{"id":21009468,"url":"https://github.com/memgonzales/pisa-2018-analysis","last_synced_at":"2025-06-13T11:07:55.882Z","repository":{"id":112399005,"uuid":"445695292","full_name":"memgonzales/pisa-2018-analysis","owner":"memgonzales","description":"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","archived":false,"fork":false,"pushed_at":"2022-12-26T16:03:57.000Z","size":3162,"stargazers_count":6,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-06-13T11:07:42.123Z","etag":null,"topics":["data-cleaning","data-modeling","data-science","data-visualization","exploratory-data-analysis","jupyter-notebook","matplotlib","numpy","oecd-data","pandas","pisa","scipy","statistical-inference"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/memgonzales.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2022-01-08T01:22:58.000Z","updated_at":"2025-04-04T16:55:05.000Z","dependencies_parsed_at":"2023-05-14T12:30:11.957Z","dependency_job_id":null,"html_url":"https://github.com/memgonzales/pisa-2018-analysis","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/memgonzales/pisa-2018-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/memgonzales%2Fpisa-2018-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/memgonzales%2Fpisa-2018-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/memgonzales%2Fpisa-2018-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/memgonzales%2Fpisa-2018-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/memgonzales","download_url":"https://codeload.github.com/memgonzales/pisa-2018-analysis/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/memgonzales%2Fpisa-2018-analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259634373,"owners_count":22887698,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-cleaning","data-modeling","data-science","data-visualization","exploratory-data-analysis","jupyter-notebook","matplotlib","numpy","oecd-data","pandas","pisa","scipy","statistical-inference"],"created_at":"2024-11-19T09:16:55.196Z","updated_at":"2025-06-13T11:07:55.871Z","avatar_url":"https://github.com/memgonzales.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 2018 PISA Analysis\n![badge][badge-jupyter]\n![badge-python](https://img.shields.io/badge/python-3670A0?style=flat\u0026logo=python\u0026logoColor=white)\n![badge][badge-pandas]\n![badge][badge-numpy]\n![badge][badge-scipy]\n\nThis 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 \n\nThis 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.\n\n## Task\nThe 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:\n- Dataset Representation\n- Data Cleaning\n- Exploratory Data Analysis\n- Research Questions\n- Statistical Inference\n- Insights and Conclusions\n\nThe complete project specifications can be found in the document [`Project Specifications.pdf`](https://github.com/memgonzales/pisa-2018-analysis/blob/master/Project%20Specifications.pdf).\n\n## Datasets\nThe following real-world data sources (one primary dataset and two auxiliary datasets) were used:\n\nDataset | Source\n-- | --\n2018 OECD PISA School Questionnaire Dataset *(Primary Dataset)* | [Kaggle](https://www.kaggle.com/dilaraahan/pisa-2018-school-questionnaire)\n2018 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/)\nISO 3166-1 alpha-3 Code List *(Auxiliary Dataset)* | [ISO](https://www.iso.org/publication/PUB500001.html)\n\n## Built Using\nThis project is a Jupyter notebook, with the following Python libraries and modules used:\n\nLibrary/Module |\tDescription |\tLicense\n-- | -- | --\n[`os`](https://docs.python.org/3/library/os.html)\t| Provides miscellaneous operating system interfaces\t| Python Software Foundation License\n[`pandas`](https://pandas.pydata.org/)\t| Provides functions for data analysis and manipulation\t| BSD 3-Clause \"New\" or \"Revised\" License\n[`numpy`](https://numpy.org/)\t| Provides a multidimensional array object, various derived objects, and an assortment of routines for fast operations on arrays\t| BSD 3-Clause \"New\" or \"Revised\" License\n[`scipy`](https://scipy.org/)\t| Provides efficient numerical routines, such as those for numerical integration, interpolation, optimization, linear algebra, and statistics\t| BSD 3-Clause \"New\" or \"Revised\" License\n[`matplotlib`](https://matplotlib.org/)\t| Provides functions for creating static, animated, and interactive visualizations\t| Matplotlib License (BSD-Compatible)\n\n*The descriptions are taken from their respective websites.*\n\n[badge-selenium]: https://img.shields.io/badge/Selenium-43B02A?style=flat\u0026logo=Selenium\u0026logoColor=white\n[badge-github-actions]: https://img.shields.io/badge/GitHub_Actions-2088FF?style=flat\u0026logo=github-actions\u0026logoColor=white\n[badge-heroku]: https://img.shields.io/badge/Heroku-430098?style=flat\u0026logo=heroku\u0026logoColor=white\n\n## Authors\n- **Mark Edward M. Gonzales** \u003cbr/\u003e\n  mark_gonzales@dlsu.edu.ph \u003cbr/\u003e\n  gonzales.markedward@gmail.com\n  \n- **Hylene Jules G. Lee** \u003cbr/\u003e\n  hylene_jules_lee@dlsu.edu.ph \u003cbr/\u003e\n  lee.hylene@gmail.com\n\n[badge-jupyter]: https://img.shields.io/badge/Jupyter-F37626.svg?\u0026style=flat\u0026logo=Jupyter\u0026logoColor=white\n[badge-pandas]: https://img.shields.io/badge/Pandas-2C2D72?style=flat\u0026logo=pandas\u0026logoColor=white\n[badge-numpy]: https://img.shields.io/badge/Numpy-777BB4?style=flat\u0026logo=numpy\u0026logoColor=white\n[badge-scipy]: https://img.shields.io/badge/SciPy-654FF0?style=flat\u0026logo=SciPy\u0026logoColor=white\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmemgonzales%2Fpisa-2018-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmemgonzales%2Fpisa-2018-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmemgonzales%2Fpisa-2018-analysis/lists"}