Master of Science in Data Science and Analytics

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11951/1204

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    TACKLING DROPOUT RATES OF STUDENTS IN UGANDA: AN EXPLORATION OF MACHINE LEARNING AND DATA-DRIVEN APPROACHES
    (Uganda Christian University, 2025-10-02) NAKIMBUGWE DIANA KIRABO
    Uganda’s education sector faced notable challenges, including high dropout rates and poor student outcomes. This study analysed the potential of machine learning and data to transform education in Uganda. According to Eccles and Roeser (2015), education was essential for both social and individual progress. The literature review revealed that 45% of primary school children and 30% of secondary school children withdrew before completing their education. To address this issue, we employed a machine learning algorithm(random-forest) to predict student dropout rates and identify at-risk students. Our review highlighted opportunities and challenges of leveraging technology to revolutionize education in Uganda. This paper proposed a framework for exploiting machine learning and data to address these issues, including data collection, model development, and stakeholder commitment. By implementing this framework, Uganda’s education sector could improve student outcomes by 30%, reduce dropout rates by 25%, and increase teacher training and resource allocation. In other words, this study outlined the problem of high dropout rates, described what was done to address it through machine learning, presented what was found regarding contributing factors, and highlighted the relevance of these findings for improving educational outcomes in Uganda. This review integrated insights from over 30 sources, providing a foundation for future research and application in this critical area, with implications for policy and practice. Keywords Primary Keywords: Machine learning, Data analytics, Educational outcomes, Dropout rates, Student performance. Secondary Keywords: Predictive modeling, Random Forest algorithm, Data-driven decision-making, Educational data mining, Learning analytics,Ugandan education, East Africa, Educational technology and Descriptors (MeSH Terms): Education, Machine learning, Data analytics, Student dropouts, Academic achievement, Educational measurement, Educational technology, and Africa.
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    Forecasting Emerging Skill Demands with Machine Learning to Inform Curriculum Development in Uganda’s Higher Education
    (Uganda Christian University, 2025-09-24) Wanyama Denis
    Rapid technological advancement and evolving industry demands have widened the skill gap in Uganda’s labor market. Higher education institutions often struggle to keep pace with these changes, leading to mismatches between graduate competencies and employer expectations. This study uses machine learning techniques to forecast emerging skill demands and inform the development of data-driven curricula in Ugandan universities. Drawing on more than one million job postings from 2021 to 2023, the research applies natural language processing (NLP), time series forecasting (ARIMA and Holt-Winters), and clustering algorithms to analyze labor market trends. Exploratory Data Analysis (EDA) revealed high-demand skills, while Holt-Winters outperformed ARIMA (MAE: 9.05 vs. 23.87), capturing the seasonal nature of skill fluctuations. Key findings indicate a growing demand for roles such as interaction designers, network administrators, user experience professionals, and social media managers. In-demand technical skills include Python, Google Analytics, CSS, Tableau, AWS, and Sketch. The increasing emphasis on digital literacy and soft skills underscores the need for more flexible and adaptive curricula. This study offers actionable recommendations for curriculum reform, including integrating technical skills, developing continuous learning pathways, and enhancing academic-industry collaboration. By applying machine learning to labor market analysis, the research equips universities, policymakers, and stakeholders with the information needed to align higher education with the demands of Uganda’s evolving digital economy. Keywords: Machine Learning, Labor Market Trends, Skill Forecasting, Higher Education Curriculum, Uganda, ARIMA, Holt-Winters, Data Science, skill mismatch.
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    CONTEXTUALIZING AI ETHICS IN UGANDA’S MICROCREDIT WITH ADAPTIVE SENSITIVE REWEIGHTING
    (Uganda Christian University, 2025-08-12) Isabirye Emmanuel
    This research tackles the pressing ethical concerns of using Artificial Intelligence (AI) in Uganda’s microcredit sector, namely to develop an Adaptive Sensitive Reweighting (ASR) model to mitigate algorithmic bias and promote equitable access to credit. Traditional credit scoring models - and AI algorithms trained on Western-biased data - discriminate against marginalized groups because they are based on formal financial records, reinforcing structural disadvantages. By iterative engagement with Ugandan policymakers, lenders, borrowers, and AI experts, we identify the most significant ethical concerns and specify context-specific fairness metrics. The ASR approach adaptively adjusts weights for sensitive features like collateral values and transaction history during model training to enhance fairness. Experimental outcomes on a typical credit scoring dataset demonstrate ASR’s success: the inclusion rate of disadvantaged borrowers is enhanced by 15% with predictive accuracy maintained, and significant improvements on key fairness metrics. The research provides actionable policy recommendations on implementing ASR-based AI systems in Uganda’s microfinance sector to drive financial inclusion and sustainable development. This study contributes to emerging Majority World scholarship on AI ethics by demonstrating the necessity of situating ethical frameworks and valuing stakeholder perspectives to develop equitable, inclusive AI systems. Our findings offer valuable insights for policymakers, microfinance institutions, and AI practitioners who aim to implement responsible AI in Uganda’s Microcredit sector.