Statistical Data Science

This program will focus on data science from the perspective of four fundamental pillars of applied mathematics and statistics, namely: (1) statistics and statistical modeling, (2) mathematical modeling, (3) computational statistics and numerical mathematics, (4) programming (implementation of methods in R, Python, SAS, and SQL) within data science.

The program is designed for students interested in working with real-world data for mathematical and statistical modeling, addressing environmental, social, and economic aspects when solving contemporary societal challenges.

The high demand in the job market across various industries that collect and analyze data demonstrates the program's broad applicability.

Upon completing this program, students will be able to apply their knowledge in various scientific fields that involve data collection and analysis, including:

Medicine (clinical and medical research, epidemiology, medical diagnostics),

Geography, Ecology and hydrology (climate change, weather forecasting, extreme events like floods), Economics and finance (econometrics, portfolio theory, and risk management at financial markets). This new three-year Bachelor's program will build upon and innovate courses from the existing Bachelor's program in Mathematics, while also integrating specific courses from the Faculty of Informatics.

The preparation of this study program was supported by the project 0016/NPO74_PZDU_VS NPO 7.4 – Support for Green Skills and Sustainability at MU.

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

• use statistical software tools effectively, write clear and efficient code for data analysis, and follow best practices for code readability and documentation;
• describe data, build appropriate statistical models, quantify uncertainty, and recognize the limitations and assumptions;
• apply modern computational techniques to effectively solve optimization problems;
• implement and validate machine learning algorithms, neural networks, and AI models, and evaluate their performance using appropriate metrics;
• build, validate, and apply predictive models to support data-driven decision making;
• interpret and assess results of statistical procedures, assess model accuracy and performance;
• produce high-quality visualizations and reports, effectively present their findings, and make clear and concise conclusions;
• use calculus for functions, analyse limits and continuity, work with distances and measures, and apply them for solving complex problems;
• perform vector and matrix operations, understand linear transformations, solve systems of equations, understand concepts of geometry.

Graduates of the Statistical Data Science program will find extensive career opportunities in areas related to data analysis. They will be capable of advanced data processing, utilizing machine learning, predictive analysis, and algorithms, and providing insights for strategic decision-making. They can work in banks, insurance companies, or investment firms, focusing on identifying, measuring, and managing risks and developing mathematical and statistical models for financial markets and trading. In technology companies, they will contribute to projects focused on designing and implementing models for automated decision-making. They will also play a key role in business and marketing companies by evaluating marketing campaigns and their impact on company performance and tracking and predicting customer behaviour to personalize services. In healthcare, they can analyze clinical studies and epidemiological data. They will also be valuable as business consultants, helping implement data-driven strategies. Thanks to their analytical and programming skills, graduates will contribute to strategic decisions focused on sustainability, social impacts, and the economic aspects of environmental projects.

An optional part of the program is a professional internship lasting 300 hours, worth 10 credits, which occurs in the final semester of study.

Completing and defending a bachelor's thesis is a mandatory part of the Statistical Data Science study program. By working on the bachelor'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 bachelor'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.

Graduates of the Bachelor's degree program may (upon meeting the admission requirements) continue in any follow-up Master's degree program. At the Faculty of Science, they can continue in the follow-up Master's program in Statistical Data Science. Additionally, they may continue in the follow-up study program in Applied Mathematics, which offers a specialization in statistics and data analysis, among other areas.