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:

• critically analyse and synthesize advanced concepts of statistical inference, estimation and testing techniques, and apply probability theory to justify and refine methodological approaches;
• recognize common violations of classical assumptions, evaluate their impact, design and implement diagnostic tests, and develop alternative modelling strategies;
• understand the concept of flexible data modelling, adapt and extend non-linear and machine learning methods for complex data scenarios, critically assess model performance, and devise strategies to mitigate overfitting;
• understand the theoretical principles of predictive modelling and decision-making applications, develop systematic training, validation, and testing protocols, evaluate performance, uncertainty and risk, and enhance model robustness;
• characterize and represent various types of complex data, including space-time data and sequential data, develop preprocessing and cleaning techniques, and implement domain-specific statistical and machine learning models;
• discriminate between different model architectures, formulate and justify appropriate statistical techniques for diverse datasets, apply systematic approaches for model selection, and identify and interpret model components ;
• create comprehensive, high-quality visualizations and reports that adhere to principles of effectiveness , synthesize and formulate statistical findings, and create logically structured reports for technical and non-technical audiences.

Graduates of the Master's program in Statistical Data Science will possess analytical, statistical, and programming skills and a broad knowledge of modern mathematical and statistical modeling. Their significant market applicability lies in the planning, simulation, evaluating, and comparing scenarios for projects and their sustainability. Graduates' analytical skills, ability to model various scenarios, simulate their future impacts, and choose the optimal approach represent a valuable contribution to organizations in many industries.

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.

Internships are not part of this study program.

Completing and defending a master's thesis is a mandatory part of the Statistical Data Science study program. By working on the master's thesis, the student demonstrates the ability to navigate the topic of the thesis, conduct specialized work under the guidance of their supervisor, and deliver both written and oral presentations. Guidelines for the preparation of the master's thesis are specified in the Dean's Measure 3/2019: Guidelines for drawing up Bachelor's, Master's and Advanced Master's theses at the Faculty of Science of Masaryk University.

A graduate of the Master's study program may (upon meeting the admission requirements) continue their studies in the doctoral program in Mathematics and Statistics (specialization in Probability, Statistics, and Mathematical Modeling, among others) or another related doctoral program in fields such as Applied Mathematics, Statistics, and Mathematics, both at Czech and foreign universities.