This course (Research Design and Policy Analysis, MEDS 5124) provides an introduction to research methods, research design, and applied policy research. It equips students with practical skills to formulate research questions, critically assess evidence, design qualitative and quantitative studies, develop questionnaires and other research instruments, and prepare sound research proposals. The course also introduces key approaches to policy analysis. Students will learn how to analyse policy issues, assess and criticize scholarly materials, and develop policy papers and research proposal using clear and effective writing and storytelling techniques.

The course balances methodological foundations with practical application, with particular attention to public policy issues in Kyrgyzstan and Central Asia. Examples will draw on topics such as governance, natural resources, labour migration, digitalisation, and public sector reform. Through practical exercises and assignments, students will develop research and analytical skills relevant to careers in government, think tanks, NGOs, international organisations, and academia. The course also serves as the foundation of the master’s thesis pathway: the research proposal developed in this course is designed so that it can be carried forward, with the student’s thesis supervisor, into the thesis proposal in later semesters.

·         The course is taught by two instructors: Dr. Bakhytzhan Kurmanov (BK) and Dr. Kanat Tilekeyev (KT).

Mathematical Foundations for Data Analysis (MEDS-5101) develops the mathematical tools needed for graduate study in economics and data science. The course covers linear algebra, differential and integral calculus, unconstrained and constrained optimization, numerical methods, and discrete-time economic dynamics.

Economic applications provide the primary context, with selected connections to data science and machine learning. Students combine analytical reasoning with Python-supported computation, visualization, and simulation to formulate, solve, and interpret quantitative problems.

The course prepares students for advanced work in economics, econometrics, data analysis, and machine learning.

Instructor: Dr. Nurbek Madmarov
Programme: MSc in Economics and Data Science, UCA

This course equips students with the tools to interpret, evaluate and apply data in real-world policy and economic settings. The first half of the course builds a solid foundation in probability and statistical inference, tested at the midterm; the second half puts that foundation to work through simple and multiple regression, culminating in the judgment to assess whether a regression-based study is credible. Emphasis is placed on developing a critical understanding of statistical findings and effectively communicating results in policy-relevant contexts.

This course examines how individuals, firms, workers, and governments make decisions under scarcity and how these decisions interact within markets and institutions. The course combines theoretical models, real-world examples, and policy discussions, helping students apply microeconomic reasoning to contemporary Central Asian issues such as labor markets, market power, inequality, environmental problems, and risk. 

This course will be focused on urban health issues and suggest methodologies for building resilient and healthy urban community