https://github.com/memgonzales/pisa-2018-analysis
Jupyter notebook presenting the process of data preparation, research question formulation, data analysis, and data modeling with the goal of extracting insights from the 2018 PISA Dataset
https://github.com/memgonzales/pisa-2018-analysis
data-cleaning data-modeling data-science data-visualization exploratory-data-analysis jupyter-notebook matplotlib numpy oecd-data pandas pisa scipy statistical-inference
Last synced: 9 days ago
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Jupyter notebook presenting the process of data preparation, research question formulation, data analysis, and data modeling with the goal of extracting insights from the 2018 PISA Dataset
- Host: GitHub
- URL: https://github.com/memgonzales/pisa-2018-analysis
- Owner: memgonzales
- Created: 2022-01-08T01:22:58.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-12-26T16:03:57.000Z (over 2 years ago)
- Last Synced: 2025-06-13T11:07:42.123Z (9 days ago)
- Topics: data-cleaning, data-modeling, data-science, data-visualization, exploratory-data-analysis, jupyter-notebook, matplotlib, numpy, oecd-data, pandas, pisa, scipy, statistical-inference
- Language: Jupyter Notebook
- Homepage:
- Size: 3.02 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 2018 PISA Analysis
![badge][badge-jupyter]
![badge][badge-pandas]
![badge][badge-numpy]
![badge][badge-scipy]
This project presents the process of data preparation, research question formulation, data analysis, and data modeling with the goal of extracting insights from the **2018 PISA Dataset**. The [PISA](https://www.oecd.org/pisa/), which stands for **Programme for International Student Assessment** is a worldwide set of tests conducted by the Organisation for Economic Co-operation and Development (OECD) to gauge the knowledge and competence of 15-year-old students in the key subject areas of reading, mathematics, and science
This is a major course output in a statistical modeling and simulation class under Mr. Arren C. Antioquia of the Department of Software Technology, De La Salle University.
## Task
The task is to create a [Jupyter notebook](https://github.com/memgonzales/pisa-2018-analysis/blob/master/2018%20PISA%20Analysis.ipynb) that presents the process leading up to the generation of insights from a raw dataset:
- Dataset Representation
- Data Cleaning
- Exploratory Data Analysis
- Research Questions
- Statistical Inference
- Insights and Conclusions
The complete project specifications can be found in the document [`Project Specifications.pdf`](https://github.com/memgonzales/pisa-2018-analysis/blob/master/Project%20Specifications.pdf).
## Datasets
The following real-world data sources (one primary dataset and two auxiliary datasets) were used:
Dataset | Source
-- | --
2018 OECD PISA School Questionnaire Dataset *(Primary Dataset)* | [Kaggle](https://www.kaggle.com/dilaraahan/pisa-2018-school-questionnaire)
2018 OECD PISA Average Score of Mathematics, Science, and Reading Test Scores Dataset *(Auxiliary Dataset)* | [FactsMaps](https://factsmaps.com/pisa-2018-worldwide-ranking-average-score-of-mathematics-science-reading/)
ISO 3166-1 alpha-3 Code List *(Auxiliary Dataset)* | [ISO](https://www.iso.org/publication/PUB500001.html)
## Built Using
This project is a Jupyter notebook, with the following Python libraries and modules used:
Library/Module | Description | License
-- | -- | --
[`os`](https://docs.python.org/3/library/os.html) | Provides miscellaneous operating system interfaces | Python Software Foundation License
[`pandas`](https://pandas.pydata.org/) | Provides functions for data analysis and manipulation | BSD 3-Clause "New" or "Revised" License
[`numpy`](https://numpy.org/) | Provides a multidimensional array object, various derived objects, and an assortment of routines for fast operations on arrays | BSD 3-Clause "New" or "Revised" License
[`scipy`](https://scipy.org/) | Provides efficient numerical routines, such as those for numerical integration, interpolation, optimization, linear algebra, and statistics | BSD 3-Clause "New" or "Revised" License
[`matplotlib`](https://matplotlib.org/) | Provides functions for creating static, animated, and interactive visualizations | Matplotlib License (BSD-Compatible)
*The descriptions are taken from their respective websites.*
[badge-selenium]: https://img.shields.io/badge/Selenium-43B02A?style=flat&logo=Selenium&logoColor=white
[badge-github-actions]: https://img.shields.io/badge/GitHub_Actions-2088FF?style=flat&logo=github-actions&logoColor=white
[badge-heroku]: https://img.shields.io/badge/Heroku-430098?style=flat&logo=heroku&logoColor=white
## Authors
- **Mark Edward M. Gonzales**
[email protected]
[email protected]
- **Hylene Jules G. Lee**
[email protected]
[email protected]
[badge-jupyter]: https://img.shields.io/badge/Jupyter-F37626.svg?&style=flat&logo=Jupyter&logoColor=white
[badge-pandas]: https://img.shields.io/badge/Pandas-2C2D72?style=flat&logo=pandas&logoColor=white
[badge-numpy]: https://img.shields.io/badge/Numpy-777BB4?style=flat&logo=numpy&logoColor=white
[badge-scipy]: https://img.shields.io/badge/SciPy-654FF0?style=flat&logo=SciPy&logoColor=white