I am a data science and analytics professional with experience in monitoring, evaluation, and learning for donor-funded programs, nonprofit organizations, and education systems. My work focuses on using descriptive analytics, predictive analytics, data transformation, automation, and machine learning to improve decision-making, strengthen program performance, and support underserved communities.

I am especially interested in applying data science, machine learning, data visualization, and analytics to nonprofit impact measurement, global health, education systems, and social development.

• Experienced in monitoring, evaluation, accountability, and learning for donor-funded programs
• Background in nonprofit leadership, education data systems, and program impact measurement
• Interested in data science, analytics, automation, and machine learning for social impact
• Building a portfolio using Python, SQL, R, machine learning, dashboards, and data visualization

Programming & Data Analysis
Python, R, SQL, Pandas, NumPy, Excel

Data Visualization & Dashboards
Matplotlib, Seaborn, Plotly, Google Looker Studio, interactive dashboards, program performance dashboards

Descriptive & Predictive Analytics
Exploratory data analysis, trend analysis, performance tracking, forecasting, predictive modeling

Machine Learning
Scikit-learn, model training, model testing, model evaluation, regression modeling, classification modeling, clustering

Data Engineering & Automation
Data cleaning, data transformation, data warehousing, reproducible workflows, automation, data quality checks

Databases & Reporting
SQLite, relational databases, SQL queries, donor reporting, structured reporting systems

Monitoring & Evaluation
Logframes, indicators, MEL frameworks, data validation, program dashboards, impact reporting

A data science portfolio project focused on monitoring, learning, and evaluation for donor-funded programs. This project uses Python, SQL, and dashboard planning to track program indicators, compare targets against actual results, identify data quality issues, and support donor reporting.

Tools: Python, SQL, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Google Looker Studio

Repository: Donor-Funded Program Monitoring, Learning & Evaluation Dashboard

A data science portfolio project analyzing Rwanda education indicators to explore trends in school enrollment, completion rates, gender parity, and pupil-teacher ratios. This project uses Python, structured public development-style data, and visualization to support education monitoring, policy analysis, and evidence-based decision-making.

Tools: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, CSV Data Files

Repository: Rwanda Education Indicators Analysis

A data analytics portfolio project for organizing, cleaning, analyzing, and reporting nonprofit grant data. This project uses Python, SQL, and dashboard-style reporting to monitor grant funding, spending rates, reporting status, compliance issues, and beneficiary reach.

Tools: Python, SQL, Pandas, NumPy, Matplotlib, Seaborn, Plotly, CSV Data Files

Repository: Nonprofit Grant Reporting Data System

A predictive modeling project focused on estimating program outcomes using structured program or participant-level data.

Tools: Python, Scikit-learn, Pandas, regression modeling, classification modeling

• Data science and analytics
• Descriptive analytics and predictive analytics
• Data cleaning and data transformation
• Data warehousing and structured reporting systems
• Workflow automation and reproducible data pipelines
• Machine learning model training, testing, and evaluation
• Regression modeling and classification modeling
• Nonprofit data science and social impact analytics
• Monitoring, evaluation, and learning
• Global health analytics
• Education data systems
• Donor-funded program analytics
• Data visualization and dashboards

• GitHub: github.com/davidniyigena
• LinkedIn: https://www.linkedin.com/in/david-niyigena-5280a1179/
• Email: davidniyigena65@gmail.com