Data Analysis for Economists
Get ahead in economics with the Data Analysis for Economists Micro-Credential. Learn to work with modern statistical methods and use R to solve real-world problems. Combine theory with practice and gain skills that will boost your value on the job market.
27 000 CZK
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29. 9. - 21. 12. 2026
Part-TimeForm of study27 000 CZK incl. VATPriceKolbenova 942/38a, Praha 9Adress
About the course
This Micro-Credential consists of a combination of two subjects: Applied Optimization, which introduces optimization methods and their use in operational research, and Econometric Models, which focuses on quantitative methods commonly used in economic analysis and modeling. The course requires knowledge of statistical theory at the level of the Statistical methods in data analysis.
18 ECTSTotal ECTS creditsBeginnerLevelCZLanguage
Course outline
1. Module: Applied Optimisation
Optimisation problemsFormulation of optimisation problems.
OptimisationContinuous vs. discrete optimisation, linear vs. nonlinear problems, multi-criteria problems.
Use casesProblems in transport and logistics, various variants of the TSP.
Process managementScheduling and process control, job scheduling.
Applications in financial analysisFinancial applications, portfolio management.
DEADEA and efficiency evaluation.
Data miningApplications in classification, data analysis, and data mining.
Basic optimisation algorithmsOverview of algorithms for linear, nonlinear and discrete programming.
Heuristic methodsHeuristic and metaheuristic methods for complex optimisation problems.
ModellingModelling languages, solvers, numerical problems, implementation issues.
Computational complexity in optimisationComputational complexity of optimisation problems.
2. Module: Economic Models
Work in R – specific tasksProgramming and visualisation tools in R for econometric applications. Basic data handling, data structures, import, export and transformation of data. Specific data issues (missing, outlier and contaminated observations).
Linear regressionThe classical linear regression model. Model formulation, cross-sectional and dynamic models, special regressors (e.g. dummy or trend variables). Estimators and their properties – finite sample properties and asymptotic properties. OLS, GLS, MLE, robust estimators. Testing and consequences of heteroskedasticity and autocorrelation. Measuring multicollinearity. Applications in macroeconomic and microeconomic contexts.
Discrete and limited dependent variablesModels with special dependent variables (e.g. linear probability model, censored and truncated regression models, logit, probit and tobit models). ML estimators. Specification issues. Applications in scoring models.
Multiequation models and panel dataGeneral formulation of systems. SUR system. Models for panel data. Simultaneous equation systems. Identification and estimators.
Time seriesUnivariate time series: decomposition methods (seasonality, trends). Box–Jenkins methodology. Stationarity. Applications in portfolio theory. Multivariate time series: generalisation of univariate methods. Vector autoregression. Granger causality. Cointegration and error correction models.
Technical Requirements
The course requires knowledge of statistical theory at the level of the
Statistical Methods in Data Analysis course.
Statistical Methods in Data Analysis course.
Your own laptop or tablet
To comfortably work on your case study and access course materials.
Stable internet connection
The course is partially online, so a reliable connection – and ideally a quiet space for consultations – is recommended.
That’s all. We’ll provide the rest.
Who is this course for?
Deepen your knowledge in applied optimization and econometric models. Learn to use modern statistical methods and R for effective data analysis and to support informed decision-making.
Managers and senior professionalsIt provides the ability to understand data analyses and apply them in planning, investment decisions, and strategic management. The course will help you turn data into actionable insights for your professional practice.
IT specialists and developersThe course opens the door to the world of economic analysis and modeling. You’ll learn how to use R and statistical methods for data work, gaining a competitive edge in developing data-driven solutions.
Students and graduatesIf you’re looking to connect theoretical knowledge with practical experience, this course is ideal for you. You’ll master modern analytical tools and enhance your appeal to future employers.
Teaching methods
The course combines self-study with guided practice, enabling participants to master key concepts and apply them through hands-on experience.
- Self-study with clearly structured materials
- Tutorials and individual consultations with the instructor
- Assessed assignment – your own project proposal
- Final online test and in-person workshop
Who will you learn from?
prof. Ing. Jan Čadil, Ph.D.
He graduated from the University of Economics, Prague. He completed his PhD in 2006, received his habilitation in 2012, and was appointed professor of economics in 2019. His work primarily focuses on applied statistics in connection with economics and economic modeling. Among the domains he specializes in, he highlights economic growth, and labor market issues including education, and impact evaluation.
He actively publishes and conducts research in these areas. He serves on several advisory groups for higher education (e.g., for digitalization, for professional programs), and is also the vice-chairman of chamber B of the Czech Rectors Conference. He has been involved with Unicorn University since its founding in 2007 (then Unicorn College), and has been its rector since 2015
He actively publishes and conducts research in these areas. He serves on several advisory groups for higher education (e.g., for digitalization, for professional programs), and is also the vice-chairman of chamber B of the Czech Rectors Conference. He has been involved with Unicorn University since its founding in 2007 (then Unicorn College), and has been its rector since 2015
