This is an intensive seven-week annual short course in Epidemiology, Biostatistics, and Qualitative Research (EBQ), organized by Mountains of the Moon University (Uganda) in collaboration with the University of Gent (Belgium). The course aims to equip health professionals and researchers with practical knowledge and skills in epidemiological research, statistical methods, and qualitative research. The programme includes hands-on experience with Python programming (including Machine Learning/AI) and NVivo software for data analysis. Participants are encouraged to work with their own datasets under expert guidance. The course will be delivered in a blended format (60% online, 40% physical) at the Mountains of the Moon University campus in Fort Portal, Uganda, from September 7th to October 23rd, 2026. A Certificate of Attendance will be awarded upon successful completion.
To be eligible for the course, applicants must meet the following requirements:
Professional Background: Researchers and students from relevant disciplines. A health training background is required.
Academic Knowledge: Background knowledge in Epidemiology, Biostatistics, and Qualitative research methods is essential, along with basic knowledge of statistics.
Technical Skills: Basic knowledge of Microsoft Office is required.
Equipment: Applicants must have access to a personal laptop.
Application: To apply, send an expression of interest, your CV, and academic documents to ikiriza.antony@mmu.ac.ug by midnight EAT on 31st July 2026. Only 40 participants will be accepted to ensure a personalized learning experience. All applicants will be informed of the selection committee’s decision within 15 days of the deadline.
The curriculum is divided into theoretical and practical modules, covering the following major areas:
Quantitative Epidemiological Study Design: Covers principles of various study designs (cross-sectional, case-control, cohort, RCTs, etc.), measures of disease occurrence and effect, and how to select the most appropriate design for a research question.
Statistical Methods in Epidemiology/Biostatistics: Focuses on the application of statistical techniques, progressing from basic tests to advanced methods. Topics include parametric/non-parametric tests, confounding/effect modification, survival analysis, and regression techniques (Linear, Logistic, Poisson, Cox, and Multilevel Mixed-Effects models).
Qualitative Study Designs: Explores various qualitative research designs and data collection methods (interviews, FGDs, observations). It covers different analysis techniques (content, thematic, grounded theory) and reporting principles using COREQ guidelines.
Practical Quantitative Data Analysis (Python with ML/AI): Hands-on sessions using Python. Includes data science, Python programming, machine learning (supervised, unsupervised, etc.), and deep learning/neural networks using frameworks like TensorFlow and Keras for health data analysis and classification.
Practical Qualitative Data Analysis (NVivo): Hands-on training on using NVivo software for qualitative data analysis, covering project management, coding, data management, visualization, and reporting.
The course employs a blended learning approach:
Format: 60% of the course is delivered online, and 40% is delivered in person (physical attendance).
Location: The physical sessions will be held at the Mountains of the Moon University, Lake Saaka Campus, Fort Portal, Uganda.
Duration: The course runs for seven weeks, from September 7th to October 23rd, 2026.
Teaching Methods: The course combines theoretical instruction with intensive practical sessions. Participants will have the opportunity to work on their own datasets and receive guidance from experienced researchers.
Materials: Participants will receive soft copies of reference materials and other didactic resources (PowerPoint presentations, articles, etc.).
Upon completion of this course, participants will be able to:
Design and Implement Research: Select and apply the most appropriate epidemiological study designs for various research contexts and questions.
Perform Advanced Data Analysis: Confidently analyze both quantitative and qualitative health data using modern tools like Python (with Machine Learning/AI) and NVivo.
Utilize Advanced Statistical Methods: Apply a wide range of statistical techniques, from basic tests to complex regression models, to interpret and present research findings effectively.
Enhance Research Profiles: This certificate course is ideal for researchers, students, and health professionals looking to strengthen their research capabilities for academic advancement, career progression in public health, or to lead data-driven health projects in various settings (e.g., academia, non-profits, government health departments).
