MSc in Economics and Data Science

Develop the statistical foundations needed to interpret data and evaluate evidence in economics and public policy. Using real-world datasets and statistical software, students learn to analyse uncertainty, test hypotheses, estimate relationships, and communicate statistical findings clearly. 

Explore how individuals, firms, workers, and governments make decisions—and how markets, institutions, incentives, and power shape economic outcomes. Students apply microeconomic models and real-world evidence to contemporary policy questions, including labour markets, inequality, market power, environmental challenges, and risk.

Build practical programming and data-analysis skills using Python. Students learn to clean, manage, analyse, and visualise real-world data and develop reproducible analytical workflows through hands-on assignments and collaborative projects. 

Learn how to transform a policy problem into a rigorous and relevant research project. Drawing on issues affecting Kyrgyzstan and wider Central Asia, students develop research questions, review evidence, select appropriate methods, and communicate their findings through policy briefs, research proposals, and presentations.

Build the mathematical foundations needed for advanced study in economics, econometrics, and data science. Designed for students from different academic backgrounds, the course develops practical understanding of linear algebra, multivariable calculus, optimisation, numerical methods, and dynamic programming, preparing students to interpret and critically evaluate quantitative research. 

Use econometric methods to investigate complex economic and policy questions with real-world data. Students learn to build and evaluate regression models, identify common modelling problems, interpret relationships and causal effects carefully, and communicate empirical findings to both technical and policy audiences. 

Learn how data is collected, stored, transformed, and prepared for analysis. Students work with databases, SQL, APIs, and data pipelines to build reliable end-to-end workflows that support economic analysis and evidence-based decision-making.

Examine how economies perform and respond to policy decisions at the national and international levels. Students apply macroeconomic theory and evidence to questions involving economic growth, inflation, unemployment, monetary and fiscal policy, exchange rates, financial stability, and the particular challenges facing emerging and developing economies. 

Learn how machine-learning methods can be used to identify patterns, generate predictions, and support policy analysis. Working with real-world data in Python, students develop and evaluate predictive models while examining their interpretability, limitations, ethical implications, and appropriate use in public decision-making. 

Students select one of two thematic electives in Semester 2, providing a foundation for related advanced study in Semester 3. They are encouraged to continue within their chosen thematic area when selecting their Semester 3 electives. 

Natural Resource and Environmental Economics 

Apply economic theory and empirical methods to environmental and natural-resource challenges. Through regional examples and work with environmental data, students examine pollution control, environmental valuation, resource management, climate change, energy transition, and water governance, while considering questions of efficiency, sustainability, and equitable distribution. 

Public Finance

Examine how governments raise revenue, allocate public resources, and manage fiscal policy. With a strong focus on the Kyrgyz Republic and Central Asia, students analyse taxation, public expenditure, budgeting, fiscal sustainability, and intergovernmental finance, including challenges associated with informality, commodity dependence, climate vulnerability, and institutional capacity. 

Learn to design, conduct, commission, and use evaluations of public policies and programmes. Covering the full evaluation life cycle, students develop theories of change and evaluation questions, select appropriate designs, apply impact, process, and economic evaluation methods, synthesise evidence, and consider the ethical, political, and institutional realities of evaluation practice, with particular attention to Central Asia. 

Examine the ethical and governance challenges associated with using data and artificial intelligence in public policy and development. Through applied case studies, students assess issues of data governance, algorithmic fairness and bias, transparency, accountability, and responsible AI, and develop context-appropriate approaches for Kyrgyzstan and Central Asia. 

Students select two, 6 ECTS advanced electives in Semester 3. The anticipated elective portfolio includes the following courses: 

• Climate Policy and Sustainable Development 
• GIS and Spatial Data for Policy 
• Development Finance and Economic Forecasting 
• Health, Labor, and Social Policy Economics 

Begin an independent research project addressing an economic or public-policy question. With guidance from a thesis supervisor, students define their research question, review the relevant literature, develop and present a research proposal, and confirm the data and methods needed for the study. 

Complete the research and analysis begun in Semester 3 and develop it into a full, defence-ready thesis. Working independently under academic supervision, students refine their findings, formulate evidence-based recommendations, revise the thesis, and prepare to present their research to academic and policy audiences. 

Develop practical skills in designing and delivering effective, student-centred teaching in economics or data analysis. Through pedagogical training, an Instructional Skills Workshop, classroom observation, and supervised teaching practice, students plan and deliver lessons to a real audience, which may include undergraduate students, continuing-education participants, or peers.

Present and defend an original, policy-relevant research project before the State Attestation Commission. The thesis demonstrates the student’s ability to formulate a meaningful research question, apply appropriate economic and data-analytical methods, interpret findings critically, and communicate clear, evidence-based conclusions and recommendations.

Anwar Naseem is Programme Director of the MSc in Economics and Data Science and Associate Professor of Economics at UCA. He has more than 20 years of research and teaching experience, including work at Rutgers University, McGill University, and IFPRI. His research covers agricultural development, innovation policy, market structures, and technological change. He holds a PhD in Agricultural Economics from Michigan State University. 

Salima Bekbolotova is an economist with extensive experience in university teaching and applied research. Her expertise includes data analysis, development economics, agricultural economics, food security, regional trade, and public policy. She has undertaken doctoral and postdoctoral research in Germany and completed postdoctoral fellowships in France and, as a Fulbright Fellow, in USA. Her teaching connects economic theory with contemporary policy questions and the development challenges facing Central Asia. She holds a Candidate of Economic Sciences degree in Economic Theory and is completing a PhD in Agricultural Economics in Germany.  

Ablay Dosmaganbetov is an Economist and Public Policy expert specializing such fields as energy economics, extractive industries and sustainable development. His research expertise includes such areas such quantitative research methods and data analysis. His teaching and research focus on using applied empirical methods on real-world data to better and deeper understand both economic and social challenges, especially in Central Asian context. He holds a PhD in Public Policy from Nazarbayev University and brings experience in academic research, applied analysis, and evidence-based policy making.  

Begaiym Emileva (PhD, Leib

Begaiym Emileva is a Postdoctoral Researcher within the SDGnexus Network, a joint initiative of UCA and Justus Liebig University Giessen. Her research expertise includes climate risk perception and adaptation, agricultural economics, dairy value chains, dietary diversity, and nutrition. She holds a PhD in Agricultural Economics from the Leibniz Institute of Agricultural Development in Transition Economies (IAMO) and Martin Luther University Halle-Wittenberg, Germany. Her teaching focuses on microeconomics and combines theoretical models, real-world examples, and policy discussions to help students apply microeconomic reasoning to contemporary economic and social challenges.  

Zalina Enikeeva is a Research Fellow at UCA’s Institute of Public Policy and Administration, with extensive experience in economic and policy research across Central Asia. Her work covers international trade, regional integration, food systems, entrepreneurship, digital transformation, and socioeconomic development. She has contributed to research for organisations including FAO, UNESCAP, UNCTAD, UNESCO, and ADB. She holds an MA in Economic Governance and Development from the OSCE Academy in Bishkek. Zalina Enikeeva is a 2024 Visiting Research Fellow at the Central Asia Regional Economic Cooperation (CAREC) Institute, Urumqi, PRC.   

Azmat Hussain is an Assistant Professor of Mathematics at UCA’s School of Arts and Sciences. His research focuses on stochastic control theory, optimisation, and financial risk management. Before joining UCA, he taught at Lahore University of Management Sciences and Karakoram International University in Pakistan. He holds a PhD in Operations Research from North Carolina State University, where he studied as a Fulbright Scholar. 

Bakhytzhan Kurmanov is a public policy scholar whose research examines governance, institutions, digital activism, authoritarian politics, and public sector reform, with a particular focus on Central Asia. His work also explores policy analysis, research design, and qualitative methods. He has extensive experience conducting applied research for international organizations, including the United Nations Development Programme (UNDP) and the Asian Development Bank (ADB), as well as for Kazakhstani think tanks, examining a wide range of public policy challenges across the region. Bakhytzhan holds a PhD from Nazarbayev University, a Master of Public Administration (MPA) from the Australian National University, and a BA from York University. Before joining the University of Central Asia, Dr. Kurmanov taught extensively at Maqsut Narikbayev University and served as a Graduate Teaching Assistant at Nazarbayev University.  

Kanat Tilekeyev is Associate Director and Senior Research Fellow at UCA’s Institute of Public Policy and Administration. His research focuses on agricultural and regional development, public finance, labour markets, migration, poverty, and the economies of Central Asia. He brings extensive experience in applied economic research and policy analysis, including collaboration with international development organisations and work addressing the socioeconomic challenges facing mountain communities. He holds a PhD in Economics degree from Justus Liebig University Giessen, Germany.  

Dr Kemel Toktomushev is a Senior Research Fellow at UCA’s Institute of Public Policy and Administration. His research examines governance, political economy, development, digital transformation, and the societal implications of emerging technologies in Central Asia. He holds a PhD from the University of Exeter and an MSc from the London School of Economics and has completed executive education at Harvard University.  

Professor Georgy Safonov is a Professorial Research Fellow at UCA’s Institute of Public Policy and Administration. His research and teaching focus on climate-change economics, sustainable development, green innovation, and energy and industrial transitions. He has held research and academic appointments in France, Finland, and Russia and contributed to numerous peer-reviewed publications and international assessments. He holds a PhD in Economics from Lomonosov Moscow State University.  

Dmytro Zubov is a computer scientist with extensive international experience in university teaching, research, and technology development. His expertise spans programming, data analysis, databases, artificial intelligence, software engineering, and complex information systems. Through applied teaching and project-based learning, he helps students develop the technical skills needed to manage data and build reliable, reproducible analytical workflows. He holds doctoral qualifications in engineering from Donetsk National Technical University and Kryvyi Rih Technical University, as well as a diploma in Systems Engineering from Volodymyr Dahl East Ukrainian National University. 

Submit your application by the priority deadline to be considered in the first round of admissions and scholarship decisions.

Provide your personal, academic, and professional information.  

Submit your degree certificate, academic transcript, CV, statement of purpose, identification document, and proof of English proficiency, if applicable.

UCA will contact your referees directly to request confidential recommendations. 

Applicants who cannot otherwise demonstrate the required English-language proficiency may register for the UCA-administered test. Instructions, dates, and the applicable fee will be provided in advance. 

Shortlisted applicants may be invited to discuss their academic preparation, motivation, and readiness for the programme.  

Successful applicants will receive an offer outlining any conditions of admission, available financial support, and the deadline for accepting their place.