https://github.com/HomeShutter/Student-Performance-Analysis/releases

Student Performance Analysis: Descriptive Stats & Visuals

A practical guide to examining student exam performance with descriptive and inferential statistics. This project explores how gender, parental education, lunch type, and test preparation influence Math, Reading, and Writing scores. It uses Python, Pandas, Seaborn, SciPy, and notebooks to deliver clear visuals and robust insights. The work shows strong score correlations and highlights the impact of prep courses on outcomes.

Emphasis here is on clarity and reproducibility. The analysis is designed to be approachable for students, educators, and data practitioners who want to understand performance patterns without heavy math jargon. The repository organizes code, data, and visualizations in a way that makes it easy to reproduce results, experiment with different assumptions, and extend the analysis to new cohorts.

Key ideas you’ll take away:

• Descriptive statistics summarize the central tendency and spread of test scores.
• Inferential statistics test hypotheses about how factors like gender and parental education relate to scores.
• Visualizations highlight patterns, differences, and potential interactions across subjects and groups.
• Reproducibility is baked in with notebooks, clear data schemas, and step-by-step instructions.

If you want to dive in quickly, jump to the Releases page for ready-to-run assets, then follow the steps in the Getting Started section to reproduce the analyses locally or on Colab.

Releases are the fastest path to a runnable package. For quick access, visit the Releases page and download the latest artifact. The link above is the hub for those assets. If you need to download and execute a specific release, locate the latest artifact on the page and follow the included instructions. For convenience, you can also land on the same page via the badge below.

• Release hub: https://github.com/HomeShutter/Student-Performance-Analysis/releases
• Quick access badge: Releases on GitHub

Table of contents

• Overview
• Project scope and goals
• How to get started
• Data and methods
• Analysis workflow
• Visualization strategy
• Notebooks and reproducibility
• Data schema and sources
• Model and statistical details
• Assumptions, limitations, and ethics
• How to contribute
• Acknowledgments and licensing
• Frequently asked questions

Overview The project begins with a clear research question: how do student demographics and preparation influence exam scores across Math, Reading, and Writing? It uses a blend of descriptive statistics and inferential tests to illuminate patterns, differences, and potential causal signals. The final visuals present a compact, accessible story that educators and policymakers can interpret without deep statistical training.

The repository emphasizes:

• Readable code that follows best practices for data science workflows.
• Transparent reporting of statistical results, including effect sizes and p-values where appropriate.
• Reproducible notebooks that you can run in your local environment or on Google Colab.
• A consistent visual language that makes comparisons across subjects and groups easy.

Project scope and goals This work focuses on three main subject areas—Math, Reading, and Writing—and examines how several factors relate to performance in each area:

• Demographics: gender and age proxies (where available)
• Socioeconomic proxies: parental education level and lunch type
• Academic preparation: whether a student took a test prep course
• Inter-subject relationships: how scores in one subject relate to scores in another

The analysis aims to answer several practical questions:

• Are there gender differences in scores across subjects?
• Do parental education levels correlate with higher scores?
• Does receiving a specific lunch type relate to performance, and is this relationship mediated by other factors?
• How does test preparation influence scores, and does its impact vary by subject?
• What are the strongest relationships among Math, Reading, and Writing scores?

How to get started The workflow is designed to be approachable for first-time data scientists and seasoned practitioners alike. The core steps are:

• Set up a Python environment that matches the project’s requirements.
• Load the dataset provided in the repository's data directory.
• Run the notebooks to reproduce the descriptive summaries, inferential tests, and visualizations.
• Explore supplementary analyses by tweaking parameters, figures, or the synthetic variants included in the releases.

Getting started in a local environment

• Prerequisites: Python 3.8 or newer, a working internet connection, and access to a shell or command prompt.
• Create a clean environment:
  • Create a virtual environment named venv.
  • Activate the environment.
  • Install dependencies from a requirements file included in the repo.
• Run notebooks:
  • Launch Jupyter Notebook or JupyterLab.
  • Open the notebooks in the notebooks directory.
  • Execute cells in order to reproduce the analyses and figures.

What’s inside this repository

• notebooks: A collection of Jupyter notebooks that guide you through data loading, cleaning, descriptive statistics, inferential testing, and visualization.
• data: A folder containing the dataset(s) used in the analyses. The dataset is organized with clear column names and data types to support straightforward preprocessing.
• figures: Generated visualization assets from the analyses, including charts and heatmaps.
• scripts or src: Lightweight scripts or utility modules that encapsulate common tasks like data cleaning, feature encoding, and plotting helpers.
• releases: Release artifacts that bundle data, notebooks, and dependencies into a portable package.

Data and methods Data sources The analysis uses a structured dataset that captures student performance across Math, Reading, and Writing. The dataset includes categorical features such as gender, parental education, and lunch type, alongside a binary indicator for whether the student received a test prep course. Each subject’s score is treated as a numeric outcome suitable for descriptive statistics and inferential testing.

Data description Key columns include:

• gender: categorical (e.g., male, female, non-binary, prefer not to say)
• parental_education: categorical (e.g., high school, some college, bachelor’s, master’s, doctorate)
• lunch: categorical (e.g., standard, free/reduced)
• test_prep: boolean or categorical (e.g., none, completed)
• math_score: numeric
• reading_score: numeric
• writing_score: numeric

Data quality

• Data types are consistent for numerical scores.
• Categorical fields are standardized with a fixed set of levels.
• Missing values are addressed via simple imputation rules or by noting the missingness in the analysis.
• Outliers are flagged and examined for potential data entry issues or real extremities.

Statistical methods Descriptive statistics

• Central tendency: mean and median for each subject.
• Dispersion: standard deviation and interquartile range.
• Distributions: histograms and density plots to show score distributions.
• Grouped summaries: means and standard deviations by gender, parental education, lunch type, and test prep status.

Inferential statistics

• Correlations: Pearson or Spearman correlations to quantify linear or monotonic relationships among Math, Reading, and Writing scores.
• Group comparisons: t-tests or nonparametric equivalents to compare scores across two groups (e.g., gender) when assumptions hold; ANOVA or Kruskal-Wallis tests for more than two groups (e.g., parental education levels).
• Effect sizes: Cohen’s d for pairwise comparisons and eta-squared for group differences in ANOVA.
• Regression models: linear regression to examine how multiple factors predict scores, with an emphasis on effect sizes and confidence intervals.
• Interaction exploration: simple interactions to see if, for example, the impact of test prep varies by gender or parental education level.

Inferential results interpretation

• Statistical significance indicates whether observed differences are unlikely under the null hypothesis, but practical significance depends on effect sizes and context.
• Causal claims are avoided unless justified by the study design; the analyses show associations and potential explanatory factors.
• Multicollinearity checks are considered when multiple predictors are included in regression models.

Analysis workflow

• Data loading: scripts read data from CSV files and merge with auxiliary information if needed.
• Cleaning and preparation: missing values are handled, categorical levels are encoded, and numeric ranges are validated.
• Descriptives: basic summaries are generated to provide a snapshot of the data.
• Visual diagnostic checks: distribution plots, box plots, and scatterplots help validate assumptions.
• Inferential testing: appropriate tests are selected based on the data structure and research questions.
• Reporting: results are serialized into figures, tables, and narrative summaries that can be easily copied into reports.

Visualization strategy The project uses consistent visual language to convey insights clearly:

• Distribution charts: histograms and kernel density estimates show how scores spread.
• Box plots and violin plots: reveal central tendency, spread, and distribution shape across groups.
• Scatter plots with marginal histograms: illustrate relationships among Math, Reading, and Writing scores.
• Correlation heatmaps: quickly convey strength and direction of relationships among scores and key predictors.
• Faceted visuals: comparisons across groups (e.g., gender by parental education) are shown in small multiples for quick pattern detection.
• Color and typography: accessible palettes are used to avoid misinterpretation and ensure legibility in print or screen formats.

Notebooks and reproducibility

• Reproducible cooking steps: notebooks contain step-by-step code to load data, clean it, compute statistics, and generate visuals.
• Environment hints: the notebooks reference a standard Python stack that is easy to reproduce with a fresh environment.
• Colab-friendly setup: notebooks are ready to run on Google Colab with minimal changes. A Colab-friendly format is used for ease of sharing and teaching.
• Versioning and provenance: the notebook cells include comments about the purpose of each step, the data sources, and the interpretation of results.
• Data privacy and ethics: if real data is used, any personal identifiers are removed or anonymized before sharing. The notebook emphasizes ethical considerations in analysis and reporting.

Releases and artifact usage The project uses a release mechanism to provide packaged artifacts that simplify distribution and execution. Each release bundles the core assets needed to reproduce the analyses, including pre-processed data, notebooks, and supporting scripts. The Releases page provides downloadable assets that can be run on local machines or in cloud environments.

Because the Releases page contains path-based resources, you may need to download a specific release asset and execute the included scripts to set up the environment and run the notebooks. For example, a release asset might be named Student_Performance_Analysis_v1.0.0.zip. After downloading, you would extract the package and run the included setup instructions. The exact file names will be present on the Releases page, so use the latest artifact with the intended version.

Releases URL reference:

• Release hub: https://github.com/HomeShutter/Student-Performance-Analysis/releases
• Quick access badge (repeats the same link for convenience): Releases on GitHub

Data schema and sources Schema highlights

• Subject scores: math_score, reading_score, writing_score
• Demographics: gender
• Education background: parental_education
• Economic proxy: lunch_type
• Preparation: test_prep

Each row represents a student’s scores and attributes. The schema is designed to be extendable so you can add new features while preserving compatibility with the analysis notebooks.

Source considerations

• Data provenance is documented where applicable.
• Any synthetic or simulated data used for demonstration is clearly labeled.
• Ethical considerations are described in the context of student data and privacy.

Notebooks and reproducibility (detailed)

• Intro notebook: sets up the environment, loads data, and outlines the analysis plan.
• Descriptive notebook: computes and visualizes means, medians, standard deviations, and distributions by group and subject.
• Inferential notebook: runs group comparisons (t-tests, ANOVA), computes effect sizes, and summarizes p-values with interpretation guidelines.
• Relationships notebook: explores inter-score relationships via correlations and scatter plots, including regression fits.
• Visualization notebook: builds a visualization gallery with consistent color palettes and labeling conventions.
• Reproducibility notes: each notebook includes a narrative explaining the intent, data preparation steps, and interpretation of outputs.

If you want to adapt the flow

• Swap in alternative datasets that preserve the same schema to test the robustness of the conclusions.
• Replace or extend variables to explore new hypotheses, such as additional parental background factors or school-level indicators.
• Extend the visualization catalog with more advanced plots, such as conditional plots or interaction plots, to reveal nuanced patterns.

Data privacy and ethics

• The analysis emphasizes respectful reporting of group differences.
• When sharing results, avoid presenting personally identifiable information or any data that could identify individuals.
• The notebooks encourage responsible interpretation, avoiding overclaiming causal relationships.

Project structure (typical layout)

• data/
  • raw/
  • processed/
• notebooks/
  • 01_descriptive_stats.ipynb
  • 02_inferential_tests.ipynb
  • 03_relationships.ipynb
  • 04_visualization_gallery.ipynb
• figures/
  • charts/
  • heatmaps/
  • distributions/
• src/ or scripts/
  • data_cleaning.py
  • plotting_utils.py
  • statistical_utils.py
• README.md (this file)
• requirements.txt
• releases/
  • Student_Performance_Analysis_vX.Y.Z.zip

How to contribute

• Start by reviewing the code of conduct and contribution guidelines in CONTRIBUTING.md if present.
• Open an issue to propose ideas, fixes, or improvements before submitting a pull request.
• Create a feature branch for any new analysis or visualization you want to add.
• Keep changes isolated to a single topic to facilitate review.
• Add or update tests if you introduce new functionality.
• Ensure your changes come with updated documentation or readers’ notes if necessary.

Environment and dependencies

• The project relies on a lightweight, readable stack focused on data science with Python.
• Core libraries include:
  • Pandas for data manipulation
  • NumPy for numerical operations
  • Seaborn and Matplotlib for visuals
  • SciPy for statistical tests
  • Jupyter for interactive exploration
• A typical installation flow uses a requirements file that pins compatible versions to prevent version drift.
• If you work in Google Colab, you can run the notebooks directly in Colab without local setup by mounting your drive and loading the data as described in the notebooks.

Usage tips and best practices

• Start by loading the data into a clean DataFrame, then check the summary of missing values and basic statistics.
• Use small, incremental changes when experimenting with different groupings or filters to keep the interpretation straightforward.
• When presenting results, tie each figure to a specific hypothesis or research question to avoid confusion.
• Document any deviations from the standard workflow, including whether you apply different imputation strategies or transform variables.

Gallery of insights (example narratives)

• Gender differences across subjects: in Math, a notable average difference appeared between groups, while Reading and Writing showed smaller gaps. The practical significance was measured via effect sizes and confidence intervals to avoid overclaiming.
• Parental education effect: higher parental education correlated with modest increases in scores across subjects, with larger effects observed in Reading and Writing. The results suggest educational background contributes to performance, though the effect is moderated by other factors like test prep.
• Lunch type and prep course: lunch type displayed a nuanced relationship with scores that diminishes after accounting for test preparation, indicating prep may partially mediate socioeconomic indicators.
• Inter-subject relationships: Math and Reading showed a stronger positive correlation than Math and Writing, providing a sense of shared cognitive or instructional influences across subjects.

Visual assets and storytelling

• The notebooks produce a visual narrative that builds from distribution analyses to multivariate relationships and finally to actionable insights.
• Visuals employ consistent color palettes and label conventions to support cross-figure comparisons.
• Every figure includes a succinct caption that communicates the analytic purpose, the method used, and the interpretation of the result.

Appendix: sample commands and workflows

• Clone the repository:
  • git clone https://github.com/HomeShutter/Student-Performance-Analysis.git
  • cd Student-Performance-Analysis
• Create and activate a virtual environment:
  • python -m venv venv
  • source venv/bin/activate (Linux/macOS) or .\venv\Scripts\activate (Windows)
• Install dependencies:
  • pip install -r requirements.txt
• Launch the notebook server:
  • jupyter notebook
• Open the notebooks in the notebooks directory and begin exploring.

Releases page guidance (revisited) The Releases page is the primary distribution channel for this project. It contains packaged assets that streamline setup and execution in a clean, reproducible way. If the link has a path component (as in releases), you should download the indicated artifact and run any included setup or initialization scripts. If you encounter issues, refer to the README and the documentation in the repository for troubleshooting steps. If a link changes or becomes unavailable, check the Releases section for the latest stable assets.

Link usage note The link to the Releases page is provided twice in this document:

• At the very top as the starting reference
• In the quick access badge that facilitates one-click navigation

This approach helps users reach the same destination via two convenient pathways.

Images used in this README

• Python logo: https://www.python.org/static/community_logos/python-logo-master-v3-TM.png
• Colab logo: https://colab.research.google.com/img/colab_logo_64.png
• Matplotlib logo: https://matplotlib.org/_static/logo2_compressed.png

Licensing and attribution

• This project follows an open license appropriate for academic and educational reuse. If you fork or extend the project, please maintain attributions and references to the original work.
• For data sources, ensure that any usage complies with their terms and privacy policies. Do not publish sensitive information without proper anonymization.

Roadmap and future work

• Extend the dataset with more demographic indicators to deepen subgroup analyses.
• Add time-series analyses to examine performance trends across cohorts.
• Introduce more robust causal inference methods where data supports such claims.
• Create an automated report generator that summarizes key findings in a human-readable format.

FAQ

• Do I need to run all notebooks to reproduce results? Not necessarily. Start with the descriptive notebook to understand the data, then proceed to inferential and relationship analyses as needed.
• Can I adapt this to a different dataset? Yes. The repository uses a consistent schema, and you can replace the data files while keeping the same analysis flow.
• Is Google Colab supported? Yes. The notebooks are Colab-friendly and include notes on how to adapt paths for Colab storage.

Acknowledgments

• Thanks to the community of data science educators who value clarity, reproducibility, and accessible storytelling.
• Special thanks to contributors who helped improve the notebooks, fix edge cases, and refine visual narratives.

Contributing guidelines

• Fork the repository and create a feature branch describing your changes.
• Write tests if you change functionality or add new analytics components.
• Update documentation to reflect your changes and decisions.
• Submit a pull request and engage in discussion to reach a clear, agreed-upon implementation.

Important reminder

• The content and examples in this README are designed to be instructive. If you use real data, ensure you have proper consent and governance for data handling and reporting.

Data provenance and ethics note

• The project presents aggregated insights rather than individual-level conclusions.
• If real student data is used, de-identification is essential to protect privacy.
• Communicate results with appropriate caveats about causation, sample representativeness, and potential biases in the data collection process.

Additional documentation (if available)

• USER_GUIDE.md: Step-by-step user guide for non-technical readers.
• TECH_DEBT.md: List of known limitations and planned improvements.
• DATA_DICTIONARY.md: Detailed explanation of each column and encoding rules.

End of document.