved source-reported values without analytical imputation.

Suggested Analyses

  • —Compare education indicators by geography
  • —Track participation or completion trends
  • —Join with population and poverty indicators
  • —Build time-series views and period-over-period comparisons
  • —Pivot to indicator x period matrices
  • —Check missingness before modeling
  • —Use source or repo metadata for country scope when a geography column is not present

Citation

bibtex
@misc{electric_sheep_africa_africa_egypt_capmas_education_and_training_in_public_and_private_training_instit_2023,
  title        = {Education and Training in Public and Private Training Institutions | Africa (CAPMAS Egypt Open Data)},
  author       = {Central Agency for Public Mobilisation and Statistics (CAPMAS), Egypt},
  year         = {2023},
  url          = {https://www.capmas.gov.eg},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-egypt-capmas-education-and-training-in-public-and-private-training-institutio-6e92a0e2}}
}

License

Released under Source-specific or other license.

Original data is published by Central Agency for Public Mobilisation and Statistics (CAPMAS), Egypt. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.


Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://www.capmas.gov.eg