Statistical Data Science

Upon completing this program, students can apply their knowledge in various scientific fields focused on data collection and analysis. These fields include medicine (clinical and medical research, epidemiology, disease diagnostics), geography, ecology, and hydrology (climate change, weather forecasting, analysis of extreme events such as floods), as well as economics and finance (econometric analysis, risk management, and portfolio theory in financial markets).

It is a new two-year follow-up master's program that will partially build upon and innovate specific courses from the master's program in Applied Mathematics and incorporate courses from the Faculty of Informatics.

After successfully completing his/her studies the graduate is able to:

• explain and apply advanced concepts of statistical inference, estimation and testing techniques, apply probability theory to understand the methodology;
• recognize common violations of classical assumptions, assess the impact of assumption violations, apply diagnostic tests and techniques, use alternative modelling approaches;
• understand the concept of flexible data modelling, apply and adapt non-linear and machine learning methods for flexible data modelling, evaluate model performance, identify and mitigate overfitting;
• understand the principles of predictive modelling and applications in decision-making, construct predictive models, train, validate and test the models, evaluate model performance, incorporate uncertainty and risk, optimize the models;
• understand the characteristics and representation of various types of complex data, including space-time data and sequential data, preprocess and clean such data, implement specific statistical and machine learning models;
• differentiate between various model architectures, choose and justify suitable statistical techniques for various datasets, apply systematic approaches for model selection, identify and explain the model components;
• understand key components of reports and principles of effective data visualization, create high-quality visualizations, summarize and interpret statistical results of a model, organize reports logically.

The expected areas of employment for graduates include:

- research and evaluation of the impact of vaccines and medications,

- analysis and planning of environmental impacts, water use, and ensuring sustainable extraction,

- financial institutions, banking, and insurance, developing mathematical and statistical models for financial markets and trading

- climate modeling, climate change, greenhouse gas emission analysis, forecasts, and adaptation strategies,

- innovations and research in clean energy technologies and renewable energy, energy efficiency modeling,

- analyzing production flows in industrial enterprises and designing and evaluating new methods that will be sustainable and effective in production,

- analyzing and planning accessible and sustainable transportation systems, public and shared transportation,

- analyzing and planning methods for monitoring the impacts of sustainable development on tourism,

- a support for research projects in scientific institutions, working with large datasets, and designing experiments,

- public administration bodies, including ministries of the environment and industry and trade, non-governmental organizations, or think tanks focused on sustainability.