Freelancer profile translated to English.
Back to original languageAbout Chadi
Data Analyst | Statistics & Modeling R | Quantitative Studies & Surveys
My added value: science-based insights
- Beyond the numbers, I provide clear insights and analytical recommendations to guide your decisions while explaining the behaviors, profiles, and explanatory factors highlighted by the data, linking your results to existing scientific literature.
- As part of my final internship (Master's 2 in Quantitative Sociology, validated at the University of Toulouse Jean Jaurès), I support your research and decision-making projects through rigorous data analysis and the production of actionable insights.
My areas of intervention
- Data Analysis (Descriptive and Inferential Statistics)
- Statistical Modeling: Linear and Logistic Regressions
- PCA, MCA, Clustering (k-means), Typologies, and Profiling
- Analysis of Large-Scale Databases (50,000+ observations)
- Indicator Construction and Scoring
- Survey Data Analysis and Processing (Sociology)
- Dataviz (Data Visualization and Analytical Reporting)
- Presentation of results for non-specialist audiences
Tools
- R • Python • Quarto • Jamovi • LimeSurvey • Excel / Calc
What I bring
Beyond the technical processing of data, I offer an analytical approach that allows:
- Identifying trends and explanatory factors,
- Building typical user or customer profiles,
- Producing clear and visual analytical reports,
- Delivering professional reports in PDF, HTML, or Quarto formats,
- Interpreting statistical results in light of existing scientific research and models to support decision-making.
Recent Projects
1. School Climate and Achievement — DEPP (15,833 students)
2. Sense of Nighttime Security in France — (ESS9)
Discover all my projects and my CV on my GitHub: github.com/Chadi-Gamal/
French
Native or bilingual
English
Native or bilingual
Arabic
Native or bilingual
Remote only
Primarily works remotely
Experience
- Projet Indépendant / Recherche Personnelle (European Social Survey)Data Analyst / Statistician - Quantitative & Sociological AnalysisHEALTH AND WELLNESSMay 2026 - June 2026 (1 month)Toulouse, France
Data Analyst — Determinants of the Sense of Nighttime Security in France (R, ESS9, Multiple Linear Regression)
Using data from the European Social Survey (ESS9), this independent research project analyzes the impact of 15 factors (socio-demographics, health, values) on the sense of nighttime security in France (sample: 2,010 respondents).The objective is to isolate the specific effect of each variable "all else being equal" and to translate these statistical results into fine-grained sociological insights (vulnerabilities, urban dynamics).Main Missions*Data Cleaning and Structuring (ESS9):Extraction of the French subsample, strict processing of missing values (NA), recoding and harmonization of qualitative and ordinal variables using R.*Univariate and Bivariate Descriptive Explorations:Performing frequency distributions, significance tests (Chi-squared, ANOVA), and calculating Cramer's V to measure the strength of raw relationships. Validating complex hypotheses (tests of gender/health mediation effects and age control).*Statistical Modeling and Multivariate Analysis:Step-by-step specification and estimation of **7 nested multiple linear regression models**. Monitoring explained variance (Adjusted R²) by variable block (living environment, well-being, health, values) to neutralize confounding effects.*Reporting, Valorization, and Reproducibility:Production of a dynamic report using Quarto (HTML). Design of dataviz (bar plots, box plots with means) and generation of the comparative table of the 7 models using the modelsummary package (p-values and significance stars). Writing theoretical interpretations (Chicago School, informal social control).Source Code and Deliverables:github.com/Chadi-Gamal/analyse-securite-nocturne-ess9 - Service Commun de la Documentation (SCD) de l'Université de Toulouse & PUD-T (Plateforme Universitaire de Données de Toulouse)Data Analyst — Quantitative Studies & User Typologies (R, PCA, MCA, Clustering)PUBLIC SECTORApril 2026 - Today (4 months)Toulouse, France
Data Analyst — Quantitative Studies & User Typologies (R, PCA, MCA, Clustering)
Within the Joint Service of University Documentation (SCD) of the University of Toulouse, I conduct advanced quantitative analyses on large-scale surveys (+4000 respondents).My work involves transforming complex survey data into actionable decision-making insights for the management teams of university libraries.The objective is also to build user profiles derived directly from data analysis (not predefined). These typologies help to better understand student behavior towards libraries, as well as the University's services and activities, in order to improve the overall understanding of campus life.Main Missions- Survey data cleaning and structuring
- Univariate and bivariate descriptive analyses
- Inferential analyses
- Statistical modeling (regressions)
- Multivariate analysis on qualitative data (MCA)
- Classification and segmentation (CAH / HCPC using R)
- Construction of finely characterized user typologies and profiles
- Statistical validation of profiles using significance indicators (V-test)
- Construction of synthetic indicators (perceived quality scoring)
Analysis, Comparison, and Decision Support- Comparative analyses between several libraries and sites
- Presentation of results in clear and readable tables
- Identification of strengths, areas for improvement, and action levers
- Support for decision-making for service management teams
Reporting and Valorization of Results- Production of analytical reports (R / Quarto / HTML / PDF)
- Data visualization and explanatory graphics
- Statistical interpretation of results
- Sociological interpretation of behaviors and perceptions
- Formulation of operational recommendations
- Université Toulouse Jean JaurèsData Analyst — Quantitative Research Project (DEPP / Jamovi)PUBLIC SECTORJanuary 2025 - February 2026 (1 year and 1 month)Toulouse, France
Data Analyst — Statistical Analysis of School Bullying and Achievement Inequalities (DEPP, Jamovi, Linear Regressions)
Within a research project in social sciences and educational sciences, I conduct a quantitative analysis using DEPP survey data (over 15,000 observations) to study the link between school bullying and academic performance among middle school students.The objective is to measure the specific effect of bullying on academic achievement, while controlling for socio-demographic, school, cultural, and relational factors, in a process based on scientific literature.Indicator Construction and Bullying Scoring- Construction of a bullying index (scoring) from several survey items
- Measurement of the intensity, frequency, and exposure to bullying
- Creation of a robust synthetic indicator for statistical analyses
- Testing different score specifications (weighted / unweighted) to verify robustness
Statistical Modeling (Jamovi)- Implementation of 6 nested linear regression models using Jamovi
- Estimation of the raw effect and then the net effect of bullying after progressive addition of control variables
- Control for factors: gender, grade, school type (REP+, SEGPA, IPS), academic engagement, social media use, cultural capital (books), social capital (friends)
Analysis and Interpretation of Results- Analysis of coefficients, marginal effects, and adjusted R²
- Comparison of nested models to evaluate the contribution of variables
- Identification of explanatory mechanisms: mediation effects, suppression effects, and interaction effects
- Highlighting the robustness of the effect of bullying on the perception of academic level
- Interpretation of statistical results in light of existing theoretical frameworks to offer a sociological perspective on academic trajectories.
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Education
- Master of Sociology — Networks and Societies trackUniversity of Toulouse - Jean JaurèsMaster de Sociologie
- Engineering DiplomaUniversity of Alexandria2010Diplôme d'ingénieur