Portfolio
Our organization is distinguished by a team of highly qualified professionals dedicated to developing innovative solutions that have been adopted by thousands of students, educators, and industry practitioners.
Delivering transformative STEM solutions across education and industry sectors
Real-world impact through innovative, client-centered projects
Applying trusted technologies and frameworks to solve problems in STEM
Bridging theory and practice through data-driven, scalable implementations
Through continuous research and development, we remain committed to setting new standards of excellence in STEM education and technological innovation.
Step-by-Step Mathematics Web App
PythonFastAPITensorFlowSQLAWS
A next-generation pedagogical learning platform, designed to revolutionize STEM education. This cutting-edge solution empowers participants by breaking down complex mathematical and scientific problems into clear, structured, and manageable steps. It aims to enhance understanding through interactive, step-by-step solutions, rich audio explanations, and dynamic visualizations—creating an engaging and immersive learning experience for students and educators alike.

Python Library for Numerical Analysis
PythonNumpyPandasMatplotlibTensorFlow
A high-performance Python library for advanced mathematical and statistical computing in STEM. It offers optimized implementations of algorithms, designed to enhance both teaching and learning experiences for students and educators.

Remark: This is the library that powers the Stemlab API - so the links given below are similar to the preceding portfolio one.
check R Library
Rdplyrtidyrggplot2gtsummary
The check library offers tools for validating user input and ensuring data integrity in R. It enables developers to verify input data types and enforce custom conditions with clear and informative error messages, making the debugging process reliable in addition to improving user experience in R packages and applications.

stemlab R Library
Rdplyrtidyrggplot2gtsummary
A comprehensive R library designed for numerical analysis and computing in Science, Technology, Engineering and Mathematics (STEM). It provides tools for solving equations, performing simulations, analyzing data, and visualizing results, empowering researchers and students in STEM.

dta R Library
Rdplyrtidyrggplot2gtsummary
The check provides simple yet powerful tools for data wrangling. It includes functions for cleaning, transforming, and formatting datasets using widely adopted data-handling methods. Additionally, it supports basic exploratory data analysis (EDA) tasks to help users gain initial insights from their data.

Stata Modules / User Functions
Stata 19
We developed an extensive suite of custom Stata programs focused on data management and cleaning, tailored to meet the needs of researchers, analysts, and institutions working with complex and messy datasets. These programs automate essential tasks such as variable labeling, missing data handling, recoding, consistency checks, and preparation of datasets for analysis or reporting.

CIE Exam Questions and Solutions
PythonFastAPITensorFlowSQLAWS
CIE Exam Questions and Solutions is a dynamic, database-powered platform designed to help students and educators access, generate, and explore questions. This platform features a comprehensive collection of past CIE exam questions from 2010 to 2024, making it an ideal resource for both revision and instruction.

Other Projects Completed for Clients
PythonRR ShinyStataExcel
Over the past 14+ years, STEM Research has had the privilege of working with a diverse range of clients, delivering tailored solutions that meet their unique needs and objectives. Below are a few highlights of our work, demonstrating our ability to drive results across different industries and challenges.
Python module with interactive visualizations for Difference-in-Difference (DID) for a monitoring and evaluation NGO.
An end-to-end data wrangling system using Stata with essential and common AI-assisted cleaning tasks.
A real-time statistical process control platform for a national standards agency, combining R with R Shiny interactive power.
Created a simple Excel app for a research organization to simulate optimal sample sizes across stratified cohorts.
Specialized Python module that streamlines the creation, management, and analysis of Monitoring and Evaluation (M&E) logframes for development and research projects.
Among many others