Head:
Dr. Vytautas JANILIONIS
+370 686 61 605
vytautas.janilionis@ktu.lt


 

 

Partners:

UAB „MICROSOFT“

UAB „SWEDBANK“

UAB „Western Union“

UAB „Paspara“

Data Analytics

Today various systems and processes are generating huge amounts of data. It is very important for companies to derive value from this data and extract insights about client behaviour, resource utilisation, productivity etc. Labour market is lacking specialists who could analyse data and draw valuable conclusions which are required for successful business future. Therefore graduates with data analysis skills are in high demand. These specialists are able to develop analytical models of various business systems. By applying mathematical and statistical methods using modern analytical tools, analysts extract meaningful and useful information, which can support business decisions and allow to understand, improve, control, optimise, forecast complex business systems and processes.

Study subjects

Data Mining Methods (P160M134)
The course provides students with the knowledge of the most commonly employed g data mining methods: frequent patterns mining, associations, cluster analysis, classification methods, neural networks, outlier detection, visualization of data mining results, sampling methods. The students are taught to select appropriate data mining methods for real data analysis and understand their strengths and weakness, to build data mining models, to analyse and interpret results of data mining, to make reasoned inferences, to apply data mining methods and business analytic software (SAS, R) to real-world data analysis.
Multivariate Statistical Models (P160M135)
The course provides students with the knowledge of parametric and nonparametric multivariate statistics methods (analysis of variance, regression analysis, logistic regression analysis, general linear model, factor analysis). The students are taught to use high-performance data analysis procedures, to select appropriate data analysis methods for real data analysis and understand their strengths and weakness, to build data analysis models, to analyse and interpret results of data analysis, to make reasoned inferences, to apply data analysis methods and business analytic software (SAS, R) to real-world big data analysis.
Time Series Analysis (P160M102)
The students are learned to understand the time series analysis principles. The notion of practical applicability of linear models is matured. The students are learned to understand process prognosis principles, to understand, estimate and interpret parameters of the autoregression and moving average models.