Certificate Program in Data Science and Economics (DSE)


Graduates of this certificate program will receive advanced training on statistical and digital tools, to interpret and analyze various economic phenomena, extract meaningful relationships and recurring patterns, build predictive and forecasting models for companies and public institutions. The certificate program will provide skills to analyze the effects of development projects and develop data-based economic policy making, or to analyze and elaborate forecasts on large data flows and evaluate any activities related to the sectors of economy, finance and business.

The certificate program will use the interdisciplinary approach to teaching and aim to provide a solid and modern background in computer science, statistics and economics, providing an integrated view of these skills in all its main courses. The curriculum is designed to construct a solid theoretical base through the general economic courses, and then to complement it with providing an extensive training in statistics and empirical economics, and new data management technologies.



According to LinkedIn’s latest Emerging Jobs Report, data science, artificial intelligence, and robotics engineering have been the top three job trends for last few years. Demand for data scientists increased by 37% in 2019 and is continuing to grow significantly across all industries. Some of this growth is attributed to the evolution of previously existing jobs, like Statisticians and Economists, and increased emphasis on data in academic research.

Data and information have become the key resources in a wide range of industries. The analysis of data is a guiding criterion in strategic choices and in the evaluation of the effectiveness of economic decisions. Ability to work with data is an increasingly important skill to all organizations, at all levels.

Even though the demand for data scientists is rapidly growing, very few universities in the region and no university in Kyrgyzstan are preparing modern economists that specialize in data analytics. The new certificate program in Data Science and Economics (DSE) aims to respond to the training needs of data scientists in the economic field by providing the skills necessary to understand and analyze economic data through modern data management techniques, data mining and visualization, and econometric modelling skills.


Student learning outcomes: expected knowledge, skills, competences for certificate program graduates

Certificate program is designed to advance knowledge of students from the general Economics Program in data analysis. It provides students with skills associated with advanced statistical and econometric analysis, and helps students apply their skills in economic reasoning. The certificate program curriculum is designed to equip Economics students with a foundation in programming, data mining, visualization, and building data management systems.

Student learning outcomes:

  • Upon completion, the candidates should be able to employ techniques of data analysis to inform economic strategy in business and public policy.
  • Be able to contribute to innovation and science-based decision-making in private and public sectors.
  • Complete a number of team and independent projects related to data analysis, data visualization, econometric modeling and applied research.


  • Understand the role of data science for sustainable development, show insight into ethical requirements of data collection and analysis, and be able to analyze economic problems using quantitative data.
  • Know basic concepts in mathematics, statistics and econometrics, and computer technology as a foundation for understanding data science methods and applications.


  • Be able to develop overall solutions to economics related problems, including creating solutions in a multidisciplinary context. Be able to evaluate tools for analyzing, visualizing, modeling independently and critically.
  • Collect and organize data, execute a multivariate analysis of high-dimensional data, pattern recognition, evaluate the quality of data and interpret the results.
  • Apply economic models to real-life problems using rich data.
  • Apply computer programming and computing software to analysis of data from economic perspective.


Courses (with brief summary), requirements:

  1. Data Visualization (3 ECTS)

The course is designed for undergraduate students with no prior experience in data visualization. Students will be introduced to fundamental concepts of data visualization. They will create different types of charts and examine when to use each one to explain their data. Students will explore the most appropriate visual representation given different types of data. By the end of the course students will be able to generate effective visualizations that will help people make decisions and take action based on data.

Prerequisites: None

  1. Data Analysis in Economics (3 ECTS)

This course introduces undergraduate students to statistical methods of data analysis in economics. Based on existing data sets, students will develop research questions, describe the variables and their relationships and calculate sample statistics. They will learn a variety of statistical tests and examine how to apply methods appropriate for a specific data type and question. By the end of the course students will be able to use data analysis tools to visualize and analyze data, present the results of their analysis and create predictions and make data driven decisions.

Prerequisites: Data Visualization course

  1. 3. Intro to Econometrics (6 ECTS) counts toward Economics major

This course is an undergraduate level introduction to econometrics, in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena. By the end of this course, students should be able to: understand the nature and scope of econometrics as a social science; use statistical analysis, including the classical regression model, to estimate relevant economic parameters, predict economic outcomes, and test economic hypotheses using quantitative data; understand the basic assumptions of the classical linear regression model; develop and maintain a working knowledge of econometrics that will provide a basic foundation for future study in econometrics and statistical techniques.

Prerequisites: Students are expected to have completed Statistics course (MAT 307).

  1. Applied Econometrics (6 ECTS) counts toward Economics major

The objective of the course is to equip students with econometric tools necessary to conduct their own research. The most widely used techniques in empirical analysis are studied. As the name of the course suggests, the emphasis will be put on application and practice with no detailed treatment of theoretical derivations. The course features weekly lectures covering theory and seminars covering practical exercises in R.

Prerequisites: Students are expected to have completed Introduction to Econometrics course (ECO 320.1) and Intro to Programming with R course.

  1. 5. Intro to Programming with R (6 ECTS)

This course is aimed at introducing programming and computational tools useful for future careers as data scientists. In the course, students will set up their own R programming environment; learn how to write, execute and modify R code and R scripts; load data sets into R, create effective numerical and graphical summary statistics, and see how to use R to perform some common statistical analyses; use programming techniques such as loops, conditionals and functions, to effectively solve practical and analytical issues that data scientists encounter when working with data.

Prerequisites: Students are expected to have completed Statistics course (MAT 307).

  1. Big Data (6 ECTS)

This course introduces to students the core concepts behind big data problems, applications, and systems. It provides necessary skills to use of the most common frameworks, Hadoop, along with R. At the end of this course, students will be able to: describe the Big Data landscape including examples of real world big data with some focus on economic problems; learn factors that affect data collection, monitoring, storage, analysis and reporting; structure analysis using Big Data.

Prerequisites: Students are expected to have completed Intro to Programming with R course

  1. Honors Seminar I and II for Data Scientists (12)

The main purpose of these courses is to equip students with skills for conducting economic research as data scientists and assist them in the process of working on senior thesis projects. Specifically, students will be guided along the process of researching the literature on a specific topic, posing a well-defined research question, and answering it using data in a form of research paper.

Prerequisites: Research Methods and Applied Econometrics courses

  1. (Optional) Online courses from MITx MicroMasters Program

Economics Department has been collaborating with MITx MicroMasters Program to provide data science related courses for Master’s students. AUCA recognizes the program’s credential as a part of collaboration agreement. The most successful senior students from DSE certificate program will also have an opportunity to take some of the online courses offered by MITx MicroMasters Program for credits.