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electricsheepafrica/africa-mauritius-ministry-of-education-and-human-resources-tertiary-and-sci-f5068624

Ministry of Education and Human Resources Tertiary and Sci | Africa (MDPA) 1,332 rows - 1 Africa country/area - 2016-2019 - 3 indicators - Engineered by Electric Sheep Africa TL;DR This dataset contains 1,332 rows from MDPA, covering Ministry of Education and Human Resources Tertiary and Sci. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This Dataset… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-ministry-of-education-and-human-resources-tertiary-and-sci-f5068624.

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Dataset Card

Ministry of Education and Human Resources Tertiary and Sci | Africa (MDPA)

1,332 rows - 1 Africa country/area - 2016-2019 - 3 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 1,332 rows from MDPA, covering Ministry of Education and Human Resources Tertiary and Sci. 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.

Source-provided context: Ministry of Education and Human Resources, Tertiary and Scientific Research budet data 2017-2018

How To Read This Dataset

  • —One row means: one indicator observation for one geography, time period, and optional source dimensions.
  • —Primary geography column: country_iso3.
  • —Best time column: year.
  • —Time coverage basis: year.
  • —Recommended join keys: country_iso3, year, indicator_id.

Coverage

DimensionValue
Rows1,332
Countries/areas1
First period2016
Last period2019
Indicators3
Columns24
Source formatCSV

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU1,33220162019Mauritius

Indicators, Variables, Or Resource Contents

  • —ministry-of-education-and-human-resources-tertiary-and-scientific-resear-5d9f346f - Ministry of Education and Human Resources, Tertiary and Scientific Research budet data 2017-2018 - itemno(sourceunitsunspecified)
  • —ministry-of-education-and-human-resources-tertiary-and-scientific-resear-75699606 - Ministry of Education and Human Resources, Tertiary and Scientific Research budet data 2017-2018 - endfinancialyear(sourceunitsunspecified)
  • —ministry-of-education-and-human-resources-tertiary-and-scientific-resear-0df2384f - Ministry of Education and Human Resources, Tertiary and Scientific Research budet data 2017-2018 - amount(sourceunitsunspecified)

Schema

ColumnTypeDescriptionExample
indicator_idstringStable source or Electric Sheep Africa indicator identifier.ministry-of-education-and-human-resources-tertiary-and-scientific-res...
indicator_namestringHuman-readable indicator name.Ministry of Education and Human Resources, Tertiary and Scientific Re...
country_iso3stringISO3 country or area code.MU
country_namestringCountry or area name.Mauritius
yearint64Observation year.2016
valuedoubleNumeric observation value.21110.0
unitstringMeasurement unit, when supplied by the source.source_units_unspecified
dimension_headstringSource dimension retained during long-form normalization.Ministry of Education and Human Resources, Tertiary and Scientific Re...
dimension_subheadstringSource dimension retained during long-form normalization.GENERAL
dimension_expensetypestringSource dimension retained during long-form normalization.Recurrent Expenditure
dimension_categorystringSource dimension retained during long-form normalization.Compensation of Employees
dimension_subcategorystringSource dimension retained during long-form normalization.Personal Emoluments
dimension_financialstatusstringSource dimension retained during long-form normalization.Provisional Actual
source_period_start_yearint64Start year inferred from source metadata.2017
source_period_end_yearint64End year inferred from source metadata.2018
source_period_labeldictionary<values=string, indices=int8, ordered=0>Source column from the original resource.2017-2018
source_providerdictionary<values=string, indices=int8, ordered=0>Publishing organization.MDPA
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.Ministry of Education and Human Resources, Tertiary and Scientific Re...
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.MOEHRTSR-budget-2017-2018-CSV.csv
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.2dc79167-9b66-4085-b1fa-747d0e56da7a
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.4db11248-7cb9-40ef-86bb-cd71b30e8785
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://data.govmu.org/dataset/2dc79167-9b66-4085-b1fa-747d0e56da7a/r...
license_iddictionary<values=string, indices=int8, ordered=0>Source license identifier.CC-BY-SA-4.0
retrieved_atdictionary<values=string, indices=int8, ordered=0>UTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-08-08T16:26:20Z

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-ministry-of-education-and-human-resources-tertiary-and-sci-f5068624")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

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

Filter By Geography

python
if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "MU"]

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 geography x period or indicator x period matrices
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_mauritius_ministry_of_education_and_human_resources_tertiary_and_sci_f506_2019,
  title        = {Ministry of Education and Human Resources Tertiary and Sci | Africa (MDPA)},
  author       = {MDPA},
  year         = {2019},
  url          = {https://data.govmu.org/dataset/ministry-education-and-human-resources-tertiary-and-scientific-research-budet-data-2017-2018},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-ministry-of-education-and-human-resources-tertiary-and-sci-f5068624}}
}

License

Released under CC BY-SA 4.0.

Original data is published by MDPA. 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://data.govmu.org/dataset/ministry-education-and-human-resources-tertiary-and-scientific-research-budet-data-2017-2018