Data processing and analysis

The Laboratory of Statistical Research and Data Analysis offers various services in both training and consulting:

• Theoretical and practical foundations of statistics, and various data analysis methods:
  • Classical statistical methods – hypothesis testing, inferential testing, dispersion analysis (ANOVA), regression models (linear, logistic and generalized linear regression), time series analysis etc;
  • Non-parametric and robust statistical methods;
  • Multidimensional analysis methods and data mining;
  • Specialized risk model application and development (risk models, customer behavior and segmentation models etc.);
  • Supervised and unsupervised machine learning methods (cluster analysis, lasso and ridge regression, factor analysis, tree and forest algorithms, neural networks, deep machine learning etc.);
  • Research design and methodology - biostatistical methods, sample surveys, selection of methods, compilation and interpretation of results, application of statistical methods in non-standard situations;
  • Development of business intelligence tools - data processing, risk dashboards, interactive visualizations with R Shiny, Tableau and Power BI.
• R, Python, SPSS, JASP, Jamovi and other statistical programs, their practical application to real data.