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Data Science and Statistics
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Summary of the Profile
General Description:
Objective(s) of a study programme:
Prepare graduates:
• who have knowledge about statistical models used in econometric analysis, their selection and specification methods, identification algorithms and their practical application solving the macroeconomic and microeconomic problems.
• who have knowledge of mathematical statistics and computer science related to big data analysis, are familiar with modern business intelligence software tools, capable to apply statistical analysis, statistical modeling and forecasting techniques.
Learning outcomes:
Knowledge and Their Application:
- knows the relationship between data analysis and probabilistic theory and mathematical statistics, the relationship of econometrics to statistics and economic theory, linear and nonlinear regression models;
- knows the models used in the statistical analysis of data, their identification methods and algorithms, methods of econometric model selection and parameter estimation;
- knows statistical analytical systems (SAS and R), their capabilities and their use in statistical analysis of data, compilation, estimation and forecasting of econometric models;
Ability To Do Research:
- will be able to create mathematical models of problem solving of finance, economics and other fields of science and substantiate their suitability;
- will be able to create mathematical models for analysis of big data, to select parameters, to test model importance to available data, to compare few models with each other;
Special Skills:
- will be able to select appropriate model of economic objects and to identify them using modern software, to solve practical company business activity, will be able to interpret and summarize the results of research;
- will be able to program in the environment of statistical analysis systems SAS and R, to prepare the data, to record statistical models, to evaluate their parameters, to prepare research results for further analysis or publication;
Social Skills:
- will be able to clearly and reasoned present scientific research to specialists and non-specialists, to critically evaluate them, to discuss;
- will be able to work in interdisciplinary and international teams, to participate in professional networks;
Personal Skills:
- will be able to learn independently and to excel in chosen mathematics and its applications areas, to plan a learning process;
- will be able to take independent decisions, to consider their consequences and their complexity;
Activities of teaching and learning:
Lectures, practical woks, laboratory works.
Methods of assessment of learning achievements:
Exam.
Framework:
Study subjects (modules), practical training:
Methods of Data Analysis, Master's Research Work 1,2,3, Master Graduation Thesis.
Specialisations:
1. Statistical Methods in Finance and Economics:
Analysis and Forecasting of Economic Indicators, Economics, Insurance Mathematics, Mathematical Models of Financial Markets, Operations Research, Selected Chapters of Probability Theory, Software Systems for Data Analysis, Statistical Modeling and Structural Analysis, Statistical Research by Sampling Methods.
2. Data Science:
Random Graphs, Bayesian Methods, Databases 1,2, Data Science Seminar, Matrix Algebra, Optimization Problems in Statistics, Analysis of Textual Data.
Optional courses:
Basics of Mathematical Statistics, Programming with R, Queuing Theory, Risk Theory.
Distinctive features of a study programme:
Access to professional activity or further study:
Access to professional activity:
Graduates will be able to work as analysts of big data, business intelligence analysts, risk assessment specialists, project managers in various business and government companies in Lithuania and abroad.
Access to further study:
Students will be able to continue their doctoral studies in mathematics at universities in Lithuania and abroad.
