The courses of the study program Advanced Data Analytics are developed around three major pillars of modern data analytics: mathematical/statistical foundations, technological foundations, and applications. The curriculum does not include required courses, but offers a wide range of elective courses around each of the three pillars, so that students can trace their learning paths more or less as desired.
Foundations
This group of courses covers mathematical and statistical foundations of data analytics, such as calculus, linear algebra, discrete structures and the like, for students who need to improve their background in mathematics (Discrete structures, Mathematical Foundations of Data Analysis, Analytics and optimization) and probability and statistics (Introduction to statistical inference, Models of statistical learning). There is also the course Programming that covers many important concepts essential for mastering technology of data analytics. Students choose 3 out of 6 courses from this group, and have to earn the corresponding credits by passing the exam in each of the selected courses.
- Mathematical foundations of data analysis
- Discrete structures
- Analytics and оptimization
- Introduction to statistical inference
- Models of statistical learning
- Programming
Technology
Th group covers computing skills, tools and technologies for data analytics. These include database technologies and working with large volumes of data (Databases, Big data analytics), different topics in artificial intelligence necessary for advanced data analytics (Artificial intelligence / Machine learning, Neural networks and deep learning), and specific topics and tools that help data analysts in working on practical problems (Data visualization, Text mining and Social network analysis, Introduction to complex networks theory). Students choose 3 out of 9 courses from this group, and have to earn the corresponding credits by passing the exam in each of the selected courses.
- Databases
- Big data analytics
- Data visualization
- Artificial Intelligence / Machine learning
- Neural networks and deep learning
- Text mining
- Introduction to time series analysis
- Introduction to complex networks theory
Applications
This group of courses pertains to applying data analytics skills, tools and techniques in different domains. The current focus is on social and life sciences, but in the future the study program will be open for extensions by elective courses covering other domains. Students choose 2 out of 8 courses from this group, and have to earn the corresponding credits by passing the exam in each of the selected courses.
- Data analysis in fundamental and clinical medicine
- Data analysis in biological sciences
- Advanced data analysis in pharmaceutical research and development
- Practical analysis of noisy and uneven time series
- Big Data in space science and its analysis
- Social network analysis
- Advanced data analysis in social sciences
- Big Data in social sciences
- Analysis of international research datasets
The program also includes mandatory internship (capstone project / practicum) for students to get practical experience in working on data analytics projects, mandatory qualification paper (term paper), as well as mandatory master thesis.
By defending his/her master thesis, the candidate receives the academic title of Master of Data Analysis.