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👋 Welcome to My Professional Page

Welcome to my professional page on Statistics, Data Analysis, Data Science, and MEAL. Explore my projects, skills, and resume to see how I transform data into impact.

🌐Where Data Meets Code

Turning complex data into actionable insights isn’t just a tagline—it’s the foundation of modern decision‑making. At the intersection of statistics, analytics, and programming lies the power to transform raw information into strategies that drive measurable impact.

📊From Numbers to Narratives

Data alone is not enough. It requires critical thinking, coding expertise, and analytical rigor to uncover patterns, predict trends, and deliver solutions that matter. By blending statistical precision with machine learning and AI, I create insights that empower smarter decisions and stronger outcomes.

🚀Driving Strategic Impact

Whether it’s cleaning and analyzing datasets, building predictive models, or designing clear visualizations, my focus is on results. Every report, dashboard, and algorithm is crafted to support evidence‑based strategies that make a difference in organizations and communities.

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Comprehensive Data Science Skillset Infographic

📊The Complete Data Science Skillset

Data science is the art and science of transforming data into actionable insights. It blends technical mastery with analytical thinking, enabling professionals to uncover patterns, predict outcomes, and drive evidence-based decisions. At its foundation lie Mathematics & Statistics, powering probability, hypothesis testing, and modeling — the pillars that support all analytical reasoning.

Python and R serve as the engines of programming and statistical computing, enabling automation, data manipulation, and advanced analytics. SQL ensures efficient data management and retrieval, forming the backbone of structured data operations. Through Data Wrangling and Visualization — using tools like Power BI, Tableau, and R Shiny — raw data is refined, cleaned, and transformed into clear, interactive insights that tell compelling stories.

Machine Learning expands this foundation with techniques such as supervised and unsupervised learning, regression, classification, clustering, neural networks, and model evaluation. These methods empower predictive analytics and intelligent automation, bridging the gap between data exploration and decision-making.

Finally, Soft Skills — including communication, storytelling, collaboration, adaptability, and business acumen — ensure that insights lead to real-world impact. They transform technical results into strategic narratives that inspire action and innovation.

From Data to Insights to Impact

The journey of data science connects analytical rigor with human understanding — turning numbers into knowledge and knowledge into progress.

📑The MEAL Skillset

The MEAL framework — Monitoring, Evaluation, Accountability, and Learning — ensures that programs are effective, transparent, and adaptive. It integrates evidence-based measurement with community accountability and organizational learning.

Monitoring & Evaluation track progress through indicators, baseline surveys, and evaluation methods. Accountability builds trust via feedback systems, community engagement, and safeguarding. Learning promotes continuous improvement through knowledge management, lessons learned, and adaptive programming.

Technical skills include Data Management & Analysis (SQL, Excel, KoboCollect, ODK, Power BI, Tableau, R Shiny, GIS Mapping), alongside Qualitative & Quantitative Methods such as focus groups, interviews, surveys, sampling, and statistical analysis.

Strategic skills cover Logframes, SMART indicators, donor reporting, storytelling, stakeholder coordination, capacity building, and resilience. Finally, Soft Skills — communication, collaboration, adaptability, integrity, and critical thinking — ensure insights lead to sustainable impact.

From Data to Decisions to Impact

MEAL professionals combine Evidence, Accountability, and Learning to transform projects into lasting change.

Comprehensive MEAL Skillset Infographic
Maurice Otieno Juma

About Me

I hold a BSc in Statistics (Upper Second Class Honours) from Jomo Kenyatta University of Agriculture and Technology, which provides the foundation for my career as a Statistician, Data Analyst, and Monitoring & Evaluation (M&E) professional. Over the past 3 years, I have served with Mary’s Meals International as a Programme Quality Assurance Officer and with the NG-CDF Board as a Corporate Planning Intern, gaining hands-on experience in humanitarian programmes and government projects.

My expertise spans data science, statistical modeling, and programme quality assurance, with specialization in designing M&E frameworks, developing digital data collection tools (ODK, SurveyCTO, Kobo Collect, RESCO), and conducting both qualitative and quantitative evaluations. I am proficient in statistical programming (R, Python, SPSS, SQL, Advanced Excel) and business intelligence platforms (Power BI, Salesforce, R Shiny), enabling me to transform complex datasets into actionable insights that drive evidence-based decision-making.

I have successfully completed professional courses in Monitoring & Evaluation, Data Management & Analysis, and Business Intelligence Tools, which strengthen my ability to deliver high-quality results across diverse sectors including education, humanitarian response, development, government, healthcare, and private entities. Experienced in preparing donor reports, strengthening data integrity systems, and ensuring SOP compliance, I also bring proven ability to mentor staff, foster collaboration, and deliver strategic recommendations that improve programme outcomes. I am committed to applying my skills to advance data-driven solutions and measurable impact across organizations working to improve lives and systems globally.

Mental Health Risk

Report Summary

The Mental Health Risk Prediction Report (2026) analyzes a dataset of 25,000 records to classify risk levels (Low, Moderate, High).

  • Risk Distribution: 37.4% Low, 47.3% Moderate, 15.3% High
  • Model Performance: Random Forest (AUC 1.00, Accuracy 99%) outperforms Logistic Regression (AUC 0.90, Accuracy 75%)
  • Key Risk Drivers: Depression, anxiety, work stress, poor sleep, substance use, prior mental illness history
  • Protective Factors: Social support, physical activity, sleep hygiene, workplace stress management
  • Implications: Employers and universities should integrate stress management programs; preventive campaigns should emphasize lifestyle changes; health systems should flag individuals with family/medical history.

In short: Random Forest delivers near-perfect classification of mental health risk, while lifestyle and social support remain critical levers for prevention.

Data Projects

Analytical models and statistical reports built with Python.

Product & Credit Analysis Slides

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Education

➤Jomo Kenyatta University of Agriculture and Technology (2018–2022)
Bachelor of Science in Statistics - Second Class Honors (Upper Division)

➤Homa Bay High School (2014–2017)
Kenya Certificate of Secondary Education - Grade: B+, 70 points

Certifications

  • 📜MEAL Certification – Humanitarian Leadership Academy (2026)
  • 📜Certified Internal Auditor – IIA (2026–Present)
  • 📜Lean Six Sigma Black Belt – Alison (2025–Present)
  • 📜Safeguarding & Data Protection – Mary’s Meals Kenya

Curriculum Vitae

Explore my comprehensive curriculum vitae. It contains detailed information about my education, professional experience, skills, and certifications in Statistics, Data Analysis, Data Science, Monitoring & Evaluation and Programme Quality Assurance.

Mary’s Meals International

Programme Quality Assurance Officer (June 2024 – Nov 2025)

  • Designed and validated large datasets using SQL, Excel Power Query, and cloud-based systems, ensuring compliance with SOPs and data protection policies.
  • Supported programme operations by managing field checks, logistics, and reporting workflows.
  • Conducted weekly field checks and monthly reviews to strengthen data quality and programme efficiency.
  • Built and maintained scalable dashboards in Power BI, R Shiny, and Salesforce to monitor KPIs across 650+ schools.
  • Led advanced data analysis to identify gaps and recommend corrective actions.
  • Mentored interns on statistical workflows, dashboard creation, and reporting automation.
  • Promoted best practices in data management through capacity building and knowledge sharing.
  • Developed mobile/cloud-based monitoring tools (SurveyCTO, RESCO) for real-time data collection.
  • Delivered tasks with reliability and attention to detail while continuously learning new technical skills in Python, R, ODK, LaTeX, Microsoft Dynamics 365, and leadership.

Programme Quality Assurance Intern (Nov 2023 – May 2024)

  • Supported QA strategy by conducting field checks and database reviews to enhance data integrity.
  • Conducted weekly field checks and monthly desktop reviews to improve data quality and SOPs compliance.
  • Collaborated across teams to troubleshoot delivery inefficiencies using Salesforce and Outlook.
  • Enhanced data quality through database reviews and monitoring tool development in SurveyCTO and RESCO.
  • Promoted best practices in data management through capacity building and knowledge sharing.
  • Delivered tasks with reliability and attention to detail while continuously learning new technical skills in Python, R, SPSS, Google Docs, Google Sheets, and soft skills in interpersonal and collaboration skills.

NG-CDF Board

Corporate Planning and Strategy Intern (Sept 2021 – Jan 2022)

  • Reviewed and analyzed departmental performance contracts using Excel and Power BI to assess KPIs.
  • Evaluated development projects in 10 constituencies using SPSS and Google Sheets, ensuring timely execution.
  • Demonstrated attention to detail and reliability in supporting strategic planning tasks.
  • Delivered additional tasks with reliability and attention to detail while continuously learning new technical skills in R, SPSS, LaTeX, and Tableau, and soft skills in collaboration and teamwork.

Core Competencies and Technical Skills

  • 📊Monitoring & Evaluation Frameworks: M&E system design, survey planning, IPTT updates, donor reporting, lessons learned documentation.
  • 📝Data Collection & Analysis: Quantitative & qualitative methodologies, digital data collection tools (ODK, SurveyCTO, Kobo Collect, RESCO), data validation, cleaning, and management.
  • 🔢Statistical Analysis & Programming: Advanced proficiency in R, Python, SPSS, SQL, and Excel for statistical modeling and data-driven insights.
  • 📐Analytical Solutions: Predictive modeling, KPI development, impact assessment, and evidence-based recommendations.
  • 🗂️Research & Data Management: End-to-end data workflows including collection, collation, validation, cleaning, and database management.
  • 👥Capacity Building: Training and mentoring staff on MEL systems, strengthening organizational learning and reporting mechanisms.
  • 📊Business Intelligence & Visualization: Power BI, Salesforce, R Shiny, Databricks for interactive dashboards and executive-ready reporting.
  • ✅Data Integrity & Governance: Quality assurance frameworks, desktop reviews, SOP compliance, safeguarding, and data protection.
  • 🛠️Collaboration & Reporting Tools: SharePoint, Google Workspace, Outlook, Microsoft Word, and PowerPoint for stakeholder communication.
  • 🚀Adaptability & Continuous Learning: Quick learner with strong willingness to acquire new skills and adapt to evolving systems.

Leadership and Attributes

  • 💼 Proven ability to manage cross-functional teams and lead high-impact projects.
  • 🗣️ Strong communication skills with ability to present complex data to non-technical stakeholders.
  • ⚖️ Demonstrated integrity, accountability, and high vocational attitude towards work.
  • 📈 Quick learner with strong willingness to acquire new skills and adapt to evolving systems.

Code Samples

📈 R Programming Projects

Regression & classification analysis.

Behavioral prediction model.

🐍 Python Projects

Classification using Python.

🗄️ SQL Projects

SQL queries for KPI tracking and reporting.

Data integrity and validation scripts.