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

This data analysis project is done on PISA 2018 data on Thai students' education achievement.

Udacity Nanodegree Program, a data analysis project using the Pisa2012 dataset.

# PISA 2012 Data ## by Nadine Amin ## Dataset > PISA is a survey of students' skills and knowledge as they approach the end of compulsory education. It focuses on examining how well prepared the students are for life beyond school. > Around 510,000 students in 65 economies took part in the PISA 2012 assessment of reading, mathematics and science representing about 28 million 15-year-olds globally. Of those economies, 44 took part in an assessment of creative problem solving and 18 in an assessment of financial literacy. ## Summary of Findings > Before starting this study, I thought the features that would affect the total scores the most were the teachers' influences, the students' immigration status, the class size, and the parents' highest schooling. However, almost none of my assumptions were correct once I started to see the relationships of the variables with the total scores and with other variables. > The number of cellphones, TVs, computers & books, the parents' schooling & jobs, and the homework study time were the variables that affected the total scores. > The higher the number of cellphones, TVs, computers and books, the higher the chances of getting a better total score. This could be because the family's social status was better, and therefore provided better support for the students. > As long as the parents' schooling was level 3A or higher, there is a good chance that the students would get higher grades. Furthermore, parents who had full-time jobs resulted in their children getting higher scores. This could be because having role models to look up to will make you work harder and believe in yourself more. > Finally, students who studied for longer hours had a higher chance of scoring better. ## Key Insights for Presentation > In the presentation, I will show the plots that had an effect on the total score the most. Those include the bivariate plots of the variables mentioned above against the total score. I will also include the multivariate plot of the father and mother's jobs vs. the number of cellphones vs. the total score.

In this project, I use Python visualization libraries to investigate the 2012 PISA dataset, which I use to create an jupyter-notebook that outlines Data Wrangling and Visualization process with intriguing properties, trends, and relationships. Libraries learned: Seaborn, Matplotlib, Numpy, Pandas.

This project is divided into two major parts. Exploratory data analysis on a PISA 2012 survey dateset. You will use Python data science and data visualization libraries to explore the dataset’s variables and understand the data’s structure, oddities, patterns and relationships. The analysis in this part should be structured, going from simple univariate relationships up through multivariate relationships, but it does not need to be clean or perfect. There is no one single answer that needs to come out of a given dataset. This part of the project is your opportunity to ask questions of the data and make your own discoveries. It’s important to keep in mind that sometimes exploration can lead to dead ends, and that it can take multiple steps to dig down to what you’re truly looking for. Be patient with your steps, document your work carefully, and be thorough in the perspective that you choose to take with your dataset. In the second part, you will take your main findings from your exploration and convey them to others through an explanatory analysis. To this end, you will create a slide deck that leverages polished, explanatory visualizations to communicate your results. This part of the project should make heavy use of the first part of the project. Select one or two major paths in your exploration, choose relevant visualizations along that path, and then polish them to construct a story for your readers to understand what you found.

Analysis of OECD’s Programme for International Student Assessment 2012 survey dataset (PISA2012)

Analysis of PISA2012 Student Assessment Dataset

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Analyse des données produites par l'enquête PISA (Projet ENSAE)

This Rep attempts to explore and analyse the PISA Global Education dataset. Due to the large volumn of the dataset, this repo zooms in on a sample of the real Data, and further narrows down to a few featues which we explore in the notebook

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Material for courses on Computing & Data Analysis & Modeling held by L'EMbeDS members of Sant'Anna Pisa - Academic Year 2024/2025

This project focuses on systematic data exploration, building from univariate to bivariate and to Multivariate

Exploratory Data Analysis of the 2015 PISA results

In this project I use Python visualization libraries to explore the 2012 PISA dataset (Exploration_template), which I use to produce a presentation (Summary_slides.slides) that illustrates interesting properties, trends, and relationships. Skills learned: Python, Matplotlib, NumPY, Pandas

Analysis of the year-2018 PISA survey data in Indonesia

Computational Mathematics for Learning and Data Analysis Exam at University of Pisa made by Riccardo Amadio and Andrea Iommi

Computational Mathematics for Learning and Data Analysis project. Computer Science Master Degree, University of Pisa. A.Y 2024/2025

Material for the Deep Learning for Multimedia Retrieval and Analysis (DLMRA) course - Master in Big Data (MBD) of the University of Pisa

C++ IPM solver for minQP problems. Project for the Computational Mathematics for Learning and Data Analysis exam (9 ECTS), professor Antonio Frangioni. University of Pisa, master degree in Computer Science

Investigated the 2015 and 2018 UK PISA data to assess student performance trends across the four UK nations. Conducted a comparative analysis with Estonia to identify potential educational strategies for improvement.

Large Databases in Applied Research was a graduate course I completed at NYU. This repository contains the annotated do files for the data analysis performed in STATA. Data came from mostly educational datasets, including PISA, IPEDS and NHES.

This repository contains a complete pipeline for the extraction, transformation, loading, and analysis of data from the Programme for International Student Assessment (PISA). The project integrates large-scale educational data into a structured database environment and applies advanced machine learning techniques to perform predictive analytics.

Material produced for my MA thesis in Digital Humanities at University of Pisa (Italy). The work consists on the research of an expressive methodology to represent and study personal mobility data, in particular exploiting the prefix tree architecture. One of the outputs produced is the python module treemob, containing all the tools implemented for the data representation and for the analysis.

Project repository for the Data Mining Andavced course in University of Pisa - AY 2020 - 2021. The Free Music Archive (FMA) is an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections. The project is divided in five principal modules, all having the main objective to find if a track is an Hit or Not-Hit: 1- Introduction, Imbalanced Learning and Anomaly Detection 2- Advanced Classification Methods 3- Time Series Analysis 4- Sequential Patterns and Advanced Clustering 5- Explainability

Simple analysis framework in pyROOT for the tt analysis in the CMS Data Analysis School 2019 at Pisa

"Distributed Data Analysis and Mining" Class' Team Project - MSc in Data Science and Business Informatics @ University of Pisa

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Analysis of PISA data